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Risk Monitoring Transformation for a Global Bank ensures financial integrity is safeguarded

Customer Context

The bank performed risk measurement and monitoring on a daily basis, involving multiple teams in the data supply chain to validate the quality of risk data. The process included using Excel-based tools to check period-to-period variances in risk measures across different dimensions such as party, region, and line of business (LOB), followed by attribution. However, this manual and error-prone process relied heavily on in-house subject matter experts (SMEs), leading to long processing times and operational risks.

Key Business Challenge

The key challenges faced by the bank in its risk monitoring process were:

  1. Manual Process: Relying on Excel-based tools and manual data validation led to inefficiencies, errors, and operational risks.
  2. Lack of Automation: The absence of an automated processing logic hindered the efficiency of risk monitoring and compliance with regulatory principles.
  3. Dependency on SMEs: The heavy reliance on SMEs made the process resource-intensive and susceptible to knowledge gaps.
  4. Inadequate Commentary: The process lacked precise commentary explaining risk factors and trade information for threshold breaches in risk measures.

Coforge Solution

To address these challenges and improve risk monitoring efficiency, following solutions were implemented:

  1. Enterprise Application: An enterprise application was developed to automate the risk monitoring process and ensure compliance with BCBS 239 principles. The application also eliminated operational risk associated with manual processing and provided system-generated precise commentary for threshold breaches.
  2. Decision Tree Documentation: The business understanding of the decision tree and steps involved in the variance attribution process based on crit ical dimensions (e.g., products, business lines, risk measures) were thoroughly documented. A Proof of Concept (PoC) was developed for sample scenarios to validate the approach.
  3. Machine Learning Solution: A machine learning (ML) solution was built to classify exposure movements and automate the generation of commentary, enhancing the accuracy and speed of risk monitoring.


The implementation of the risk monitoring transformation resulted in several positive outcomes for the global bank:

  1. Efficient Business Process: The automated processing logic streamlined the risk monitoring process, reducing manual efforts and processing times.
  2. Integration with Risk Workflows: The new enterprise application was fully integrated into the bank's risk workflows, ensuring seamless operations.
  3. Reduced Operational Risk: By removing manual processes and dependency on SMEs, operational risks associated with data validation and risk measurement were mitigated.
  4. Comprehensive Dashboards: Integrated risk monitoring dashboards were developed, providing comprehensive insights for a more effective risk mitigation process.
  5. Improved Processes and Controls: The business processes and controls were re-calibrated to align with the new automated approach, improving overall risk management.
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