AI-Powered Collections: Transforming Banking & Fintech with AWS | EXL PayMentor Case Study

March 22, 2026 Priya Shah – Business Editor Business

Financial institutions are turning to artificial intelligence and machine learning to overhaul collections operations, aiming to personalize outreach and improve efficiency in a sector facing increasing regulatory scrutiny and evolving customer expectations. EXL Service Holdings has launched PayMentor, an AI-powered collections platform built on Amazon Web Services (AWS), designed to address these challenges.

Traditional debt collection methods, characterized by high volumes of phone calls and standardized messaging, are proving less effective as customers exhibit diverse financial situations and communication preferences, according to EXL. This approach leads to rising costs, diminished customer experiences, and potential regulatory issues, particularly from the Consumer Financial Protection Bureau (CFPB) and state regulators.

EXL identified collections as a problem fundamentally rooted in data science and customer engagement – determining the optimal timing, method, and messaging for each individual. Building a system capable of handling millions of daily interactions requires a secure, compliant cloud infrastructure, robust machine learning operations, and real-time tracking of customer engagement.

The company selected AWS for five key reasons: its maturity in financial services compliance, offering certifications like PCI-DSS, SOC 1/2/3, and ISO 27001; its production-ready machine learning infrastructure, including Amazon SageMaker; its omnichannel communication services through integrations with Amazon Simple Email Service, AWS End User Messaging, Amazon Connect, and Amazon Lex; its real-time data processing capabilities with Amazon Kinesis; and its scalability and cost efficiency through serverless architecture using AWS Lambda.

PayMentor’s architecture prioritizes security through multi-account isolation, encryption using AWS Key Management Service, and zero-trust networking. It also ensures regulatory compliance with audit trails via AWS CloudTrail and data lineage tracking. The platform is designed for operational resilience with multi-Availability Zone deployment on AWS Global Infrastructure, scalability through a serverless-first approach, and cost optimization through pay-per-use pricing and intelligent data tiering in Amazon S3.

The platform employs a three-pronged data ingestion strategy. Secure File Transfer Protocol (SFTP) is used for batch data and model training, utilizing end-to-end encryption and AWS Glue for data validation and loading. A REST API, leveraging AWS Lambda and Amazon API Gateway, handles real-time data ingestion with authentication provided by Amazon Cognito and protection from Distributed Denial of Service (DDoS) attacks via AWS WAF. Third-party integrations, such as credit bureaus and payment processors, utilize Amazon AppFlow for secure, managed connections.

To maintain security, EXL separated its machine learning development and production environments into distinct AWS accounts. This isolation protects sensitive customer data during experimentation, ensures compliance through clear audit separation, minimizes risk to the production system, and enforces governance through artifact-based deployment. PayMentor utilizes two primary machine learning models: a Customer Ranking Model, predicting the probability of payment, and a Channel Preference Model, identifying the most effective communication channel for each customer.

Daily scoring and strategy generation are orchestrated by AWS Step Functions, triggering a workflow that fetches customer data from Amazon S3, invokes the SageMaker endpoint for customer rankings, saves rankings to Amazon RDS, generates communication plans, and sends completion notifications via Amazon Simple Notification Service. Step Functions was chosen for its visual workflows, built-in retry logic, parallel execution capabilities, and state persistence for auditability.

Omnichannel execution is managed through AWS Lambda, integrating with Amazon SES for email, AWS End User Messaging for SMS, third-party services for WhatsApp, Amazon Connect with Amazon Polly and Amazon Lex for voice interactions, and API Gateway with Lambda for self-service portals. AWS Translate enables multi-language support for broader customer reach.

Real-time engagement tracking is achieved through an Amazon Kinesis streaming architecture. Customer interactions from SMS and email are processed by Amazon Data Firehose, sending data to both Amazon S3 for archival and Amazon Kinesis Data Streams for real-time processing. This allows for immediate strategy adjustments and channel optimization.

Analytics and reporting are provided through Amazon QuickSight dashboards powered by Amazon RDS and Amazon S3. Operational dashboards offer real-time metrics on message delivery, engagement rates, and cost per contact. Strategic performance dashboards provide historical analysis of cure rates, channel effectiveness, and regulatory compliance.

EXL emphasizes that modern collections require an intelligence-driven approach, focusing on understanding individual customer needs and preferences. AWS provides the infrastructure and services to support this transformation at scale, offering a secure, compliant, and cost-effective solution for financial institutions.

AWS highlights its role in transforming financial services, offering resources for institutions looking to modernize collections and leverage AI and ML services. Further information is available on AWS’s financial services solutions page and on its AI/ML services for regulated industries.