Case Study

Enhancing Payment Investigations with AI-Driven Automation and NLP

Industry

Banking & Financial Services

Location

Global

Our Contributions

Payment Operations Transformation, AI Automation, NLP-Based Data Extraction

Technologies

NER, Machine Learning (SVC), Kofax Total Agility

Coforge partnered with a leading global financial group operating across 50+ countries to modernize its payment investigation process. The existing process relied heavily on manual interpretation of unstructured SWIFT messages, leading to inefficiencies, errors, and high operational costs.

By implementing an AI-powered solution leveraging natural language processing and machine learning, Coforge automated the extraction and processing of investigation data. The solution enabled faster, more accurate handling of payment investigations, improving throughput and enhancing responsiveness for customer support teams.

Transformation Timeline

Phase 1:

Process assessment and use-case definition

Phase 2:

AI model development and training

Phase 3:

Integration with KTA workflow and validation layer

Phase 4:

Deployment, scaling, and optimization

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The Challenge

The bank handled a high volume of payment investigation messages daily, primarily in unstructured, free-format SWIFT messages. Operations teams were required to manually read, interpret, and extract relevant information before updating payment workflows, resulting in significant time and effort.

The lack of standardization, incomplete data, and presence of both structured and unstructured information increased the complexity of the process. Additionally, legacy systems and manual dependencies led to inconsistencies, reduced accuracy, and slower turnaround times.

The organization needed a scalable, automated solution to improve accuracy, reduce manual effort, and bring consistency to the payment investigation process while ensuring seamless integration with existing systems.

Our Approach / Solution

AI-Based Data Extraction Engine

Developed machine learning models using Named Entity Recognition (NER) and multi-class classification to extract key information from unstructured SWIFT message types (MT195, MT196, MT199, MT295, MT296, MT299).

Intelligent Classification & Context Analysis

Leveraged SVC-based classification models and keyword analysis to accurately categorize message intent and extract relevant fields.

Seamless Workflow Integration

Integrated the AI solution with the existing Kofax Total Agility (KTA) workflow, enabling automated data extraction with a human-in-the-loop validation mechanism.

Scalable Open-Source Architecture

Designed a flexible, open-source-based solution that is cost-effective, scalable, and easily adaptable to new use cases and increasing transaction volumes.

End-to-End Automation Enablement

Automated the payment investigation workflow to reduce manual intervention and improve processing speed and consistency.

Partner / Technology Ecosystem

• Kofax Total Agility (KTA)
• Machine Learning & NLP Models (NER, SVC)
• Open-Source AI Frameworks

Impact to Date

>90%

Data Extraction Accuracy

-80%

Reduction in Payment Investigation Effort

24/7

Automated Processing Availability

Zero

Manual Effort in Data Validation (for majority cases)