Background

Debt collections are traditionally manual, time-consuming and inefficient especially when managing thousands of overdue accounts across banking and telecom sectors. To address this, we implanted an ML and an AI-powered collections platform using our Clientrator that automates the end-to-end process from data ingestion to customer outreach significantly improving both recovery rates and operational efficiency.

 

Objectives and Approach

Model 1: Predict Payment Likelihood

• Objective: Identify customers most likely to repay.
• Approach:
  • LightGBM algorithm trained with 24 structured financial, behavioral, and demographic features.
  • Enhanced with 384-dimensional sentence embeddings from remarks data processed via LLMs.
• Outcome: Probability score attached to each account to focus resources where repayment likelihood is highest.

 

Model 2: Determine Best Communication Mode

• Objective: Maximize customer engagement by selecting the right channel (Call, email).
• Approach:
  • Recovery model built on demographic, balance, and payment behavior data.
  • Integrated LLM-generated embeddings (reduced via PCA) as additional signals.
  • LightGBM classification for channel success prediction.
• Outcome: Automated assignment of optimal communication mode for each debtor, supported by universal fallback campaigns.

 

Model 3: Optimize Call Timing

• Objective: Improve connect rates and conversion by finding the best time to contact.
• Approach:
  • K-Means clustering on call history, payment response patterns, and demographics.
  • Groups debtors into behavioral clusters with distinct optimal time windows.
• Outcome: Suggest appropriate time buckets that aligns outreach time with debtor’s likely availability.

 

Model 4: Email Template Recommendation

• Objective: Generate and deliver personalized, context-aware communication at scale.
• Approach:
  • Dynamic recommendation of email templates based on customer profile, payment stage, and prior interactions.
  • Retrieval-Augmented Generation (RAG) using:

• Vector embeddings of profile and emails
• Matching of profile/transactions to templates
• Filling template using LLM (Gemini Flash 2.0)

• Outcome: Customized subject and mail body are automatically generated  and the client able to send emails automatically to their customers

 

Data and Feature Engineering

• Inputs: Financial information (balances, dues, previous repayments), call details, payment behavior logs, status checks, demographic data, and interaction remarks.
• EDA & Feature Processing:
  • Duplicate handling, feature engineering, and standardization.
  • Text processing on remarks with embeddings.
  • Dimensionality reduction via PCA for embedding-heavy datasets.

 

Deployment and Integration

• Platform: Models deployed via Clientrator proprietary APIs hosted on AWS
• Integration:
  • Data ingestion from client CRM systems hosted on AWS via S3 buckets
  • Low-latency inference and communication execution through AWS-native services and LLM APIs
• Scalability: Supports bank, telecom, and third-party collection portfolios with API-based orchestration.

 

Conclusion

The business can now prioritize high-likelihood accounts and optimal channels. A personalized engagement AI-driven communication increases debtor responsiveness. The automated repetitive tasks like follow-ups, freeing agents for high-value accounts. It also ensures consistent, professional and compliant messaging across all channels.

 

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