Fintech startup improved its debt collection efficiency with Altysys’ ML-powered predictive solution
About the company
A fintech startup.
Business Need
Fintech lenders face the critical challenge of ensuring timely loan repayments to maintain financial stability and minimize losses. However, predicting whether customers will pay their outstanding bills or debts within the designated timeframe remains challenging. Traditional collection strategies rely on manual processes and static credit risk assessments, leading to inefficiencies and increased operational costs.
To address these challenges, our client sought Altysys’ team to develop an advanced machine learning-driven solution to enhance its loan repayment prediction capabilities. The primary objectives were to:
- Accurately forecast the likelihood of a customer making a payment within 30 days
- Streamline collection workflows while reducing manual interventions
- Optimize debt recovery rates and operational effectiveness
Solution
The Altysys team conducted a comprehensive needs assessment, crafted a solution roadmap, and executed these key initiatives:
- Developed a classification model leveraging historical payment data, account status, and communication records (texts, calls, payments) to predict loan repayment likelihood.
- Integrated multiple data sources, including accounts, text logs, call logs, and payment history, to train an ML model with a recall of 40% and precision of 73%.
- Planned future enhancements by incorporating NLP techniques to analyze call transcripts and text interactions, utilizing large language models (LLMs) to improve prediction accuracy and model performance.
Tech Stack
Python, SQL, Mlflow, Databricks Runtime ML
Business Impact
- Higher collection efficiency, minimizing manual effort
- Increased payment success rate, covering 69% of the debt amount
- Boosted billing team productivity