The Borrower Engagement Score: Enhancing Pre-Default Collections

In today’s dynamic lending environment, effective management of consumer loan portfolios depends on sophisticated, data-driven tools that can accurately predict borrower behavior. A successful loan servicing approach, along with lease and PPA servicing, demands integrating advanced analytics and borrower engagement metrics for proactive pre-default collections and effective portfolio management. To this end, Launch Servicing developed a Borrower Engagement Score as part of a comprehensive analytics suite, improving collection outcomes before default occurs.

The Borrower Engagement Score, Launch Servicing’s proprietary solution, harnesses machine learning and AI to generate detailed insights into borrower engagement levels. By analyzing a broad array of data including multi-channel communication interactions, reasons for delinquency, and borrower attributes, the score helps collection teams identify which borrowers are most likely to respond to outreach efforts.

This predictive capability enables a move to targeted strategies. By altering outreach strategies for high and low engagement borrowers, servicers can increase cure rates, reduce defaults, and accelerate resolution timelines, ultimately improving cash flow and operational efficiency.

A key advantage of the Borrower Engagement Score is its ability to provide granular insights into the most effective communication tactics for individual borrowers. The model predicts overall engagement and analyzes historical interaction data to identify optimal communication channels such as email, text messaging, or phone calls that resonate best with each borrower.

Furthermore, the score incorporates timing and frequency considerations, guiding outreach during the most responsive times of day and at appropriate contact intervals. This tailored approach enhances borrower experience and engagement, leading to optimum repayment timing and lower operational costs.

Utilizing an advanced scoring model offers multiple advantages, including:

• Faster Resolution and Reduced Cycle Times: Data-driven insights enable quicker identification of high-risk borrowers, reducing time-to-resolution and boosting liquidity.
• Enhanced Risk Management: Early warning signals and borrower behavior patterns improve at risk profiling, enabling better portfolio management and more accurate provisioning.
• Scalability and Consistency: Automated, analytics-driven processes ensure uniform application of collection strategies across portfolios, supporting growth and diversification efforts.
• Regulatory Compliance and Fair Lending: Data-driven models help ensure collection practices are fair and compliant, eliminating reliance on subjective judgment or biases that can inadvertently lead to discriminatory practices.

The effectiveness of the Borrower Engagement Score relies on the continuous collection and analysis of a wide array of data points. These include multi-channel communication interactions such as email, SMS, portal metrics as well as call data. Additionally, borrower attributes such as credit score, loan type, repayment history, and demographic information are integrated to provide a comprehensive profile. Contextual factors like reasons for delinquency and behavioral patterns are also analyzed to gain deeper insights into borrower motivation and responsiveness. Importantly, the score is designed to be dynamic, with borrowers being rescored daily along with continuous iterative updates on the modelling algorithm every quarter. This ongoing recalibration ensures that the engagement assessment accurately reflects current borrower behavior, enabling collection strategies to be promptly adapted. By leveraging machine learning algorithms that incorporate fresh data, the system can identify shifts in engagement levels, predict future responsiveness more accurately, and optimize outreach timing and methods accordingly. This real-time, adaptive scoring process enhances the precision of collection efforts and ensures resources are focused on the optimal accounts.

Partnering with a provider that leverages a Borrower Engagement Score delivers tangible financial benefits including:

• Increased Cure and Recovery Rates: By accurately identifying borrowers fewer delinquent accounts escalate to default.
• Cost Savings: The use of predictive analytics and automation streamlines collection workflows, reducing manual effort and operational overhead. Automated, data-driven contact strategies mean fewer wasted efforts on unlikely responders, allowing staff to concentrate on high-potential accounts.
• Improved Cash Flow: Faster identification of high-engagement borrowers and delinquent accounts enables more immediate and effective intervention. Enhanced borrower engagement means payments are collected sooner, boosting cash inflows.
• Better Portfolio Resilience: Early detection of at-risk accounts allows for proactive risk management. By addressing potential delinquencies before they escalate, servicers and collection partners can mitigate losses and prevent portfolio deterioration.

Enhanced scoring models inform pre-default collection activities more precisely, including:

• Inclusion of Reason for Delinquency (RFD): Using AI-powered language models, the RFD provides contextual insights, enabling more personalized outreach.
• Multi-Channel Interaction Data: Considering email, text, voicemails, portal activity and call attempts offers a comprehensive view of borrower engagement.
• Cure Prediction: Anticipating which delinquent borrowers are likely to cure their accounts helps prioritize efforts and optimize resource allocation.

The Borrower Engagement Score is an adaptable tool to optimize strategies across the entire account lifecycle. By harnessing detailed engagement data, predictive analytics, and communication insights, servicers and collection partners can increase cure and recovery rates, reduce operational costs, and bolster portfolio performance and resilience.