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Revolutionizing Collections: How Machine Learning and RPA Are Slashing 90+ Day Delinquencies

Home > Banking > Collections Optimization > Revolutionizing Collections: How Machine Learning and RPA Are Slashing 90+ Day Delinquencies

July 23, 2024

Banking

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Collections Optimization

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GOBLIN

By David LaRoche

I’m getting calls these days from consumer lenders concerned about surging delinquencies and struggles with traditional collection methods. The conversation normally pivots we can help them leverage Machine Learning Models (ML) and Robotic Process Automation (RPA) to tackle concerns over 90+ day delinquencies.

For context, Machine Learning Models spot patterns in vast amounts of data, while RPA handles repetitive tasks with precision and speed. RPA is being used for tasks such as inbound collections calls, while the benefit of Machine Learning Models is that they’re algorithm-based and learn over time but let you define the response.

Together, these technologies are redefining how financial institutions approach debt recovery.

The Challenge: A Perfect Storm of Delinquencies

As I write this, big banks are reporting 2Q 2024 earnings.

JPMorgan Chase reported 30-day delinquency rates on card loans at just under 2.1%, higher than the 1.7% seen in the same period last year but a bit lower than the 2.2% seen in the 1Q. Charge-offs stood at 3.5% in the 2Q, up from 3.3% in the 1Q and 2.4% fa year ago, And Chase’s 90-day delinquency rate was 1.1%, up from 0.8% a year ago but down a bit from the first-quarter’s 1.2%.

Wells Fargo’s 30+ day delinquency rate for the card segment was 2.7%, compared with 2.3% a year ago and down from 2.9% in the first quarter. The bank attributes the improvement to credit tightening.

Citigroup saw similar trends, although its delinquencies were significantly lower than Wells. The bank said lower FICO-band customers have sharply reduced their payment rates and are borrowing more as they are more acutely impacted by high inflation and interest rates. For additional context, 86% of Citi’s card loans are to customers with 660+ FICO scores.

On top of that. PYMNTS Intelligence reports that 65% of the U.S. population lives paycheck to paycheck, the highest share it has seen in two years. As consumers increasingly lean on credit to bridge financial gaps, traditional collection strategies are struggling to keep pace.

We all know the larger banks have the advantage of proprietary data and analytic platforms that help them optimize their collections efforts and address the volatility within FICO bands. Smaller financial institutions and fintechs rarely have those tools.

What’s you need is an approach that anticipates risks, adapts to individual circumstances, and navigates the ever-changing regulatory landscape. Enter GOBLIN, Predictive Analytics Group’s enterprise data platform.

The Game-Changers: Machine Learning and RPA in Collections

By leveraging vast amounts of data and advanced algorithms, ML and RPA are consistently outperforming traditional methods in reducing 90+ day delinquencies. Here’s how:

  1. Early Warning Systems: GOBLIN’s ML models analyze hundreds of variables to identify at-risk accounts long before they hit the critical 90-day mark. It’s like having a financial weather forecast, offering suggestions for proactive intervention.
  2. Hyper-Personalized Engagement: We’re leaving the days of one-size-fits-all collection notices. ML tailors communication strategies to individual borrowers, considering factors like preferred contact methods and optimal timing. RPA then executes these strategies with precision, significantly improving engagement rates.
  3. Adaptive Repayment Strategies: By continually analyzing borrower behavior and financial situations, systems like GOBLIN can create and adjust repayment plans in real time, increasing success rates.
  4. Regulatory Compliance Assurance: RPA automates compliance checks and adapts to regulatory changes, helping institutions navigate the compliance minefield while maintaining effective collection practices.
  5. Intelligent Prioritization: ML can prioritize collection efforts to focus on accounts with the highest probability of recovery. This targeted approach not only improves recovery rates but also allows human agents to focus on complex cases that truly require their expertise.

The Future of Collections

We’re not far from systems that engage in natural language conversations with borrowers, negotiating payment plans in real-time with empathy and understanding. Predictive economic modeling powered by ML will allow institutions to anticipate and prepare for market shifts that could impact delinquency rates.

Moreover, the integration of these technologies across all channels will create seamless, omnichannel collection strategies that meet borrowers where they are, whether through a mobile app, social media, or traditional communication methods.

Implementing Advanced Analytics: A Strategic Imperative

Embracing these technologies is a leadership decision. Successful implementation requires more than a technology investment. It demands a shift in organizational mindset, a commitment to data quality and integration, and a willingness to continuously learn and adapt.

Consider the experience of a major U.S. credit card issuer we recently worked with. By implementing new strategies powered by ML and RPA, they saw dramatic improvements across key metrics, including a significant reduction in 90+ day delinquencies and operational costs. This success story underscores the transformative potential of advanced analytics in collections.

The Road Ahead

The path forward is clear: Machine Learning and RPA offer a debt-recovery strategy that’s more proactive, personalized, and ultimately more effective.

For institutions ready to embrace this future, the rewards are substantial: reduced delinquencies, improved customer relationships, and a competitive edge in an increasingly challenging market. The question is no longer whether to adopt these technologies in collections, but how quickly you can harness their power to transform your operations.

Let’s set up a no-cost, no-obligation call to see how PAG can help you leverage the power of Machine Learning Models and Robotic Process Automation with our GOBLIN data and analytics platform.

David LaRoche is managing partner of U.S. Operations for Predictive Analytics Group. You can follow us on LinkedIn here.

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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."

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)