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Banking Fintech

Five questions for banks considering partnering on a fintech card program

By David LaRoche

Over the past few years, we’ve helped financial institutions conduct due diligence before partnering or supporting a fintech that wants to enter the credit-card market.

Generally speaking, we advise them to consider the fintech’s experience in the credit industry, its pricing model, its ability to scale and grow – and whether the bank can help without exposing itself to unreasonable risk. We also recommend the bank consider the fintech’s technology and its ability (and willingness) to improve product offerings, increase efficiency, and lower costs.

We recommend the bank detail its expectations and take a project-based approach: Who needs to know what by when? A big early discussion: Share risks with the fintech partner and determine what each one does best and how they should present a united front to regulators and other outside organizations.

Here are five things we tell them to consider before we ever discuss helping them vet the fintech.

Is the fintech and its partners well capitalized 

This is generally the threshold for most fintechs to support the numerous operational investments needed to successfully launch a card portfolio.

Does the fintech have a strong affinity to its customer base, and does the card they are offering have a good value proposition? Affinity-card issuers such as the former MBNA America (now Bank of America) found that customers carrying the card of an organization, professional group, or alma mater were less likely to fall behind on payments or default on their loan either because of their demographics or because of a (misplaced) fear that the organization might find out.

In terms of the value proposition, the fintech may be able to offer something that the financial institution doesn’t. This includes:

  • New ways to assess creditworthiness (e.g., going beyond a credit score to offer banking products to those who might otherwise not qualify).
  • Products specifically designed for customers with low credit scores or no credit scores to help them build (or rebuild credit). These products often come with a higher APR or an annual fee to reduce risk to the institution.
  • Custom card rewards. This may get back to affinity, or it may involve other partnerships. There are probably no better examples than travel-rewards cards (miles tied to spending) and collegiate cards, particularly those offered through athletic departments that might offer access to tickets or other experiences.
  • Hybrid cards that combine a credit card with an installment loan that could include debt consolidation.
  • Cashback cards that can be used to save money on purchases, buy gift cards, or pay off balances.

Does the fintech bring any next-level technology to excite the customer base? This is one you can test by looking at their website or mobile app and during conversations about investments that might also reduce risk concerns:

  • Automated credit scoring and monitoring services, which are admittedly already offered fairly widely by people like Credit Karma and Experian.
  • Electronic invoicing and online customer portals, both of which can help financial institutions reduce late payments and mitigate credit risk.
  • Mobile payment apps such as Apple Pay or Google Pay that use tokenization to allow customers to pay without exposing their actual credit card account number.

Does the fintech plan to invest in critical infrastructure like compliance and data capabilities? There are three broad categories of risk in these types of partnerships:

  • Reputational, which occurs whenever any new product or service is introduced, regardless of whether it was developed in-house or by a third party.
  • Regulatory, which is likely a priority for any financial institution, particularly given (1) recent events and (2) the likelihood that it could be a long time before banking regulations are changed to address fintech products.
  • Unforeseen, fintechs may have limited experience dealing with regulations and regulators. For banks, this risk can be avoided if you choose to partner with a more experienced fintech (or one that has leaders with experience in the credit card business).

If a Sponsor bank is involved, are there qualified people at the fintech to manage the relationship? As noted above, a LinkedIn search of top executives across operations, revenue generation, credit, and collections can answer this question, as can simply asking, “Who’s got credit card experience?”

Entering into a relationship with a fintech requires proper due diligence from stakeholders – or consultants – who understand the minefields, or at least some of the significant differences between the two of you. PAG can manage this process to ensure the partnership is optimized, compliant, and profitable before the final contract is signed and even afterward as results start coming in.

David LaRoche is the managing partner of U.S. operations for Predictive Analytics Group. Our proprietary GOBLIN enterprise data platform helps clients consolidate data from multiple legacy systems, overcome a lack of in-house advanced analytics experience, and identify new segmentation opportunities to improve portfolio growth and profitability.

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Portfolio Purchase Support

If you’re buying a portfolio, data analytics support can be helpful


As a data analytics company, we understand the importance of informed decision-making when it comes to investing in portfolios. With ever-evolving market trends and the need for a more data-driven approach to portfolio purchases, it can be challenging for companies to navigate this process on their own. This is where our team of experts comes in to offer support, guidance, and data-driven insights to help companies maximize their portfolio purchases.

Our portfolio purchase support services are designed to assist companies in the due diligence process. This includes conducting market research, analyzing financial data, and providing market insights to help companies make informed decisions. Our team of experts leverages their industry experience and our data analytics tools to provide the most comprehensive and up-to-date information for our clients.

Once the portfolio purchase has been made, our team continues to offer support by helping companies manage their portfolios. This includes monitoring market trends, providing regular performance updates, and making recommendations for portfolio optimization. Our data analytics tools and expert analysis help companies in maximizing returns and minimizing risk.
We understand that every company has unique investment goals and risk tolerance. That is why we offer customized solutions tailored to each client’s specific needs. Our team works closely with each company to understand their objectives and provide tailored recommendations that align with their goals.

With Predictive Analytics Group’s portfolio purchase support and ongoing portfolio management services, we help companies to make informed decisions and maximize their portfolio purchases. Contact us today to learn more about how our team of experts can help you achieve your goals.

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Banking News

The top risks facing community banks: How data analytics can help

Recent research has shown that community bank executives view cybersecurity and credit as the top risks facing their institutions. As a data analytics company, we understand the challenges that community banks face in managing these risks, and we believe that our services can help mitigate these risks by providing valuable insights into the data that these banks collect. In this post, we’ll discuss how data analytics can help community banks to manage these risks.

First and foremost, data analytics can help community banks to identify and mitigate cybersecurity risks. By analyzing data about network traffic, login attempts, and other security-related events, we can help banks to detect patterns that indicate a potential threat. By identifying these patterns early, we can help banks to take steps to mitigate the risk before a cyber-attack can occur. Additionally, our expertise in data visualization allows us to make data clear and easy to understand.

Credit risk is another area where data analytics can provide valuable insights. By analyzing data about loan performance, credit scoring, and other financial metrics, we can help banks to identify patterns that indicate a higher risk of default. This data can help banks to make more informed decisions about which loans to approve or deny, and can also help banks to identify areas of their loan portfolio that may need additional attention. By providing these insights, data analytics can help banks to minimize their exposure to credit risk.

Another way data analytics could help community banks is by providing insights into customer behavior and banking habits. This information can be used to improve customer service and to create targeted marketing campaigns that drive new business.

Finally, data analytics can help community banks to comply with data privacy and security regulations. Our team can help banks to understand the data that they collect and how it is being used, and we can also help banks to establish policies and procedures to ensure compliance with data protection regulations. By providing this level of visibility and oversight, data analytics can help banks to mitigate the risk of regulatory fines and penalties.

At our Predictive Analytics Group, we understand the unique challenges facing community banks, and we believe that our services can help these institutions to mitigate the top risks that they face. By providing valuable insights into the data that these banks collect, we can help community banks to make more informed decisions, reduce their exposure to risk, and comply with regulatory requirements.

Categories
Banking Case Studies Decision Support General Consulting Underwriting Strategies

Adjustment of initial line assignments and authorization strategies helps client achieve growth & loss targets

line assignments authroization strategies

Challenge:

A PAG client in the US credit card market was not achieving its Year 1 growth and loss-rate projections on a new credit card product.  Balances were growing in the lower half of the credit spectrum and activation on higher FICO balances was not happening at a rate to offset the lower credit balance growth.

PAG Solution:

PAG reviewed the Client’s underwriting approach to see if adjustments could be made to the existing account management strategies to stimulate balance growth in the proper areas.  PAG found the Client was being too conservative with its initial line assignments and authorization strategies.  Average credit lines for 680+ FICO customers were only $1,800 while the Client was blocking nearly 20% of transactions at the point of sale. These higher-end customers simply weren’t using the card: They weren’t being assigned sufficient credit lines to meet their shopping needs and using the card was a hassle.

PAG used refreshed Vantage scores along with other data attributes to quickly build credit lines in a revamped Credit Line Increase (CLI) strategy so those higher-end credit customers could see significant credit limit increase to meet their shopping needs. PAG also rebuilt the authorization strategy to fit the demographics of the Client’s portfolio, resulting in fewer stoppages at the POS without a significant increase in transactional fraud.  PAG also designed Reissue strategies so the Client could continue to pursue the right credit growth when the initial batch of cards expired.

Client Benefits:
  1. PAG was able to increase balance growth in customers with an initial line of $2,000 or greater by 40% while lowering overall Year 1 delinquencies on the portfolio by more than 10%
  2. PAG complemented the enhanced CLI program by loosening the authorization strategies, enabling higher-credit customers to feel more comfortable using their cards. The resulting model and analysis increased transactions per month on active users by more than 20% with a minimal increase in fraud losses to only five basis points.
  3. PAG worked with the Client’s marketing team to deliver digital and direct mail messages to inactive customers by promoting its reward platform and mobile application. The results were a 22% increase in first-time use of the card in the next nine months.
  4. The Client exceeded its Year 1 growth targets and has since seen no substantial risk in fraud or credit losses. Growth continues to trend in the right direction.
  5. The Client has re-engaged PAG to have a PAG SME on its risk council. That person is helping guide account-management strategies while making sure learnings are being shared with the underwriting team to ensure wins are seen in the initial book of business for future portfolio bookings.
Categories
Banking Case Studies General Consulting Regulatory Compliance Regulatory Readiness Preparation Scorecard Builds/Validation

PAG adds processes and reports to help lender convince regulators its exception documentation was sound

Challenge:

A large unsecured loan provider in the United States was having challenges with its model suite and convincing its regulator and partners that its models were compliant and in control. The client had a few Matters Requiring Attention (MRAs) and other regulatory issues threatening to disrupt its business and didn’t have a large budget to hire a full modeling team to close the gaps.

PAG Solution:

PAG was hired to do a complete end-to-end review of three different models and assess their overall performance and compliance relative to industry expectations. PAG was able to determine that all three models were built from sound assumptions, and the performance seen in the development samples was consistent with production.

The client, however, had a number of issues with exceptions in its lending process and a complete lack of model documentation that confused regulators about performance and compliance. PAG created a report to separate underwriting performance on loans within the credit sandbox versus those approved on exception. We also compared the client’s model documentation to the OCC’s requirements and identified the missing documentation. PAG put a six-month plan together to close all gaps and identified an ongoing governance structure that the regulator blessed, and the enhancement work began.

Client Benefits:
  1. PAG successfully built out its client’s enhancement plan to close down its areas of opportunity in the model governance space to the point it was able to meet monthly with its regulator to review progress.
  2. PAG’s staff allowed the client to focus on documenting its model practices while we educated the model team on ongoing governance, saving the client hundreds of thousands of dollars on personnel costs.
  3. PAG was able to help get the client’s current MRAs closed and allow it to go back to running its business.
  4. The client successfully passed a regulatory audit after one year with zero significant findings or MRA’s.
Categories
Banking Case Studies Collections Optimization

Overhaul of client’s collections and recovery processes drives strong improvement across buckets

Challenge:

A large client in the US credit card market was not getting the performance on its forecasted portfolio roll and delinquency rates.  Most of its booked account vintages were underperforming, which was restricting how fast it could grow its portfolio. The client wanted to improve performance, reduce operating costs, and ultimately lower charge-off rates.

PAG Solution:

PAG was hired to re-engineer the client’s Collections and Recovery processes.  After an initial diagnostic, PAG was able to determine our client was over-penetrating its delinquent portfolios and not taking advantage of scorecards and technology it had at its disposal.  PAG helped analyze delinquency trends and built a number of strategies including a Bucket 1 import strategy, payment propensity strategies to optimize payment suite offerings, and a channel optimization strategy to help contact the customer via their channel of preference.

PAG also built a vendor placement model to help determine which third-party vendors should work late-stage delinquency and charged-off accounts based on prior performance and helped design business requirements for the client’s website so full collections functionality could be achieved. As a result, the Client could start driving customers there for payment instead of over-dialing.

Client Benefits:
  1. PAG increased website traffic by 50% and online payments increased from 30% to 60% of delinquent payments in the first six months of the transformation.
  2. Operating costs were reduced by 35% in the first six months as the Client used SMS and email to replace much of its dialing and drove customers to the website via embedded links to pay there.
  3. Payment suite optimization was increasing by 20% with more targeted offers reducing backend roll rates by 12% in the first six  months.
  4. Recovery rates improved by 11% in the first six months as vendor placements were optimized using the new PAG model.
  5. Customer complaints dropped by 23% as more customers were paying online, which led to fewer outbound phone calls.
  6. Overall charge-off rates were reduced by 7% in Year 1 and the Client continues to report those numbers improving in Year 2.
  7. The Client has re-engaged PAG to look at its fraud servicing area to produce similar results.
Categories
Banking Case Studies Forecasting General Consulting

Card portfolio acquirer sees double-digit improvement in profitability after PAG overhauls forecasting process

Forecasting
Challenge:

One of PAG’s large clients in the United States purchases credit card portfolios from other US-based financial institutions. Our client was looking for highly effective forecasting methods to determine loss rates, growth curves, and other critical metrics to increase its profit margins through a more accurate determination of portfolio profitability, thus impacting its offered purchase price.

PAG Solution:

We re-engineered and overhauled its forecasting process, designing a data template and schema for standard portfolio purchases that would provide more detailed information on what drives credit card profitability.

We then used our GOBLIN platform to ensure data from the prospective portfolio was received quickly and in a ready-to-analyze format. Using our years of experience in the card industry and our proprietary forecasting models, we utilized different forecasting techniques (e.g., exponential smoothing, complex weighted averages) to increase the speed and accuracy of our client’s portfolio evaluation process. This allowed the client to focus on portfolios with more upside and avoid ones with limited profit potential.

Client Benefits:
  1. PAG successfully increased the speed of the valuation process from approximately three weeks to five business days under the current redesigned process. These changes enabled our client to evaluate more portfolios and turn around bids much faster, getting a leg up on other interested acquirers.
  2. Our client has access to more streamlined data through GOBLIN and can run its own analysis and compare using our models.
  3. Our forecasting models had reduced variability in the observed loss curves by over 30%, with an average variance of only +- 2%.
  4. Our client has seen portfolio profits increase by an average of nearly 17% within a year of acquisition.
Categories
Banking Case Studies Underwriting Strategies

Profitability pressure forcing banks to review their underwriting strategies

By David LaRoche

Credit card issuers – whether they’re lending to a broad consumer base or to the customers of affinity partners – are facing challenges ranging from rising delinquencies, pressure from partners that don’t want to alienate their fans and customers with low approval rates, and a desire to mitigate risk.

For the past decade, the card lending community has faced increasingly tighter regulation, restrictive lending criteria, and continued economic challenges. More recently, they’ve felt pandemic and rising inflationary pressures that some believe could lead to a near-term recession. As a result, lenders are increasingly looking at revisiting their underwriting strategies.

Are you putting updates on hold?

Many of them, however, are so understaffed that they’ve had to put updates to their scoring models, segmentation strategies, and model documentation requirements on the back burner.

In some cases, they’ve turned to external data scientists to help them address their concerns before they become full-blown issues with regulators and internal finance teams. You may find that you can use what we have done for clients to assess your own situation.

Case Study: Top 10 Card Issuer

PAG worked with a top 10 card issuer with more than 30 co-brand partner portfolios and several branded portfolios. The client asked its small staff of risk-management analysts to revise the custom update to its portfolios and then asked us to take a broader look at its strategy.

The team took a two-stage approach, starting with developing a new custom score for underwriting. We then used the approved score to develop custom underwriting strategies for several of the client’s larger co-brand portfolios and two of its branded portfolios.  Our analysts and data modelers worked with the bank to gather the needed data requirements from both internal bank data and external data that PAG supplied. Our team then developed a new custom underwriting score with six unique segments. That led to an increase in the Kolmogorov-Smirnov (KS) Statistic from 34 to 48 for the overall scorecard when rolled up.  The client quickly approved the score and quickly coded it into the system and refreshed its underwriting strategies using the new custom score and the Vantage Score.

In the case outlined above, the new model drove higher approval rates across the targeted portfolios by 22%, while targeted loss rates decreased by 6% and profitability increased by 26% across those portfolios.

Having an adequate number of data sources is also a concern of many issuers we talk to. We worked with a client whose underwriting strategy for line assignments, risk-based pricing, and decisioning used generic data sources that had been in place for 24 months. The portfolio’s performance had been acceptable, but the client wanted to integrate more alternative data sources and use a retro study with several data providers. The client also wanted to reduce loss rates by 15% without impacting approval rates.

Try some different approaches

If you’re having similar issues, you could obtain retro data on previously reviewed applications (both approved and declined). The PAG team used two quarters’ worth of data and asked our data scientists to create a reject inference model to project the performance of declined applications to allow both swap-in and swap-out capabilities of the new strategy.  We then used actual performance on the approved accounts and reject inferencing on the declined applications to create a new underwriting strategy, line assignment, and risk-based pricing approach.

As a result, our analysts and data modelers were able to identify and implement key areas of automation for the company with three new data sources for both credit and fraud risk targeting and create a model that was trending toward a 28% decrease in Lifetime Loss Rate within the first nine months – about 25% better than original projections.

In addition, approval rates came in significantly above the client’s approval rate and manual referral volume objectives.

Many issuers are so busy playing whack-a-mole with current application volume that they don’t have time to take the longer view. In other cases, they’re so used to “doing things the way they’ve always been done” that they don’t assign a fresh set of eyes to the problem. And that may be a mistake.

Dave LaRoche is director of business development for Predictive Analytics Group, which helps clients make better decisions, reduce regulatory risk, and optimize business performance through data storage and consolidation, creation of account-management strategies, actionable reporting, new marketing segmentation opportunities, scorecard builds/validation, and short-term staffing augmentation. 

Categories
News

Predictive Analytics Group funds endowed scholarship

Predictive Analytics Group funds endowed scholarship at University of Delaware to engage future Business Analytics professionals

CONTACT: David LaRoche ([email protected]), 302-588-7053

NEWARK, DE — As the use of data analytics expands, many executives are challenged to consolidate data from multiple legacy systems, develop in-house advanced analytics experience, and control access to specific reports across the enterprise. In many cases, they need advanced analytic support but either can’t afford them on a full-time basis or don’t know where to find them and train them.

Enter Predictive Analytics Group (PAG), a company formed by three University of Delaware alumni. PAG is poised to leverage the boom in business analytics – an industry that the U.S. Bureau of Labor Statistics predicts will grow by up to 35 percent between 2019 and 2029.

The company, which employs 25 people from offices on UD’s STAR campus – a full 40% of whom are UD grads – is adding a $50,000 endowed scholarship to its ongoing support of the UD Business Analytics department, with the first student receiving assistance in the Spring 2022 semester.

“Being able to partner with our alma mater and then sharing what we experienced as students and throughout our careers has been, and will continue to be, very rewarding for all of us at Predictive Analytics Group,” said CEO Stephen Hoops, a 1998 graduate of UD’s Business and Economics (BE) school. “We are proud to be the first company to fund a scholarship like this for the Business Analytics department and to support the next generation of top business analysts. The university is already doing a terrific job in this area as we’ve seen with the people we’ve hired and brought in as interns and contractors.”

Hoops and co-founders (and ‘98 BE classmates) Chief Data and Analytics Officer Dee Ridgway and Managing Partner of U.S. Operations David Laroche brought their own experiences in top roles at major financial institutions to their full-service management consulting company – and to emerging data analysts.

Launching the scholarship has been personally meaningful to the company leaders, especially Hoops, who originally came to UD on an athletics scholarship. When an injury abruptly changed his plans, he found himself changing majors and working at MBNA America full-time in order to graduate.

“Working full-time while I was in school made me who I am today, but it’s not something I would wish on today’s students,” Hoops said. “I’d rather students have the ability to concentrate on their school experience and it not be a secondary aspect of their life, which it was for me.”

For some time, PAG has made it a priority to support UD and its students, through philanthropy as well as sharing their expertise. The company regularly hires recent UD graduates, mentors current students through Horn Entrepreneurship programming, and offers internship positions to undergraduates. The scholarship, Hoops said, is the next step in connecting talented students with opportunities to success in the business analytics field.

“Today’s leading-edge technologies are creating new opportunities for businesses to elevate their performance through data analytics,” said Alfred Lerner College of Business & Economics Dean Bruce Weber. “We at Lerner College are delighted to see Predictive Analytics Group and our alumni step in to help advance and support our students in this rapidly growing discipline. I am grateful for PAG’s generous philanthropy and mentorship and know that, together, we can further business education opportunities for talented students.”

“Working as a part time employee for PAG under Dave LaRoche’s leadership was a great learning and development experience,” said Carson Furci, a UD senior majoring in Entrepreneurship. “I joined PAG this past Spring and started working on projects with Dave. Since day one, he has worked hard to be the best mentor and teacher to me that he can be. I have used Excel, Salesforce, Tableau, LinkedIn, and learned basic SQL coding, and was included in the redesign and enhancement of the PAG website and other social media platforms that we use. I feel the experience is invaluable and will help me in whatever role I pursue after my graduation.”

“At Predictive Analytics, we have people with a tremendous amount of experience,” LaRoche said. “It can be very tough for organizations to onboard and train new hires at once, but we can help students early in their careers, so they graduate with a foundation of skills that benefit them and make them attractive to future employers, whether it’s working with us or with someone else. But I won’t deny that the scholarship-application process is a great way to meet top candidates.”

This is especially important as the industry grows and looks for new talent. Mentorship and hands-on learning experience ensure graduates can find jobs in any number of industries, producing reports and forecasts so businesses can anticipate trends, meet customer needs, and manage their products and services better.

For Ridgeway, who works closely with the UD student interns at Predictive Analytics Group, interacting with current Blue Hens is a chance to tap into fresh talent. Students may stay with the company for several years, so Ridgeway and his colleagues also get to witness students come into their own, as they grow from undergraduate to young professional.

“Our interns are home-grown, but they have their own backgrounds and experiences, so we get insights that are different from when we were at UD in the late ’90s and early 2000s,” he said.

“The students and recent graduates that work with us are eager to learn,” Hoops added. “They want to understand your experiences and there is nothing more rewarding than being able to relay those experiences and offer them meaningful advice that will help shape their own careers.”

Categories
Case Studies Regulatory Compliance

PAG helps new affinity card issuer build compliance infrastructure

 

Company: New Market Entrant – US Co-Brand & Branded

Challenge:
Company was planning to enter the US Credit Card Market and had no compliance personnel or infrastructure to support their efforts. As they were looking to procure affinity partners, they were facing numerous audits for documentation and processes that did not exist.

PAG Solution:
PAG was engaged in a 3-stage approach, which started with PAG building out over 50 policies and supporting procedures to govern regulatory expectations and to support ongoing call center operations. From there, PAG documented technical requirements to help the client’s engineers program the regulatory requirements and controls directly into the proprietary card system. PAG then built user test cases and performed user acceptance testing to ensure all programmed controls were working as intended. PAG also supplied the client with a highly qualified Chief Compliance Officer to help guide their internal efforts, hiring practices, and to represent them in customer facing meetings.

Client Benefits:

  1. PAG was able to successfully build out our client’s complete Compliance Management System allowing them to successfully pass multiple prospective partner audits which led to several lucrative affinity relationships.
  2. PAG’s staff allowed the client to focus us their system engineering efforts while saving time, money, and resources on needed Compliance activities.
  3. PAG assisted the client’s eventual hiring of their internal Compliance Department by interviewing prospective employees and training them to be successful in taking over PAG Compliance duties in time.
  4. Our client was able to successfully pass a regulatory audit after 1 year in practice with zero significant findings or MRA’s.