Blog | Insurance

Auto Loan Portfolio Risk Management

Do you have a single source of truth to manage and mitigate the risk associated with your auto loan asset portfolio? With auto loan delinquencies trending at record highs, leading banks are aspiring to empower their decision makers across origination, risk management and collections to gain a 360-degree view of their auto loan customer portfolio. Such a single source of truth allows decision makers to proactively manage delinquency risk and run effective collection operations to mitigate that risk.

FEBRUARY 26, 2021

Give Yourself an Edge in Managing Delinquencies and Improving Collections Efficiency

Do you have a single source of truth to manage and mitigate the risk associated with your auto loan asset portfolio?

With auto loan delinquencies trending at record highs, leading banks are aspiring to empower their decision makers across origination, risk management and collections to gain a 360-degree view of their auto loan customer portfolio. Such a single source of truth allows decision makers to proactively manage delinquency risk and run effective collection operations to mitigate that risk.

However, most banks face the challenge of siloed sources of intelligence, ranging from ad-hoc excel reports to disjointed business intelligence tools. To add to this, systems teams supporting these business units are often also faced with the daunting task of modernizing their technology infrastructure while continuing to serve their customers day to day.

What You Can Do

Modernize your Business Intelligence Platform for Full Lifecyle Auto Loan Asset Management

Use a combination of business intelligence and delinquency & collection analytics to improve the loan issuance process, better manage delinquency risk, and increase collection efficiency.

The result? Stronger business outcomes while providing improved customer experience.

How We Can Help

Sutherland brings deep banking, auto loan, consumer and commercial lending expertise. We currently work with 3-of-the-top-5 auto loan lending banks in North America, which has allowed us to develop deep familiarity with common enterprise systems such as—but not limited to—SHAW Systems, eCollections, etc. Because of this, we’re able to complete rapid cycles of innovation with our clients.

Modern Data Foundation

Sutherland provides modern data warehouse management, business intelligence (BI) and machine learning services to improve the loan issuance, asset risk management, and collection operations of our clients. To do this, we build a foundational, modern, on-prem or cloud-based data infrastructure that supports descriptive, predictive and prescriptive use cases.

Achieve integrated business intelligence across the full auto loan lifecycle: Get a single source of truth to support business decision makers across loan acquisition, risk management, and collections groups.

While, every lenders needs are somewhat different, we typically deploy the following web- and mobile app-enabled reports for our clients:

  • Delinquency dashboard: showing actionable insights on delinquency trends across days-past-due and flexibility to slice & dice across year/month, collectors, months-on-balance, etc.

  • Inventory of delinquent accounts: revealing with trend of roll rate, cure, churn across a time period, current month distribution, etc.

  • Collector level KPI metrics: providing insights on penetration rate, right party contact (RPC), promises kept, utilization, etc.

  • Recoveries dashboard: showing recoveries by collections agencies, legal recoveries, etc.

  • Repossessions dashboard: revealing insights on repossession assignments, repossessions sold, repossessions redeemed, repossessions closed/cancelled, etc. 

Sample Dashboard

delinquency dashboard

Strategic Predictive Analytics

Building a solid data foundation allows our clients to not just harness business intelligence but move into predictive and prescriptive analytics. We bring deep expertise in collections analytics. Our expertise in leveraging the power of machine learning and harnessing customer payment behavior, both historical & recent pattern changes, allows us to proactively predict delinquency and offer suitable treatment strategies.

  • Pre-Delinquency: Our propensity to pay machine learning models identify payment patterns and suggest best multichannel engagement strategy to minimize delinquency in the first place.

  • Post-Delinquency: Our specialized delinquency models predict roll forward across aging buckets and help in implementation of a bespoke treatment strategy to optimize ROI on effort-based collections.

  • Contact Maximization: Our algorithms predict best channel & phone number for maximizing “right party contact” to enable a higher contact rate & dialer penetration.

  • Collection analytics: Our treatment strategies improve collection efficiency, including cure rate, roll rates, days past due, CEV and EVPH, etc. We use an agent matching algorithm, next best action, and interaction analytics to maximize outcome metrics.

Case Studies

A Top 5 national auto loan bank modernized their business intelligence technology infrastructure to an Azure-based, single-source-of-truth business intelligence system covering loan acquisitions, and streamlining operations across sales, applications, origination and loan servicing. This reduced time-to-insights for decision makers from weekly to daily self-serve.

A Top 10 national bank, specializing in sub-prime lending through credit cards, leveraged predictive analytics to increase their right-party connects and increase collections. This has driven an increased RPC percentage of roughly 47%, and an increased collection rate of roughly 19%.

Reduce Risk. Gain Advantage. Improve Retention.

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