credit underwriting
Banking, 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. 

Managing Partner of U.S. Operations

Mr. LaRoche is a resourceful, results-oriented Executive with over 25 years of financial services experience; emphasizing collections risk management, dialer operations, MIS and reporting analytics, acquisition strategies, loss forecasting, credit policy, account management strategies, portfolio conversions, due diligence, and collections operations management. He also has over 15 years of direct risk management experience, with 3 years of collections line management experience possessing excellent analytical skills and the ability to manage diverse groups in strategies, modeling, collections, dialer operations, loss forecasting/loan loss reserve modeling, financial analysis, operations and loss avoidance.

David started his career in 1997 as a customer service representative for Travelers Bank. Since then, he has held the following senior positions:

  • Director, US Operations for Bridgeforce Consulting
  • Sr. Director and Call Center Leader for American Express
  • SVP. Collections Strategy and IT leader for Washington Mutual

Chief Risk Officer

Dale Hoops has over 25 years of experience within the financial services industry, with a focus on Risk Management, Collections, Fraud, Account Management Strategies, Loss Forecasting, stress testing, and economic analysis.

Dale started her financial services career in 1996 as a part time customer service representative and teller in a small financial center while attending the University of Richmond. Her career has included senior roles at Bank of America, Citi, and MBNA America. She has experience with multiple retail products, including consumer and commercial cards, private label and co-brand, deposits, vehicle lending, mortgage, and home equity. Her key strengths have been identifying opportunities for improvement through business analysis, strategy development, and risk governance.

In addition to her professional career, Dale has extensive leadership experience with non-profits with event planning, policy, budget, and audit management. She is a member of the Board of Directors for the Girl Scouts of the Chesapeake Bay, which serves 8,000 girls in Delaware, Maryland, and Virginia. She is the former President, Vice President, and Treasurer at a local Parent-Teacher Association, former community pillar chair for Bank of America’s LEAD for Women Delaware network, and served on the leadership team for the Field of Dreams Relay for Life event to raise funds for the American Cancer Society.

Chief Data and Analytics Officer

Mr. Ridgeway has over 30 years of experience within the financial services industry, including Risk Management, Finance, Project Management, Compliance, MIS, IT & Operations. He has held senior roles at several of the top 5 Banks, including MBNA, Wells Fargo & Citibank. Dee has expertise in Risk oversight and a wealth of knowledge in the regulatory footprint (CFPB / OCC / FRB) in financial services. He has hands-on knowledge in the strategy world with numerous credit products including: credit cards, auto lending, mortgage and home equity, and unsecured lending. Dee is a co-founder of Predictive Analytics Group, worked as a Senior Consultant for Hoops Consulting, LLC., and owned & operated Mayflower Analytics LLC.

From a Risk Management perspective, Dee has experience in portfolio management in credit underwriting and loss mitigation during several growth cycles and economic contraction periods. He understands the needs and partners well with operational risk, modeling, and loss forecasting risk functions.

Dee is a SME on risk data strategy (data architecture, data management, and systems integration) and often creates a "passable bridge" between Risk and IT that translates business needs into executable business plans.

From an MIS, reporting, and portfolio analytics perspective, Dee has a proven track record of designing portfolio reporting that meets executive and end user needs that often have been labeled the "gold standard."

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CEO and Chief Strategy Officer

Mr. Hoops has over 25 years of experience within the financial services industry, including Credit Collections & Fraud Risk Management, Business Operations, Control &Compliance, Strategic Planning, Forecasting, and Marketing Analytics. He has served as a Chief Risk Officer for Barclaycard US Partnerships, a Global Scoring Head at Citibank, and a Site President for Wells Fargo Financial. Steve is a co-founder of Predictive Analytics Group and has owned & operated Hoops Consulting, LLC for the past 4 years.

Steve started his financial services career in 1993 as a part-time telemarketer while attending the University of Delaware for his Business Administration degree. Mr. Hoops has spent his 25-plus years within the industry building best-in-class operations with each company he has supported. His career has been highlighted by leading several large functions for several Tier 1 and Tier 2International Banks, including:

  • Credit Policy (CRO for $20B co-branded portfolio, Barclaycard US partnership)
  • Credit Policy (SVP for $28B retail Co-brand & Private Label portfolio, Citibank)
  • Loss Forecasting / Loan Loss Reserves ($30B Consumer portfolio, Citi-Financial)
  • Collections Risk Management ($70B Co-brand & Private label portfolios, Citibank)
  • Modeling ($30B Consumer Loan & sub-prime Mortgage portfolio, Citi-Financial)
  • Collection Operations (Head of 410 person operations center, $17B Auto, Personal Loan & Mortgage portfolio, Wells Fargo)
  • Credit Analytics (MBNA/Bank of America, Wells Fargo, Citibank)