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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