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Innovative Strategies for Collections & Recoveries Using Scorecards and Models
To improve debt collections, businesses need smart strategies that can change based on how borrowers behave and what’s happening in the market. Using scorecards and models, whether traditional or based on machine learning (ML), can help improve collection rates and make the process more efficient. In this article, we’ll look at how data-driven strategies can be tailored to different types of loans, such as credit cards, unsecured loans, mortgages, and small business loans.
Scorecards and predictive models can be customized for different types of loans, offering targeted strategies for better collections:
1. Credit Card
Challenges: Credit cards have high transaction volumes, varied payment patterns, and lots of borrower interactions.
Strategy: Behavioral scorecards group borrowers based on how they use their credit, their payment history, and how much they owe. Predictive models can detect signs of financial trouble early, allowing for quicker action.
Tactics:
Early Alerts: Scorecards can set off alerts when borrowers show signs of risk, like missing payments or maxing out their credit limits.
Incentive-Based Recoveries: ML models can identify opportunities to offer repayment options or
temporary relief, encouraging borrowers to pay.
Outcome: By acting early and offering options, you can collect more and reduce missed payments.
2. Unsecured Loans
Challenges: These loans are riskier because there is no collateral to fall back on if the borrower doesn’t pay.
Strategy: Predictive models look at things like payment history, income stability, and spending habits to better understand the borrower’s ability to pay. Scorecards help sort borrowers early on, while ML models adjust the recovery approach based on new data.
Tactics:
Custom Payment Plans: ML models can predict how likely a borrower is to repay and suggest repayment options that fit their financial situation.
Dynamic Risk Segmentation: Continuously segmenting borrowers ensures that follow-ups are focused and timely.
Outcome: Personalized strategies can lead to better collection rates and more efficient use of resources.
3. Mortgage Loans
Challenges: Mortgages are long-term loans that come with high-value property and more complex borrower situations.
Strategy: Scorecards can help assess risk based on payment history, while ML models factor in the value of the property, the borrower’s financial situation, and market conditions.
Tactics:
Predictive models can suggest the best actions based on property values and market changes.
Collateral-Based Strategies: Predictive models can suggest the best actions based on property values and market changes.
Foreclosure Risk Prediction: ML models can predict the risk of foreclosure, allowing for earlier intervention.
Outcome: Reduced foreclosure rates, better use of resources, and stronger borrower relationships.
4. SME Loans
Challenges: Small businesses often have unpredictable cash flow and performance can vary by industry.
Strategy: Scorecards provide a basic risk overview, while ML models look at business data, cash flow, and industry trends to make more accurate predictions.
Tactics:
Cash Flow-Based Solutions: Predictive models analyze business cash flow and suggest repayment options based on business cycles.
Industry-Specific Adjustments: ML models can adjust strategies depending on industry-specific conditions, making collections more effective.
Outcome: Increased recovery rates and more focused engagement with small business borrowers.
Using scorecard-based and ML-driven strategies can bring several benefits:
Proactive Interventions: Data models help identify risks early, leading to quicker action.
Better Borrower Segmentation: Scorecards provide initial grouping, and ML models adjust this over time, improving how lenders target borrowers.
Personalized Recovery Plans: Both scorecards and ML models allow for repayment plans that are more tailored to each borrower, improving results.
Higher Efficiency: Predictive models help prioritize cases, reducing costs and making collections more efficient.
A provider of unsecured loans used a mix of traditional scorecards and machine learning models, resulting in a 20% improvement in collections. The combination of early-stage borrower segmentation (using scorecards) and real-time analysis of borrower behavior (using ML models) helped target the right borrowers at the right time, improving recovery rates while cutting costs.
By combining traditional scorecards with machine learning models, financial institutions can create effective collections strategies tailored to each type of loan. This helps increase recovery rates, improve efficiency, and ensure better outcomes for both lenders and borrowers.
Want to boost your recovery rates with data-driven strategies? Contact us today to explore how we can help you customize solutions for your lending products.
