Case Study

Transforming Risk Exposure Monitoring with AI-Driven Automation

Industry

Banking

Location

Global

Our Contributions

Risk Analytics, AI-Driven Monitoring, Workflow Automation

Technologies

Machine Learning, Real-Time Analytics, Risk Dashboards

Coforge partnered with a global bank to modernize its exposure monitoring capabilities across Credit and Market Risk. The objective was to eliminate fragmented, manual processes and enable a scalable, real-time risk monitoring framework.

By leveraging AI and machine learning, Coforge implemented an intelligent exposure monitoring solution that automated classification, enhanced transparency, and improved the speed and accuracy of risk insights. The transformation enabled proactive risk management, reduced operational dependencies, and strengthened enterprise-wide risk control.

Transformation Timeline

Phase 1

Risk monitoring assessment and data consolidation strategy

Phase 2

ML model development and exposure classification design

Phase 3

Dashboard enablement and workflow automation

Phase 4

Real-time monitoring rollout and optimization

Drag

The Challenge

The bank’s exposure monitoring processes were fragmented across multiple teams and data sources, resulting in delayed and inconsistent risk insights. Siloed data pipelines made it difficult to achieve a unified view of exposure, limiting transparency and scalability.

Heavy reliance on manual analysis increased operational risk and introduced control gaps, while manual handoffs slowed down decision-making. Additionally, the lack of automated attribution mechanisms made it challenging to accurately identify the drivers behind exposure movements.

The organization required a scalable, automated solution to unify data, improve accuracy, and enable real-time risk monitoring across Credit and Market Risk functions.

Our Approach / Solution

AI-Driven Exposure Classification

Implemented machine learning models to automatically classify exposure movements and generate contextual commentary for faster analysis.

Unified Risk Monitoring Framework

Developed integrated, real-time dashboards providing a consolidated view of Credit and Market Risk exposures across the enterprise.

End-to-End Workflow Automation

Reengineered controls and workflows to eliminate manual dependencies and enable seamless, automated exposure monitoring.

Intelligent Attribution Models

Combined rule-based and machine learning approaches to accurately attribute exposure movements to underlying trades and risk drivers.

Scalable Real-Time Monitoring

Enabled near real-time limit monitoring at scale, supporting proactive risk control for both Risk and Front Office teams.

Partner / Technology Ecosystem

Machine Learning & Analytics Platforms
Real-Time Data Processing Frameworks
Risk Monitoring & Visualization Tools

Impact to Date

-60%

Reduction in Manual Effort

2–3×

Faster Risk Insight Generation

+35%

Improvement in Attribution Accuracy

-50%

Reduction in Operational Risk