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Tag: Data Analytics

  • Data Spotlight: Dividend forecasts, market signals & more | Insights | Bloomberg Professional Services

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    1. Short interest data to enhance fundamental signal

    Factor investing can be strengthened by looking beyond traditional fundamentals to gain a clearer understanding of market trends. In this analysis, we explore how factor investing can be strengthened by looking beyond traditional fundamentals to gain a clearer understanding of market behavior. To do so, we incorporate short-interest data from S3 Partners, delivered through its collaboration with Bloomberg Enterprise Data. This dataset provides point-in-time coverage back to 2015 for more than 62,000 companies globally across a range of proprietary metrics.

    Our analysis begins with a standard quality factor—consensus estimates for Return on Equity (ROE)—to identify high-quality companies. We then combine this with S3 short-interest metrics to highlight companies with more favorable market positioning. The study universe is the Bloomberg B500 Index over the past 10 years.

    Each S3 variable is transformed into a daily-change factor, with bearish movements defined according to financial intuition: negative changes for Short Interest Availability and positive changes for all other measures. These bearish signals are intersected with the sector-level bottom quintile of ROE to construct the short-leg screen, applied point-in-time to the B500 constituents. 

    The long side maintains full, unfiltered exposure to the index. A standalone ROE backtest serves as a benchmark, allowing us to assess whether short-interest dynamics offer incremental value beyond fundamentals alone.

    Chart 1 illustrates how combining Short-Interest Borrowing Cost with ROE can help uncover potential market opportunities.

    Building on the results from the Borrow Cost + ROE signal shown in the Chart 1, the second chart broadens the perspective to evaluate all seven short-interest overlays within the same framework. While the initial analysis illustrated how a single signal can influence cumulative performance over time, this cross-sectional view compares the end-of-period outcomes for each combined factor as of 31 December 2024. 

    Applying the same long-index/short-screen methodology—where a bearish short-interest move is added on top of the bottom-quantile ROE filter—reveals a consistent pattern: six of the seven short-interest dynamics generated higher cumulative returns than the ROE-only benchmark.

    Annualized Return for Multiple Short Interest Metrics

    Themes: Short Interest, Investment
    Roles: Equity Portfolio Managers, Quants, Strategists
    Bloomberg Datasets: S3 Partners Short Interest Data, Company Financials, Estimates and Pricing Point-in-Time

    2. The value of dividend forecasts in modern investment decisions

    Dividends, especially dividend forecasts, have long played a vital role in trading strategies, portfolio management and risk management practices in both sell-side and buy-side institutions. This has become even more relevant for institutional investors seeking to invest in markets that have been recently supported by dividend distribution policies, such as China. 

    A robust, forward-looking dividend forecast model with regulatory overlays to anticipate how companies are likely to adjust their dividend payouts could provide an edge in navigating today’s fast-changing financial markets and help investors make well-informed investment decisions.

    In this study, we first look at how Bloomberg’s Dividend Forecast (BDVD) data can  help with derivatives pricing. For derivatives desks, accurate dividend forecasts are essential because dividends directly affect option pricing, index futures, and structured products. Derivatives traders can adjust option fair values based on forecasted dividends rather than flat or assumed yields. 

    Chart 1 shows the index points difference for various A share and H share indices, representing the difference between projected and realized dividend. Our results show that Bloomberg dividend forecast could maintain an accuracy within roughly 10 basis points of the index level for a variety of China Indices.

    The value of dividend forecasts in modern investment decisions

    For Buy-Side investors, Bloomberg’s Dividend Forecast (BDVD) data can provide additional insight to support dividend-focused strategies. In our analysis of a monthly-rebalance backtest from 2020 to 2025, companies in the top quintile of forecasted three year dividend growth exhibited higher cumulative returns than those ranked by three-year actual dividend growth.

    This suggests that the market may place greater emphasis on the potential for dividend growth than on realized dividend payouts. Bloomberg’s dividend forecast data can offer portfolio managers deeper visibility into expected dividend trends, supporting more informed portfolio management decisions.

    Cumulative Return Analysis: Top Quintiles from Dividend Forecast Growth and Actual Dividend Growth

    Themes: Quantitative Trading, Alpha Generation
    Roles: Portfolio Managers, Quantitative Researchers, Derivatives Traders
    Bloomberg Datasets: Dividend Forecast

    Intraday liquidity for accurate backtests

    When performing backtests, quantitative researchers often treat the closing auction price as an executable level. This approach implicitly assumes that sufficient liquidity is available in the auction to support execution at that price. There is extensive evidence that market impact cannot be ignored, as it varies significantly with trade size, market capitalization, and time of day, for example, see Direct Estimation of Equity Market Impact (Almgren et al). In practice, trading with minimal market impact during the closing auction typically requires that the order size represent less than 5% of the auction’s total volume.

    As illustrated in Chart 1, smaller-cap companies may exhibit closing auction volumes that are insufficient to support execution without market impact. More realistic backtesting results may be achieved by incorporating pricing from different intraday windows, particularly those characterized by higher volume and lower volatility. For this purpose, we consider the volume-weighted average price (VWAP) observed during each selected session.

    Company Market Cap vs. Percentage of Closing Volume (log scale)

    To support more effective execution, traders should take into account the available trading volume throughout the day, as this can also contribute to more realistic simulation results, especially in the context of trading smaller capitalizations. Chart 2 shows the distribution of available trading volume (right axis) across market-cap segments, alongside the realized 5-minute volatility (left axis) for each session. We observe that trading volume tends to be higher in the afternoon, while volatility is typically elevated in the morning around the market open. These patterns suggest that execution may be more favorable during the afternoon sessions.

    This type of analysis enables tick-data users to conduct more granular assessments and can help refine the implementation aspects of their trading strategies, thereby supporting an improved risk-return balance.

    5-min Bar Volatility vs. Percentage of Total Volume

    Themes: Transaction Costs, Liquidity
    Roles: Equity Portfolio Managers, Quantitative Researchers, Traders
    Bloomberg Datasets: Tick History

    How can we help?

    Bloomberg’s Enterprise Investment Research Data product suite provides end-to-end solutions to power research workflows. Solutions include Company Financials, Estimates, Pricing and Point in Time Data, Operating Segment Fundamentals Data and Industry Specific Company KPIs and Estimates Data products, covering a broad universe of companies and providing deep actionable insights. This product suite also includes Quant Pricing with cross-asset Tick History and Bars. Additional solutions such as Geographic Segment Fundamentals Data, Company Segments and Deep Estimates Data and Pharma Products & Brands Data products will be available in 2025. All of these data solutions are interoperable and can be seamlessly connected with other datasets, including alternative data, and are available through a number of delivery mechanisms, including in the Cloud and via API. More information on these solutions can be found here.

    Bloomberg Data License provides billions of data points daily spanning Reference, ESG, Pricing, Risk, Regulation, Fundamentals, Estimates, Historical data and more to help you streamline operations and discover new investment opportunities. Data License content aligns with the data on the Bloomberg Terminal to support investment workflows consistently and at scale across your enterprise.

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    Bloomberg

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  • How companies integrate private market data at scale? | Insights | Bloomberg Professional Services

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    What’s driving the push to integrate private market data with public market workflows? How are investors and asset managers addressing persistent data frictions to create a unified view across portfolios? 

    This episode of Market Dialogues features Leila Sadiq, Global Head of Enterprise Data Content at Bloomberg, in discussion with Todd Hirsch, Head of Private Capital at Point72, Mark Neely, Director of Alternative Investments at GenTrust, and Avi Turetsky, Partner and Head of the Quantitative Research Group at Ares, on how private markets are evolving toward greater transparency, valuation consistency, and data connectivity. 

    Source: Enterprise Data & Tech Summit, October 16, 2025, New York

    The Market Dialogues podcast series provides access to curated, thought-provoking discussions from Bloomberg global events. It offers in-depth insights from experts on key trends and themes driving the markets today and beyond. 

    Discover more conversations in the Market Dialogues series here. 

    Featured insights from this episode of Market Dialogues: 

    On technology changing valuation transparency

    Todd Hirsch: Technology has enabled us to have much greater frequency of inputs for valuations. Whether they’re public or private… I think you can have a better sense of how often you want to mark your positions and how frequently you need to adjust those marks based on the inputs you have.

    When new information comes in, it’s important that it gets incorporated. So if a building sells down the street and you now know there’s a new comparable, you can incorporate that in real time. The frequency of adjustments on the private side today is much better than it has ever been, and transparency for investors continues to improve as that frequency increases, and the supporting data and analytics become stronger.* 

    On the data gap between public and private companies

    Avi Turetsky: We know that public markets show meaningfully higher vol[atility] than private market NAVs. We know the valuations are different: public market valuations represent marginal trades, while private market valuations are appraisal-based. We have a pretty good sense of the relationship between the two.  

    But whether privately owned companies are actually more stable than publicly traded companies, if you’re looking at revenue, EBITDA, or cash flows, as far as I know, no one knows the answer to that question…The prices are more stable… But whether the revenues are more stable to EBTIDA, no one knows because no one’s been able to get the data on large enough scale to my best knowledge. 

    On managing allocation across private and public assets 

    Mark Neely: I focus a lot of my time on the allocation model for clients, and it’s always challenging because we’re constantly comparing public and private markets and they don’t really compare. You look at private equity over a trailing two-year return, and it doesn’t track public equities over the last 10 years  

    Clients will say, “The public markets are really strong, I want to go into private,” and I’ll say, “Okay, but let’s look at what the EBITDA multiples are that private markets are purchasing at. What are things being marked at?” That transparency, or lack of it, makes it very challenging to take advantage of dislocations or to reallocate capital between private and public markets, or up and down the capital stack in direct lending, Broadly Syndicated Loans (BSLs), and term loans.” 

    *Quotations have been edited for brevity and clarity.

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    Bloomberg

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  • Global insight: AI’s three revolutions for macro forecasting | Insights | Bloomberg Professional Services

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    Filling data gaps

    Economists have long mined text for research and forecasting. Early applications, from economic uncertainty (Baker et al. 2016) to central bank sentiment (Bulir et al. 2012), mainly relied on keyword counting.

    Today, natural language processing and large language models unlock far richer signals. Sentiment scores of commentary in purchasing manager indexes can improve GDP nowcasts in combination with traditional data (de Bondt and Sun 2025). Topic modeling of news helps forecast aggregate stock market returns (Bybee et al. 2023). And language models turn headlines into forward-looking indicators of central bank policy — as with Bloomberg Economics’ indexes for the Federal Reserve, European Central Bank and Bank of England. These are available to Bloomberg Terminal subscribers via BECO MODELS CBSPEAK .

    Satellite imagery is another important data source. Computer vision models can leverage interpretable features, such as roads, parking lots and crop fields, to provide information that official statistics either miss or capture too slowly. For example, we’ve built a monthly global GDP tracker using night-light data. Other researchers have found that retail parking occupancy measured from space can offer investors an edge (Katona et al. 2018), while snapshots of port-occupancy can help nowcast GDP and trade (Spelta et al. 2025).

    Supercharged workflows

    AI is changing how economists work as much as what they analyze.

    • Automation of routine tasks: Data cleaning, classification and feature engineering are increasingly handled by AI, freeing researchers to focus on interpretation and strategy.
    • Faster iteration: LLMs can increasingly act as research assistants — summarizing literature, coding econometric routines or stress-testing assumptions. Incorporating generative AI can cut project timelines from weeks to days (Korinek 2025).
    • Collaborative tools: AI-driven platforms integrate data, models and visualization, creating more transparent and reproducible research pipelines.

    At Bloomberg Economics, we’re working faster across a broader suite of models, and spending more time contextualizing the results in our research.

    Beyond linear models

    AI is improving the predictive power of econometric models, enabling policymakers to be more proactive in monitoring financial risks. From classical machine learning to transformer-based foundation models, new methods capture signals — including nonlinear relationships — that can provide early warning systems for stress events.

    • Classical ML methods like Lasso, Ridge, and Elastic Net handle large datasets effectively, while tree-based ensembles such as Random Forest and Gradient Boosting capture nonlinearities that emerge during periods of high macroeconomic uncertainty (Aldasoro et al. 2025). Newer variants, such as the macro random forest, combine ML flexibility with the structure of economic models, outperforming traditional econometric techniques even when even when the time series are short (Chinn et al. 2023).
    • Deep learning models (neural networks) extend these gains to complex, high-dimensional tasks, such as predicting FX dislocations (Aquilina et al. 2025). These models excel at identifying nonlinear patterns and rare events, though they require substantial tuning (Athey and Imbens 2019).
    • Text-based large language models and NLP systems extract sentiment and information from unstructured sources like news, policy statements and corporate filings. This can boost forecast accuracy when combined with numerical data (de Bondt and Sun 2025). Similarly, time-series foundation models — such as TimeGPT (Nixtla), Moirai (Salesforce), and TimesFM (Google) — bring transformer architectures to economic forecasting. While they don’t yet outperform econometric mainstays like Bayesian VARs, combining their predictive flexibility with structural model discipline yields accuracy gains across variables and horizons (Carriero et al. 2025).
    • Although some studies have explored using LLMs directly as forecasters, their lack of a true “point-in-time” notion makes them unreliable for out-of-sample evaluation (Lopez-Lira et al. 2025). The frontier instead lies in hybrid approaches — blending the interpretability of structural economics with AI’s adaptive, data-driven strengths.

    Central banks and international institutions are increasingly deploying these same AI models to anticipate financial stress. The ECB’s Cassandra system, for example, fine-tunes language models to analyze financial news sentiment and then applies boosting and neural network methods to flag early warning signals for banks (Petropoulos et al. 2025).

    Similarly, BIS research (Aquilina et al. 2025) combines recurrent neural networks with LLMs to forecast and interpret episodes of FX market stress. The BIS system combines quantitative assessments with qualitative interpretation of financial news to predict stress events up to two months ahead.

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    Bloomberg

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  • Risk Budgeting for Chinese Equities: Exception Proves the Rule | Insights | Bloomberg Professional Services

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    Strategy Exploits Low Correlation, Volatility Difference

    Chinese equity sectors are far from perfectly correlated, and their volatility profiles vary widely — conditions that favor a risk-budgeting approach. Over the full sample, the average pairwise monthly correlation between sectors is about 67%, allowing diversification to reduce overall portfolio variance. Historical sector volatilities range from roughly 24% to 34% annualized. Utilities and consumer discretionary have been the least volatile, while telecoms and IT have been the most volatile. The ERC approach systematically adjusts exposures to these differences, increasing allocations to low-volatility, low-correlation sectors and scaling down those with higher and more correlated risk. This results in a more balanced and resilient portfolio.

    Sector Correlations

    Risk Budgeting Shifts Sector Weights in China Equities

    Sector allocation in risk-budgeting strategies can be markedly different from weights in the cap-weighted benchmark. Financials, for example, have the highest weight (27%) in the CSI 300, but ERC reduces this to around 11% so that its risk contribution is equal to that of the other nine sectors. As of June 2025, utilities received the highest weight (16%), while consumer staples had the lowest (7%) in the ERC portfolio.

    Sector Allocation: Benchmark vs. Risk Budgeting

    Customizing Risk Budgeting Reduces Tracking Error

    Large deviations from benchmark weights can result in tracking errors that some portfolio managers prefer to limit. The unconstrained ERC strategy has an annualized tracking error of about 5.6%. A constrained version — capping sector deviations at 5% for small sectors and 10% for large sectors — reduces tracking error to 2.8% while preserving much of the downside protection and maintaining superior risk-adjusted returns relative to the cap-weighted benchmark. This allows risk-budgeting to serve as a benchmark alternative with greater proximity to index weights.

    Performance: Benchmark, ERC, Constrained ERC

    ERC Strategies Show Modest Turnover

    ERC portfolios exhibit relatively low turnover given the stability of sector allocations through time. For the CSI 300 cap-weighted benchmark, average quarterly turnover is 1.7%. For ERC, turnover averages 4.4% for the constrained version and 3.1% for the unconstrained version. The limited trading activity reflects adjustments driven by changes in sector volatility and correlation rather than frequent tactical shifts, keeping implementation costs contained.

    Quarterly Turnover

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    Bloomberg

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  • How to Reduce Chronic Absenteeism in Schools

    How to Reduce Chronic Absenteeism in Schools

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    Since the Covid-19 pandemic, the rate of students who are chronically absent—defined as missing just under one month of class—has doubled to 26 percent nationally, reaching crisis levels and threatening the educational foundation of our nation’s youth. Chronic absenteeism is estimated to be responsible for up to 27 percent of the overall decline in math test scores and a shocking 45 percent of the drop in reading scores between 2019 and 2022.

    To combat this problem, the company Edia recently unveiled an AI-powered platform aimed at school districts across America. Within minutes of an absence, Edia initiates personalized AI-driven conversations with families in more than 100 languages, enabling school districts to identify and tackle root causes of chronic absenteeism.

    “Today, nearly three-quarters of absences are unexplained, meaning no one called in ahead of time and districts don’t know where those children are,” said Joe Philleo, CEO of Edia. “With so many students missing school, staff don’t have the capacity to reach out to every single family and understand what is happening with their child.”

    “Every situation is different,” Philleo continues. “When staff don’t know the reason students are missing school, they can’t fix the root cause. One student may miss school because they don’t have reliable transportation, and another student may skip Math and English in the morning and just attend Computer and Welding at the end of the day because they find those classes more engaging.”

    By leveraging AI, Edia enables schools to identify and solve the root causes of chronic absenteeism. Its system ensures no absence goes unnoticed, helping to restore accountability, rebuild connections between schools and families, and resolve underlying challenges that keep students from attending class.

    Key features of the Edia AI platform include:

    1. AI Conversations within minutes of Absence: Personalized text message conversations in 100+ languages sent to parents within minutes of an absence, reducing unexplained absences by up to 80 percent.
    2. Analysis to understand why students are missing class: Texts, calls, and notes come together in a single profile to identify why students are missing school and enable teams to take the right set of action.
    3. Purpose-built workflows for MTSS interventions: Ability to launch, track, and coordinate personalized intervention plans for students at risk.

    Edia’s new solution is currently being used in K-12 school districts nationwide, including Raton Public Schools, Farmington Municipal Schools, and Hobbs Municipal Schools.

    “Chronic absenteeism is a significant issue in education and in the Raton Public Schools that can severely impact student achievement and the long-term success of a student,” Kristie Medina, Superintendent at Raton Public Schools. “It refers to students missing a substantial number of school days, typically defined as 10 percent or more of the school year, for any reason, whether excused or unexcused. The challenge of chronic absenteeism lies in its widespread impact, affecting not just individual students but the entire school community. Our district is committed to addressing chronic absenteeism because it is critical to ensuring every student has the opportunity to succeed and thrive in both school and life.

    Medina continued, “I’m genuinely excited for Raton Public Schools to implement Edia’s AI Attendance Solution! The integration of AI into tracking and improving attendance will be a game-changer, especially when tackling chronic absenteeism. By leveraging AI, the district can gain deeper insights into attendance patterns, identify at-risk students earlier, and tailor interventions more effectively.”

    Kevin Hogan
    Latest posts by Kevin Hogan (see all)

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  • Capri Global Capital plans to develop insurance platform 

    Capri Global Capital plans to develop insurance platform 

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    Capri Global Capital Ltd (CGCL) plans to revolutionise delivery of insurance products and services with the use of data analytics, artificial intelligence, and blockchain in insurance solutions. The company has received a composite corporate agency licence from the Insurance Regulatory and Development Authority of India (IRDAI) in December 2023.

    It plans to automate claims processing and customer support services by reducing cost of operations.

    CGCL said in a press statement the platform will adopt a customer-friendly payment policy, including digital wallets, credit cards, net banking, and debit cards.

    The stock traded at ₹901.40 on the NSE, up by 10.85 per cent as of 9:16 am on Tuesday.

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  • This Is the Top Budget Priority for Marketers Today | Entrepreneur

    This Is the Top Budget Priority for Marketers Today | Entrepreneur

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    Opinions expressed by Entrepreneur contributors are their own.

    Recession or no recession, marketing budgets are getting smaller. Marketers are being asked to do more with less.

    The upside of this is that it provides clarity to the priorities marketers need to make with the budget they have. Time and money work the same way in that regard. Given an unlimited number of hours in a day, you’d accomplish everything. But that’s not how life (or budgets) work.

    Over several informal conversations with marketing leaders at over 20 companies across a range of industries, we asked what struggles, pain points and wish lists dominated their day and influenced their spending decisions.

    Specifically, we asked what their priorities were when making budget allocation decisions.

    One clear desire rose above the rest — Reporting and Analytics. If they were given free money to spend on anything they liked, increasing their reporting and analytics capabilities regularly came out on top among the wish lists.

    Related: How to Grow Your Business With Marketing Analytics: The Ultimate Guide

    The marketer’s wish list

    Here are the top 10 results, in order of importance, of how these marketing leaders told us they would stack rank their priorities against their budget. This was not a list of pre-set options, but rather what they volunteered themselves that simply laddered up into the categories below.

    Take a look and see if your priorities match:

    1. Reporting / Analytics

    2. Machine Learning / Artificial Intelligence

    3. Stability

    4. Audience Growth

    5. Customer Journey

    6. UI and operational efficiency

    7. Privacy and Trust

    8. Loyalty

    9. Content

    10. Simplify Stack

    Why Reporting / Analytics?

    The first question, of course, is why? What makes Reporting and Analytics so important that it so far outpaces the other items on this potential wishlist?

    For starters — ROI. Marketing departments have to constantly justify every action and every dollar through the results they achieve. Marketers (and those they report to) need to see that their efforts are performing as expected in terms of direct attribution (read: revenue) across all channels — email, mobile, and so on.

    That leads us to autonomy. Marketing teams would prefer to analyze the results of their campaigns themselves directly from the platform they use, rather than rely on a separate IT or tech department to pull data for them.

    Not only is this more efficient from a time/resources perspective (eliminating the back-and-forth request/response/request/loop), but it also makes the insights gained more actionable within the marketing team and the campaigns they manage.

    Automation is another one. Marketing teams are trending smaller as budget is pulled away into building IT and tech-focused groups like marketing automation. So, marketers say they’re spending too much time on data creation and the manual tasks behind that effort, and would prefer platforms with built-in automation wherever possible to help them.

    This includes connecting data and analysis functions directly with the CRM platform they use, as well as proactive predictive customization to automatically implement campaign changes based on pre-set parameters.

    And finally, monitoring is a big part of the data/analysis equation. The ability to monitor incoming data and make rapid changes as needed is a logical place to invest data and analytics dollars. This includes robust A/B testing capabilities with the ability to rapidly and dynamically modify tests on the fly, as well as the ability to monitor the entire customer journey.

    Preferably, this monitoring can take place through a single dashboard that compiles all datasets from across the platform (or integrates data from multiple platforms) to reduce the number of multiple screens or handoffs necessary with most systems today.

    Related: 5 Analytics Tools to Supercharge Your Marketing Strategy

    What to report/analyze?

    The ability to report and analyze data is one thing. Knowing what data to focus on is another.

    Revenue was a common data point the marketers we spoke with wanted to measure. Partnering with a technology company that can track web behavior and tie it back to channel performance is a key data point. What marketing emails, ads and other tactics are driving the most revenue, and why? If something outperformed historical trends, what was the differentiating factor? Could a change in one channel drive a shift in channel share?

    Engagement stats like clickthrough is another important metric to monitor because engagement often leads to revenue. Conversions are important.

    And finally, ensuring that interested customers are being serviced properly through digital channels to avoid involving a human intervention, which can tie up resources and ultimately slow down a conversion. Imagine receiving a push notification with a coupon code but then not being able to redeem that code upon checkout.

    The “human” cost associated with any digital channel snafu can be expensive in the form of customer service representatives ultimately needing to complete the transaction. Keeping the activity online and completing sales in a single session is the mark of a well-functioning marketing campaign that drives both engagement and revenue.

    Ultimately, the number one goal is to avoid sparking a phone call to customer support. A phone operator can only assist one person at a time, while a website can serve thousands.

    On paper, good data reporting and analysis seem obvious. Time and time again, good data and analysis result in improved ROI. But in the reality of the fast-paced marketing world, carving out the time needed to both collect and analyze data can be difficult when doing so remains a manual process.

    That’s why companies should seek out and demand automated reporting and analytics features from their marketing platform providers. Revenue modeling and channel attribution are too important to be left to chance. Working with a platform that can easily automate this kind of performance reporting, and then using AI to detect the small shifts in these results, gives marketers the insights they need to optimize their efforts in real time.

    In other words, the same tools that marketers use to automate marketing outreach should make collecting and extrapolating data just as easy and automatic. This allows marketers to spend more time making the data more actionable for more personalized communications — and ultimately, more meaningful relationships.

    Related: 10 Tools Helping Companies Manage Big Marketing Data

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  • Podcast: Envestnet President Farouk Ferchichi on hyper-personalization | Bank Automation News

    Podcast: Envestnet President Farouk Ferchichi on hyper-personalization | Bank Automation News

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    Financial institutions can look to data to create hyper-personalized experiences within back- and front-end operations — if they prioritize data and analytic literacy throughout their institutions.

    In creating a hyper-personalized experience, banks must lay a “foundation of culture change around data, machine learning, artificial intelligence and machine learning literacy,” Farouk Ferchichi, president of data and analytics provider Envestnet, tells Bank Automation News in this episode of “The Buzz” podcast.

    Through data and analytics, machine learning and AI, banks can benefit from improved risk management for decisioning, fraud detection and anti-money laundering capabilities, he said. Additionally, clients benefit from a personalized experience based on their needs.

    Listen as Envestnet’s Ferchichi discusses how financial institutions can harness data to create efficiencies in front- and back-end operations.

    The following is a transcript generated by AI technology that has been lightly edited but still contains errors.

    Whitney McDonald 0:09
    Hello, and welcome to the buzz of bank automation news podcast. My name is Whitney McDonald and I’m the editor of bank automation news. Joining me today is president of investment Farukh for Chi Chi. He’s here to discuss the importance of harnessing the power of data through technology for added efficiencies and better understanding of the target audience.Farouk Ferchichi 0:29
    Yeah, first of all, hi, Whitney. Very good to see you. Again. For the listening audience, my name is food for cheeky and I am the president of investment data analytics line of business, also known to many of your listeners as Yodlee. Or like we’d like to joke internally and say, it’s usually to point out, and we serve globally, the banking tech and wealth industry with an alternative data and AI powered bank as a service platform that brings together candidate data connectivity, that data intelligence, and hyper personalized digital money management experiences in one integrated ecosystem.Whitney McDonald 1:15
    Now, I know that investment has been busy, definitely for the past six months or so can you talk through some of the latest upgrades and newest offerings that investment has been working on?

    Farouk Ferchichi 1:29
    Yeah, I mean, investment generally has had a lot of new things going on. And particularly here in the investment DNA line of business, a lot has happened over the past 18 months. For example, in wealth management. We we launched our wealth data platform, or as our clients know it as w DP. And the focus there has been on driving and measuring growth for our clients and their end clients that are investors. In the banking, retail banking space, we have a lot going on, we moved from a pure aggregation to a leading open banking and alternative data value providers. We invested more in the AI and machine learning and data and AI governance, in addition to kind of grow in our open banking footprint here in North America and abroad. And as a result, we were able to launch kind of a new alternative data solutions. We were actually our alternative credit, credit data solutions, our small business solution, and continue to kind of improve our customer facing digital experiences, taking kind of PFM, or the personal financial management experiences to the next level growing from what is used to be just a money discovery tool, to more of a planning and execution of your money management experiences, like tokenization, for verification and identity check, goal setting savings, and subscription management to name few, of course, all of powered by our unique set of alternative data, database, as well as the analytical capability we have behind.

    Whitney McDonald 3:14
    Now with these recent launches in mind and new products in mind. And of course, being in the business of data. I’d love to start things off by talking about really just the importance of harnessing data and analytics for financial institutions. Can you talk us through that?

    Farouk Ferchichi 3:29
    Yes, Whitney. When you think about this, going to generally speaking about the socio political and economic challenges that are facing us in the world. Financial institutions are obviously not immune, and are seeking a stable business that can overcome these headwinds, and the way they do that is balancing the risk management side of the business and the growth side of the business. And more importantly, in these days with a finite number of resources available to them. So as such, we see the the weight and the importance put into harnessing the power of data is essential. It is a great tool, especially these days to enable automation and productivity on one hand, enabling faster and cheaper development and augmentation of risk management processes, while enabling at the same time, deeper sales and product and marketing, segmentation. Enabling them truly to differentiate product offering with a higher degree of targeting.

    Whitney McDonald 4:53
    Now getting into the how behind that, really, how can FIS approach this stuff? Energy of harnessing data, and maybe you can talk through where the technology element comes in. Yeah,

    Farouk Ferchichi 5:07
    as we listen as we constantly are listening and talking to our clients and at the same time finding ways to respond and serve their needs, we see data, AI, and technology harness in delivering, particularly the hyper personalized services to the employees in the back office, to do their job better and of course, the front office to their clients to achieve their financial needs. Focusing on the employee and the back office, we see it in risk management improvements of existing like credit risk management processes for decisioning. Around 40, decision a credit decisioning, loss forecasting or even collection, as well as in the operation risk management processes side automation, we spend improvement and augmentation, we see it in that including like fraud detection, security monitoring, as well as augmenting anti money laundering capabilities. We see also an emerging an emergence at scale of deploying data and AI in the product planning aspect, understanding the lifetime needs of existing clients and build that personalized roadmap of what and when a given a product can be offered at what price to a given customer. We also see marketing segments become segmentation becoming more refined, allowing the organization frankly to meet the needs of their clients in a more hyper personalized way. And again, hyper personalized not to fall but at the right time, using the right omni channel that is preferred by the clients. But But honestly, Whitney for this data, AI and analytics harnessing to be deployed effectively. We see companies who are the most effective at this have laid the foundation of a cultural change around quote unquote, data and artificial and machine learning artificial intelligence and machine learning literacy. The second area where we see is laying the foundation of data governance as well as model governance processes, and then data and AI infrastructure, preferably in the cloud. When you have these type of technical prerequisites, I like to say, they will enable a faster and more effective and efficient deployment of the data AI and technology combined. Obviously, we preach this to our clients all the time, different clients and advisors at different stages of their maturities. But all three areas are our areas we are actively consulting at no additional cost to our clients because for them to take the to get the most return that to achieve the most return from our products and services. We work with them in laying that prerequisite foundation.

    Whitney McDonald 8:43
    Now speaking of that foundation, and I know you touched a little bit on some of the areas where you can see the benefits coming through the back end, the front end, maybe we could dive a little bit deeper into some of those benefits that a financial institution might see from leveraging their data and analytics.

    Farouk Ferchichi 9:02
    Yeah, absolutely. We do. We do believe the benefit to end consumers or clients is access to the promise of open finance powered by open banking. And that promise needs to be featured with this hyper personalized product options that they have access to that they don’t today at a competitive price at the right time. On the flip side, for the financial institution, the benefits are to grow and be more productive. And when I say grow, I mean via higher client retention, and more holistic kind of lifetime relationship and value from from the customers they managed today. Above and beyond. They’re onboarding new clients and prospects. And then when I say productivity, I mean the ability to scale and differentiate back office processes around product management, servicing and marketing plans and strategies at a lower cost.

    Whitney McDonald 10:07
    Now wondering if you can discuss or give an example of a bank or client that’s doing this? Well, what data has brought to a certain financial institution or client? May we talk through what some of those time savings, or monetary savings might look like?

    Farouk Ferchichi 10:29
    Yeah, absolutely. This is one of my favorite topics with me because, well, while whether internally within our organization, or more importantly, with our clients, we like to talk a lot about value captured. Because we as a business to business to the end client kind of provider, we want our, we want to make sure that our products and services are adding measurable value. And without naming names. As you know, many of our clients are using our open banking and value add data, AI and digital technology services. And I want to share with you a couple, a couple of examples, one of our one from one of our large ePHI clients, where the customer retention across multiple product line and segments has improved incrementally because customer considering another firm, maintain their accounts and respective fee revenue. For the composite organization or this organization, I’m talking about the total risk adjusted operating profit increased due to this improved client retention, believe it or not by 24 million over a three year period of time. And then another client of ours who’s a little bit smaller mid size, regional FYI client, increase their wallet chair. And that’s due to more efficient reliable aggregation of financial data of their customer and supporting behind the scenes, the intelligence and the analytical services that we provide customers account managers get increased visibility into the assets, they do not actively managed with their client, which allow them to put the programs together to compare services of external assets and design internally products and solution to bring those assets in house leading to fundamentally an increase in revenue to the new due to the new asset and their management, the composite three year risk adjusted, which is the value metric that we use with our clients and confidence, profit increase for this FY with the effect of this wallet share program to a total of $15 million.

    Whitney McDonald 12:58
    Yeah, when you put it into those quantifiable measures, and I know that you said of course there’s the value capture and value add it really the the times the money savings, the time savings at all, it all adds up. And that’s exactly what you guys are working toward anything that we didn’t hit on that you wanted to be sure to. Yeah.

    Farouk Ferchichi 13:25
    If I may, I know everyone speaks about Chad GPT, and AI and generative AI and all of that. And a couple things I’d like to share are three things one, it is reality, you cannot run from it, it is coming. We invest in it in general and DNA. In particular data analytics line of business in particular, we’ve been using generative AI for years right now. It is our core IP behind the scenes, we just didn’t advertise it because it was not something that people talk about. It’s too technical. But we do now, the second thing I would say the best application that we see and we invest in it of how to implement charge GPT it is going to be on the back office to gain back credibility with the employees with the organization. It will be focused on automation creating content at scale, and so on. And then finally, I would say for charge GPT to be accepted and rollout at scale that has to be a deliberate effort around AI literacy as well as AI governance and openly discussing the AI ethics and The Good, the Bad and the audio that comes with it.

    Whitney McDonald 14:52
    You’ve been listening to the buzz a bank automation news podcast please follow us on LinkedIn and as a reminder, you can rate this podcast on Your platform of choice thank you for your time and be sure to visit us at Bank automation news.com For more automation news

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    Whitney McDonald

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  • Envestnet Group President on Hyper-personalization | Bank Automation News

    Envestnet Group President on Hyper-personalization | Bank Automation News

    [ad_1]

    Financial institutions can look to data to create hyper-personalized experiences within back- and front-end operations — if they prioritize data and analytic literacy throughout their institutions.

    In creating a hyper-personalized experience, banks must lay a “foundation of culture change around data, machine learning, artificial intelligence and machine learning literacy,” Farouk Ferchichi, group president of wealthtech giant Envestnet Data & Analytics, tells Bank Automation News in this episode of “The Buzz” podcast.

    Through data and analytics, machine learning and AI, banks can benefit from improved risk management for decisioning, fraud detection and anti-money laundering capabilities, he said. Additionally, clients benefit from a personalized experience based on their needs.

    Listen as Envestnet’s Ferchichi discusses how financial institutions can harness data to create efficiencies in front- and back-end operations.

    The following is a transcript generated by AI technology that has been lightly edited but still contains errors.

    Whitney McDonald 0:09
    Hello, and welcome to the buzz of bank automation news podcast. My name is Whitney McDonald and I’m the editor of bank automation news. Joining me today is president of investment Farukh for Chi Chi. He’s here to discuss the importance of harnessing the power of data through technology for added efficiencies and better understanding of the target audience.Farouk Ferchichi 0:29
    Yeah, first of all, hi, Whitney. Very good to see you. Again. For the listening audience, my name is food for cheeky and I am the president of investment data analytics line of business, also known to many of your listeners as Yodlee. Or like we’d like to joke internally and say, it’s usually to point out, and we serve globally, the banking tech and wealth industry with an alternative data and AI powered bank as a service platform that brings together candidate data connectivity, that data intelligence, and hyper personalized digital money management experiences in one integrated ecosystem.Whitney McDonald 1:15
    Now, I know that investment has been busy, definitely for the past six months or so can you talk through some of the latest upgrades and newest offerings that investment has been working on?Farouk Ferchichi 1:29
    Yeah, I mean, investment generally has had a lot of new things going on. And particularly here in the investment DNA line of business, a lot has happened over the past 18 months. For example, in wealth management. We we launched our wealth data platform, or as our clients know it as w DP. And the focus there has been on driving and measuring growth for our clients and their end clients that are investors. In the banking, retail banking space, we have a lot going on, we moved from a pure aggregation to a leading open banking and alternative data value providers. We invested more in the AI and machine learning and data and AI governance, in addition to kind of grow in our open banking footprint here in North America and abroad. And as a result, we were able to launch kind of a new alternative data solutions. We were actually our alternative credit, credit data solutions, our small business solution, and continue to kind of improve our customer facing digital experiences, taking kind of PFM, or the personal financial management experiences to the next level growing from what is used to be just a money discovery tool, to more of a planning and execution of your money management experiences, like tokenization, for verification and identity check, goal setting savings, and subscription management to name few, of course, all of powered by our unique set of alternative data, database, as well as the analytical capability we have behind.Whitney McDonald 3:14
    Now with these recent launches in mind and new products in mind. And of course, being in the business of data. I’d love to start things off by talking about really just the importance of harnessing data and analytics for financial institutions. Can you talk us through that?Farouk Ferchichi 3:29
    Yes, Whitney. When you think about this, going to generally speaking about the socio political and economic challenges that are facing us in the world. Financial institutions are obviously not immune, and are seeking a stable business that can overcome these headwinds, and the way they do that is balancing the risk management side of the business and the growth side of the business. And more importantly, in these days with a finite number of resources available to them. So as such, we see the the weight and the importance put into harnessing the power of data is essential. It is a great tool, especially these days to enable automation and productivity on one hand, enabling faster and cheaper development and augmentation of risk management processes, while enabling at the same time, deeper sales and product and marketing, segmentation. Enabling them truly to differentiate product offering with a higher degree of targeting.

    Whitney McDonald 4:53
    Now getting into the how behind that, really, how can FIS approach this stuff? Energy of harnessing data, and maybe you can talk through where the technology element comes in. Yeah,

    Farouk Ferchichi 5:07
    as we listen as we constantly are listening and talking to our clients and at the same time finding ways to respond and serve their needs, we see data, AI, and technology harness in delivering, particularly the hyper personalized services to the employees in the back office, to do their job better and of course, the front office to their clients to achieve their financial needs. Focusing on the employee and the back office, we see it in risk management improvements of existing like credit risk management processes for decisioning. Around 40, decision a credit decisioning, loss forecasting or even collection, as well as in the operation risk management processes side automation, we spend improvement and augmentation, we see it in that including like fraud detection, security monitoring, as well as augmenting anti money laundering capabilities. We see also an emerging an emergence at scale of deploying data and AI in the product planning aspect, understanding the lifetime needs of existing clients and build that personalized roadmap of what and when a given a product can be offered at what price to a given customer. We also see marketing segments become segmentation becoming more refined, allowing the organization frankly to meet the needs of their clients in a more hyper personalized way. And again, hyper personalized not to fall but at the right time, using the right omni channel that is preferred by the clients. But But honestly, Whitney for this data, AI and analytics harnessing to be deployed effectively. We see companies who are the most effective at this have laid the foundation of a cultural change around quote unquote, data and artificial and machine learning artificial intelligence and machine learning literacy. The second area where we see is laying the foundation of data governance as well as model governance processes, and then data and AI infrastructure, preferably in the cloud. When you have these type of technical prerequisites, I like to say, they will enable a faster and more effective and efficient deployment of the data AI and technology combined. Obviously, we preach this to our clients all the time, different clients and advisors at different stages of their maturities. But all three areas are our areas we are actively consulting at no additional cost to our clients because for them to take the to get the most return that to achieve the most return from our products and services. We work with them in laying that prerequisite foundation.

    Whitney McDonald 8:43
    Now speaking of that foundation, and I know you touched a little bit on some of the areas where you can see the benefits coming through the back end, the front end, maybe we could dive a little bit deeper into some of those benefits that a financial institution might see from leveraging their data and analytics.

    Farouk Ferchichi 9:02
    Yeah, absolutely. We do. We do believe the benefit to end consumers or clients is access to the promise of open finance powered by open banking. And that promise needs to be featured with this hyper personalized product options that they have access to that they don’t today at a competitive price at the right time. On the flip side, for the financial institution, the benefits are to grow and be more productive. And when I say grow, I mean via higher client retention, and more holistic kind of lifetime relationship and value from from the customers they managed today. Above and beyond. They’re onboarding new clients and prospects. And then when I say productivity, I mean the ability to scale and differentiate back office processes around product management, servicing and marketing plans and strategies at a lower cost.

    Whitney McDonald 10:07
    Now wondering if you can discuss or give an example of a bank or client that’s doing this? Well, what data has brought to a certain financial institution or client? May we talk through what some of those time savings, or monetary savings might look like?

    Farouk Ferchichi 10:29
    Yeah, absolutely. This is one of my favorite topics with me because, well, while whether internally within our organization, or more importantly, with our clients, we like to talk a lot about value captured. Because we as a business to business to the end client kind of provider, we want our, we want to make sure that our products and services are adding measurable value. And without naming names. As you know, many of our clients are using our open banking and value add data, AI and digital technology services. And I want to share with you a couple, a couple of examples, one of our one from one of our large ePHI clients, where the customer retention across multiple product line and segments has improved incrementally because customer considering another firm, maintain their accounts and respective fee revenue. For the composite organization or this organization, I’m talking about the total risk adjusted operating profit increased due to this improved client retention, believe it or not by 24 million over a three year period of time. And then another client of ours who’s a little bit smaller mid size, regional FYI client, increase their wallet chair. And that’s due to more efficient reliable aggregation of financial data of their customer and supporting behind the scenes, the intelligence and the analytical services that we provide customers account managers get increased visibility into the assets, they do not actively managed with their client, which allow them to put the programs together to compare services of external assets and design internally products and solution to bring those assets in house leading to fundamentally an increase in revenue to the new due to the new asset and their management, the composite three year risk adjusted, which is the value metric that we use with our clients and confidence, profit increase for this FY with the effect of this wallet share program to a total of $15 million.

    Whitney McDonald 12:58
    Yeah, when you put it into those quantifiable measures, and I know that you said of course there’s the value capture and value add it really the the times the money savings, the time savings at all, it all adds up. And that’s exactly what you guys are working toward anything that we didn’t hit on that you wanted to be sure to. Yeah.

    Farouk Ferchichi 13:25
    If I may, I know everyone speaks about Chad GPT, and AI and generative AI and all of that. And a couple things I’d like to share are three things one, it is reality, you cannot run from it, it is coming. We invest in it in general and DNA. In particular data analytics line of business in particular, we’ve been using generative AI for years right now. It is our core IP behind the scenes, we just didn’t advertise it because it was not something that people talk about. It’s too technical. But we do now, the second thing I would say the best application that we see and we invest in it of how to implement charge GPT it is going to be on the back office to gain back credibility with the employees with the organization. It will be focused on automation creating content at scale, and so on. And then finally, I would say for charge GPT to be accepted and rollout at scale that has to be a deliberate effort around AI literacy as well as AI governance and openly discussing the AI ethics and The Good, the Bad and the audio that comes with it.

    Whitney McDonald 14:52
    You’ve been listening to the buzz a bank automation news podcast please follow us on LinkedIn and as a reminder, you can rate this podcast on Your platform of choice thank you for your time and be sure to visit us at Bank automation news.com For more automation news

    [ad_2]

    Whitney McDonald

    Source link

  • Forecasts and Black Swans: Generative AI Company AIMdyn, Inc. Uses Koopman Operator Approach to AI in CDC Covid-19 Challenge

    Forecasts and Black Swans: Generative AI Company AIMdyn, Inc. Uses Koopman Operator Approach to AI in CDC Covid-19 Challenge

    [ad_1]

    AIMdyn Inc showed a substantial improvement in forecasting capability using a 3rd-wave class of AI technologies based on Koopman operator approach to Artificial Intelligence.


    SANTA BARBARA, Calif., June 14, 2023 (Newswire.com)
    –
    AIMdyn Inc., a generative AI company, developed a suite of adaptive, context recognizing AI algorithms that can be used for forecasting dynamical processes with complex evolution, such as the spread of disease, and used for playing online games (https://ieeexplore.ieee.org/document/9483027). In the latest development, the AIMdyn team took on the challenge of forecasting disease evolution and applied the methodology to the CDC challenge data on COVID-19.

    The quote “‘It’s tough to make predictions, especially about the future‘” is attributed to a baseball-playing philosopher, Yogi Berra. And yet, we always try, with more or less success.

    In the latest contribution, the AI and Data Analytics company AIMdyn, Inc. compared one week ahead predictions of COVID-19 cases on the CDC COVID-19 challenge (https://covid19forecasthub.org/) made by a variety of prominent research groups.

    Different scientific teams utilized different approaches to the problem. AIMdyn’s is somewhat unique in that it recognizes (http://arxiv.org/abs/2304.13601) the limits on predictability enshrined in Berra’s comment. Namely, it incorporates the idea that “black swan” events (N. N. Taleb, The Black Swan: The Impact of the Highly Improbable. Penguin Books, 2008) are unpredictable, and thus, the best one can hope for is to recognize the change of environment and adapt the prediction algorithm.

    Utilizing the underlying mathematical methodology, the AIMdyn team obtained results that beat the next best algorithm by more than 20%, a very substantial improvement in week-ahead prediction capability.

    The methodology is based on the Koopman operator approach to artificial intelligence that enables self-supervised creation of generative models which fall into the latest, 3rd-wave class of AI technologies.

    The same methodology was previously applied to power an AI approach to network security via an algorithm that was licensed to Mixmode.ai, a leading AI network security provider protecting networks of large corporate and government customers.

    “Perhaps the prediction science is not as dismal as Berra stated – as long as one recognizes its limits and bounds the uncertainty associated with them,” said Dr. Igor Mezic, the co-Founder of AIMdyn.

    Acknowledgements:
    This work was partially supported under DARPA contract HR001116C0116, DARPA contract HR00111890033, NIH/NIAAA grant R01AA023667, and DARPA SBIR Contract No. W31P4Q-21-C-0007. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the DARPA SBIR Program Office. The support of scientific research of the University of Rijeka, project No. uniri-prirod-18-118-1257, and the Croatian Science Foundation through grant IP-2019-04-6268.

    Distribution Statement “A” (Approved for Public Release, Distribution Unlimited)

    Source: AIMdyn Inc.

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  • 7 Ways Data Helps Your Restaurant Succeed | Entrepreneur

    7 Ways Data Helps Your Restaurant Succeed | Entrepreneur

    [ad_1]

    Opinions expressed by Entrepreneur contributors are their own.

    Data makes the world go around. While not every restaurant takes advantage of the wealth of data, it’s essential in making smarter decisions. Cloud-based POS systems are equipped with numerous restaurant analytics and insights that generate data every time your staff takes an order, processes a credit card payment or closes a check.

    While each piece of information may provide some insight into your restaurant’s sales performance, when collected and analyzed together, they tell a complete and compelling story about your business.

    But once you have all this data, what can you actually do with it?

    1. Optimize your menu

    It’s easy to assume that your most popular menu item is also your most profitable. This might not be the case, however. By analyzing your data, you can get a clearer picture of your menu performance and understand which items bring you repeat customers and make you the most money. For example, if burgers are one of your best-selling items, but those customers don’t return, it’s time to investigate.

    The same is true for your lower-selling items. Some of your lower-performing items could have a lot of untapped potential. Your restaurant analytics software can tell you which items have a higher-than-average return rate for guests. With this new data, you can make decisions to improve your menu, like highlighting a particular item or updating the description and picture to tap into that potential.

    Related: Here’s How Data Analytics Is Improving Dining Experiences While Helping Increase Revenues for Restaurants

    2. Measure staff performance

    How well do you know your staff? Staff performance can be directly linked to your profitability. Staff reports let restaurants track productivity, efficiency and customer service levels. While some staff might be doing great, others might need more training. With this data, you can quickly identify rockstar employees and reward them, but also determine which employees aren’t measuring up to the mark and give them additional training to reach their potential.

    3. Uncover strengths – and weaknesses

    Is one server a pro at upselling? If a server is the best at selling high-priced menu items like wine bottles, this is an opportunity to pair them with other staff for training purposes. Pair high-performing staff with servers with low-performance numbers for shadowing and other exercises to help improve their sales.

    Is your best customer coming in next weekend? Make sure you schedule at least one of your best-performing staff members to make their experience memorable.

    4. Decrease turnover

    Turnover is a huge issue in the restaurant industry. Restaurant owners have been scrambling to find new ways to hire and keep staff. Keeping a closer eye on their performance could be the difference between staff that stays for the long haul or finding a new employee. By regularly looking at staff performance, you can better understand the employees that are struggling and might need more training or a change of role.

    Related: Using Data-Driven Concepts To Unlock Incremental Growth

    5. Increase staff happiness

    Staff performance can also give you insights into employee happiness levels. Sometimes the environment needs to change to keep staff happy and performing at their best. If you notice a pattern of decreased productivity across staff, it might be time to sit down for a chat with them or to start looking at how the current environment might be affecting the team.

    6. Create repeat customers

    How often are customers coming back? What are they ordering? Knowing these key pieces of data will help you determine how to shape your menu and how you upsell or interact with customers. With 360 analytics tools that connect operations, customer data and payments into your reports, you can get eye-opening data you can act on.

    Each time a credit card is swiped, the restaurant analytics software generates a unique profile for every guest. This provides insights into their preferred menu items, purchase history, frequently used payment methods, preferred location and other details. With this information, you can pinpoint VIP customers and elevate their experience with tailored promotions or complimentary items.

    Related: 25 Ways You Can Turn a One-Time Buyer Into a Repeat Buyer

    If a guest has dined at your restaurant six times in the last four weeks, you can access their guest profile to identify their favorite drink or appetizer and offer it to them as a complimentary item. This gesture is an excellent way to show your appreciation and build customer loyalty.

    7. Improve stock management and reduce waste

    If you’re constantly running out of ingredients or always have specific ingredients leftover from under-ordered items, it’s time to take a look at your inventory.

    Proper inventory management is an essential part of running a successful restaurant. By analyzing inventory data, restaurants can identify trends in food waste and improve profitability. Restaurants can also use inventory data to optimize ingredient usage and reduce the risk of running out of popular menu items.

    With inventory management software like Lightspeed Inventory, restaurants can make the most of their ingredients, eliminate manual stock counting, reduce human error and simplify their inventory management with real-time deductions as items are sold and automatic replenishment when you get fresh inventory.

    Every day is an opportunity to get new insights into your business. Data can help you do everything from optimizing your operations to improving the overall guest experience. Not sure where to start? All it takes is partnering with the right restaurant management software.

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    Peter Dougherty

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  • Banks empower those who power the economy | Bank Automation News

    Banks empower those who power the economy | Bank Automation News

    [ad_1]

    While we continue to measure the economy by market strength, we can’t forget that for many, financial security means affording the basics like shelter, food and gas. There’s a lot of talk about democratizing finance, but how do we move our society forward if we don’t reach the masses in a meaningful way? The underserved market — with one of the biggest populations being the middle class — requires access to a financial system that can service them responsibly. 

    The opportunity 

    There is a massive opportunity in providing financial services catered for the middle class. Although commonly overlooked, this segment fuels two-thirds of the world’s consumer spending. With about two of every five consumers having a credit score under 700, there is a massive population of people facing financial rejection and financial services are not meeting them where they are. 

    Linda Brooks, chief technology officer at Atlanticus

    Being “underbanked” starts with banks, but doesn’t end there. It impacts all aspects of people’s lives, including their ability to buy or rent a home, acquire insurance and utilize affordable services that can help get them off their feet. With millions of Americans having limited options when it comes to financial services, there is an urgent need for banks and fintechs that have a deep understanding of this demographic and can cater their offerings to them prudently. 

    The challenges 

    Despite a large consumer need for banking services catered toward the middle class, financial institutions are not capitalizing on it because of the challenges presented when working with those with a less-than-perfect financial history. This segment is underserved because it’s not easy to serve middle-class Americans responsibly; it’s serious work that requires deep expertise and a long track record of success to do it properly.  

    Providing banking and lending services to non-prime lenders presents risks, but with 58% of Americans living paycheck to paycheck as inflation spikes, ignoring the changing environment can be detrimental. 

    The solutions 

    It starts at the top: to provide services to an underbanked market, you need executives, business leaders and product developers that understand that market. Focusing on diversity, equity and inclusion within our financial institutions will continue to push us forward in our evolution and understanding of the needs of all demographics.

    More tactically, we must lean more heavily on tech, analytics and data to inform our understanding of the middle class better. A track record of data on consumer behavior, repayment patterns and spending habits can help banks and their partners tailor their offerings to the middle class, but data is only as good as the conclusions that can be drawn from it. 

    Banks should lean on technology that can empower them to more comfortably provide services to this demographic. Deep historical data informs many financial institutions’ decision-making engines, and analytics can be tapped to better predict outcomes and minimize risks that come along with lending for both the consumer and the bank. These tech tools are readily available, however, there is not a broad enough adoption to give the everyday consumer the options they require. Banks should leverage fintechs for predictive and risk mitigation solutions that prioritize reaching these consumers in a way that provides a positive outcome for both the bank and the banked. 

    The middle class plays a critical role in our economy’s growth, and yet financial services are leaving this segment underserved. The technology needed to provide banks and lenders with the security and confidence to help the middle class exists, but there needs to be a desire from the top to implement them. It starts with us, with building diverse leadership teams of individuals who want to make a change. 

    Linda Brooks is the chief technology officer at Atlanticus, a financial technology company powering more inclusive financial solutions for everyday Americans, and was previously a developer at IBM for more than 16 years.  

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    Linda Brooks

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  • Ardent Awarded Competitive Three-Year $3.69M CBP Task Order

    Ardent Awarded Competitive Three-Year $3.69M CBP Task Order

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    Press Release
    –

    Oct 19, 2022


    ARLINGTON, Va., October 19, 2022 (Newswire.com)
    –
    Ardent Management Consulting, Inc. (Ardent), a trusted provider of digital transformation, data science and analytics, and location intelligence, announced its award of a competitive task order in support of the US Border Patrol’s (USBP) Advanced Analytics Branch at DHS Customs and Border Protection (CBP).

    Ardent expands its portfolio of work with CBP under this new three-year contract, supporting the Advanced Analytics Branch of the Strategic Planning and Analysis Directorate with geospatial, temporal, and geostatistical analysis. Through the building and enhancing of analytic tools, Ardent will enable USBP to use and access data to create greater situational awareness of the environments where Agents and stakeholders within the CBP operate. Ardent also will provide user training and technical support to the Border Patrol for geospatial tools and capabilities at USBP HQ and in the field.

    “Ensuring the safety and security of our border is a critical national security and humanitarian issue. Enabling better data and data-driven decision-making through technology is at the core of what we do. Ardent thrives in helping DHS and our government customers resolve their most complex and challenging problems,” said Josh Rubin, Ardent’s Chief Growth Officer.

    “Our team takes pride in being selected by CBP as a partner supporting informed decisions on trends in border security and identifying risks along the borders that make our country safer.”

    About Ardent

    A digital transformation, location intelligence, and data analytics firm, Ardent brings a significant history of innovative proven best practices “at the speed of the mission” to Federal Civilian agencies, DHS mission components, State and Local entities, and the commercial and non-profit sectors. Ardent Management Consulting is certified to 9001:2015, its Development Projects are CMMI-Dev V2.0 Maturity Level 3 rated and its management systems (ISMS/ITSMS) are certified to IS0 27001:2013, and ISO 20000-1:2018 standards by SRI Quality System Registrar. For more information, visit www.ardentmc.com or reach out to Emily Morgan (emily.morgan@ardentmc.com).

    Source: Ardent

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  • Ardent Announces FAA eFAST Contract Vehicle Award

    Ardent Announces FAA eFAST Contract Vehicle Award

    [ad_1]

    Press Release
    –

    Jul 22, 2022


    ARLINGTON, Va., July 22, 2022 (Newswire.com)
    –
    Ardent Management Consulting, Inc. (Ardent), a trusted provider of digital transformation, data science and analytics, and location intelligence, announced its award of the Federal Aviation Administration (FAA) Electronic Accelerated and Simplified Tasks (eFAST) multi-year Master Ordering Agreement (MOA). 

    The eFAST MOA offers FAA and government-wide customers broad technical scope, several contract and award types, and a wide array of labor categories with fixed ceiling rates. “We are excited to bring Ardent’s service offerings to a new market. The FAA is an ideal consumer of our innovative solutions in Agile, DevSecOps, cloud, data, and geospatial,” said Josh Rubin, Ardent Chief Growth Officer. “We look forward to the opportunity to support critical government missions through eFAST.” 

    About Ardent

    A digital transformation, location intelligence, and data analytics firm, Ardent brings a significant history of innovative proven best practices “at the speed of the mission” to Federal Civilian agencies, DHS mission components, State and Local entities, and the commercial and non-profit sectors. Ardent Management Consulting is certified to 9001:2015, its Development Projects are CMMI-Dev V2.0 Maturity Level 3 rated and its management systems (ISMS/ITSMS) are certified to IS0 27001:2013, and ISO 20000-1:2018 standards by SRI Quality System Registrar. For more information, visit www.ardentmc.com or reach out to Emily Morgan (emily.morgan@ardentmc.com).

    Source: Ardent

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  • Ardent Selected as Joint Venture Partner for Department of Commerce’s National Technical Information Service

    Ardent Selected as Joint Venture Partner for Department of Commerce’s National Technical Information Service

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    Press Release
    –

    Jul 12, 2022


    ARLINGTON, Va., July 12, 2022 (Newswire.com)
    –
    Ardent Management Consulting, Inc. (Ardent), a trusted provider of digital transformation, data science and analytics, and location intelligence, announced its selection as a Joint Venture Partner (JVP) for the National Technical Information Service (NTIS), Department of Commerce. NTIS helps federal agencies make better decisions about data by providing the support and structure that helps partners securely store, analyze, sort, and aggregate data in new ways. 

    Ardent joins an exclusive group of data sciences experts that provide-next generation solutions to NTIS customers across the Federal landscape. NTIS leverages its private-sector partners’ knowledge to create new ways of using data to solve problems. As a Joint Venture Partner, Ardent will apply its Data framework using advanced analytics and ML Ops to accelerate federal agencies’ ability to collect, connect, access, analyze, and extract valuable insights from federal data and enhance data services. “The award of this unrestricted vehicle demonstrates the maturity and depth of Ardent’s data science and analytics capabilities,” said Brandon LaBonte, Ardent CEO. “In partnership with NTIS, this vehicle uniquely positions Ardent to enhance the way our Federal partners manage, secure and apply data to deliver outcomes that improve the mission.”

    About Ardent

    A digital transformation, location intelligence, and data analytics firm, Ardent brings a significant history of innovative proven best practices “at the speed of the mission” to Federal Civilian agencies, DHS mission components, State and Local entities, and the commercial and non-profit sectors. Ardent Management Consulting is certified to 9001:2015, its Development Projects are CMMI-Dev V2.0 Maturity Level 3 rated and its management systems (ISMS/ITSMS) are certified to IS0 27001:2013, and ISO 20000-1:2018 standards by SRI Quality System Registrar. For more information, visit www.ardentmc.com or reach out to Emily Morgan (emily.morgan@ardentmc.com)

    Source: Ardent

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  • Ardent Honored With Small Business Award From DHS

    Ardent Honored With Small Business Award From DHS

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    Press Release
    –

    Jun 13, 2022


    ARLINGTON, Va., June 13, 2022 (Newswire.com)
    –
    Ardent Management Consulting, Inc. (Ardent), a trusted provider of digital transformation, data science and analytics, and location intelligence, has received the prestigious Department of Homeland Security (DHS) Small Business Award from the Office of Small and Disadvantaged Business Utilization (OSDBU) and the Office of the Chief Procurement Officer (OCPO). The award recognizes Ardent’s distinguished performance and technology innovation in support of the U.S. Citizenship and Immigration’s important mission. DHS established the Annual Small Business and Small Business Advocate Awards Program to recognize strong, innovative business partners in advancing the Department’s mission. 

    “We are honored to receive this award for exemplary performance. Since our inception, Ardent has driven to consistently provide excellence, and help our federal partners accelerate innovation. We look forward to continued collaboration with DHS across the enterprise to help solve the most difficult technical challenges to advance the mission,” said Brandon LaBonte, Ardent’s CEO.

    A notification of this award is posted on www.sam.gov and additional information about the award can be found on the DHS website here.

    About Ardent

    A digital transformation, location intelligence, and data analytics firm, Ardent brings a significant history of innovative proven best practices “at the speed of the mission” to Federal Civilian agencies, DHS mission components, State and Local entities, and the commercial and non-profit sectors. Ardent Management Consulting is certified to 9001:2015, its Development Projects are CMMI-Dev V2.0 Maturity Level 3 rated and its management systems (ISMS/ITSMS) are certified to IS0 27001:2013, and ISO 20000-1:2018 standards by SRI Quality System Registrar. For more information, visit www.ardentmc.com or reach out to Emily Morgan (emily.morgan@ardentmc.com).

    Source: Ardent

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  • Urban SDK Receives the EFI Entrepreneur and Job Growth Award

    Urban SDK Receives the EFI Entrepreneur and Job Growth Award

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    Enterprise Florida recognizes Jacksonville company for job creation and fueling innovation across Florida.

    Press Release
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    updated: Feb 16, 2022


    JACKSONVILLE, Fla., February 15, 2022 (Newswire.com)
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    Enterprise Florida presented Urban SDK with the EFI Entrepreneur and Job Growth Award during its most recent Board of Directors meeting in Tallahassee. The Jacksonville-based data analytics and visualization company was recognized for its commitment to job growth within the state, as well as its work showcasing Florida as a global leader in innovation.

    “This is a proud moment for our company,” said CEO and Co-founder Drew Messer. “We started Urban SDK to do 2 things: help the government transform itself through a modern data management strategy and prove we can build a $100M dollar tech startup out of Jacksonville, Florida. This award further validates our vision.”

    Urban SDK was started in 2018 by Messer and COO and Co-founder Justin Dennis. In 2021, the company expanded internationally and tripled its valuation in under 12 months. For 2022, the company forecasts 300% growth, which Dennis believes is due to the company’s best-in-class technology meeting a strong demand in the market.

    “Our modern data management system optimizes business decisions and operations through data, and the impact is no clearer than within Florida,” said Dennis. “In Jacksonville, we’re the data management system for the country’s first autonomous public transportation vehicles. Meanwhile, in Tampa Bay and Central Florida we’ve established regional contracts between counties and DOT Districts that give leaders an ability to plan regionally instead of county by county.”

    The EFI Entrepreneur and Job Growth Award comes on the heels of Urban SDK closing its Seed Series, which was led by the Florida Opportunity Fund (FOF), Florida-based venture capital firms DeepWork Capital and venVelo, and TechStars.

    “Urban SDK and its founders are changing the way we look at the world and embody the purpose and power of investment by the Florida Opportunity Fund,” said Florida Opportunity Fund Executive Director Meredith Pelton. “The Fund proudly supports Urban SDK’s dynamic financial success, team expansion, and state and community investment as Florida continues to fuel innovation.”

    “We’re honored to be in the FOF portfolio,” said Messer. “We’re a home-grown Florida business with the state as a majority shareholder. We employ 21 Florida residents with high paying jobs, and we’re building a platform that helps our government innovate at the highest levels. This investment into Urban SDK is evidence that EFI is making Florida the premiere business destination in the world.”

    About the EFI Entrepreneur and Job Growth Award 

    The Enterprise Florida Entrepreneur and Job Growth Award was established by Enterprise Florida in 2019 to recognize Florida businesses that have created jobs and invested in communities across the state.

    About Urban SDK

    Urban SDK is a data analytics and visualization software that transforms data from the physical world and turns it into intelligence. The platform is a single solution for all data needs — transforming, visualizing, enriching, and managing data for all subscribers. 

    Media Contact

    Jonathan Bass
    jonathan.bass@urbansdk.com
    386-228-7668

    Source: Urban SDK

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