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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 Data Warehousing Underwriting Strategies

Fintech kicks off card program after PAG merges four data streams into a relational database

fintech credit card relational database

Challenge:

One of PAG’s clients – a medium-sized fintech with a long history of customer loyalty – wanted to enter the U.S. card market to leverage its strong customer relationships. The client also had good data history on their customers through its non-card products, but it was in multiple legacy systems and the data was not designed to be migrated from one system to another. The Client also did not have an internal data warehouse to store all of its data nor did it have internal Risk expertise to merge data and build the strategies and reporting packages it needed to monitor a credit card portfolio.

PAG Solution:

PAG was asked to evaluate the fintech’s data streams and infrastructure and determine how it could take advantage of its rich data history.  PAG identified four major system feeds that contained valuable data and had customer-identifying data to unify that data.  PAG used its GOBLIN enterprise data platform to automate the four data streams into one relational database.

PAG merged the data sets into a series of unique tables that are now available each day for querying.  PAG then built an initial underwriting strategy, merging the Client’s existing relationship data with PAG’s 30M-record bureau data set. That allowed us to build detailed performance and profitability models. After getting approval on the underwriting strategy from the Client and its sponsor bank, PAG built a robust set of monitoring reports and automated their delivery through GOBLIN so the Client now starts each day reviewing the performance of its new card portfolio and making decisions to run its business more effectively.

Client Benefits:
  1. PAG merged all four data streams into a relational database that is ready for querying each day by the Client or PAG by 4 a.m.
  2. PAG helped architect the data environment on the new cards being booked so their data from a third-party provider is also streamed into GOBLIN and available for querying and merging back to the original relational data streams.
  3. The underwriting model PAG built passed a regulatory audit and has exceeded Year 1 growth targets by 5% while coming in 10% lower on Year 1 loss targets.
  4. PAG supported interviews of Risk personnel for the Client, which now has a small team that is creating monthly dashboards and executive presentations for senior management.
  5. PAG was recently rehired to build new Credit Line Increase, Authorizations, and Card Reissue strategies in conjunction with the Client’s new Risk Team.
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
Banking Case Studies Underwriting Strategies

PAG helps Top 10 card issuer rebuild its outdated customer score model

credit card data analytics

Company: Top 10 Credit Card Issuer – US Co-Brand & Branded

Challenge:
Company has greater than 30 co-brand partner portfolios and several branded portfolios. Over the last six years, their underwriting strategies were using an older version of FICO and a custom score built in 2013. They have a very light staff of risk-management analysts to revise a custom update to their portfolios. They were looking for support to rebuild their custom score to the next generation and revamp several of the larger portfolio underwriting strategies.

PAG Solution:
PAG was engaged in a two-stage approach, which included developing a new custom score for underwriting and then utilizing that score, once approved, to develop custom underwriting strategies for several of their larger co-brand portfolios and two of the branded portfolios. PAG used its internal capabilities and systems to work with the bank to gather the needed data requirements from both internal bank data and external data were applicable. PAG developed a new custom underwriting score with six unique segments, which overall increased the KS from 34 to 48 for the overall scorecard when rolled up. From there, the score was quickly approved by the client, and coding into the system began immediately. PAG then developed the underwriting strategies using the new custom score and the newest version of FICO.

Client Benefits:

  1. PAG was able to not only deliver a new custom underwriting score, but also worked with the Decision Science team to upgrade their internal model documentation process, and provided them a new model governance template for ongoing model management (all OCC & FDIC aligned best practices)
  2. PAG was to improve the approval rate across the portfolios that were managed by PAG an average 22%
  3. Targeted loss rates were decreased by 6% overall, while overall profitability increased by an average of 26% across all portfolios touched.
  4. PAG’s solution came in slightly over budget, due to the mid-project request for model governance and procedures, however, came in below the overall expectations of the client