Risk Management & Credit Risk Blog | RMA

How the World’s Biggest Lenders Use Machine Learning

See machine learning in action and discover how lenders forecast creditworthiness and improve AML practices.

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How the World’s Biggest Lenders Use Machine Learning

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Community Banks and Fintech: A Complex Relationship

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Key Takeaways from GCOR XV

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Community Banks and Fintech: A Complex Relationship

Community banks have long prided themselves on their high-touch, super-local service to their communities. Now, along comes a new generation of consumers with a preference for low-touch, technology-enabled banking. What is a community bank to do?

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How the World’s Biggest Lenders Use Machine Learning

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Community Banks and Fintech: A Complex Relationship

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Key Takeaways from GCOR XV

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Key Takeaways from GCOR XV

Below are the highlights from GCOR XV sessions focusing on three main themes – megatrends that are shaping the future of operational risk management, organizational culture, and the complexity of the world that we live in.

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How the World’s Biggest Lenders Use Machine Learning

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Community Banks and Fintech: A Complex Relationship

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Key Takeaways from GCOR XV

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RMA’s Plug and Play integrated Dual Risk Rating scorecards with Finastra Fusion CreditQuest®

See how top banks can integrate their internal risk rating methodology seamlessly within their end-to-end commercial loan platforms. RMA dual risk rating product can be deployed on its web-hosted platform or can be fully integrated into a bank’s existing LOS, such as CreditQuest®.

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How the World’s Biggest Lenders Use Machine Learning

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Community Banks and Fintech: A Complex Relationship

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Key Takeaways from GCOR XV

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Model Performance Monitoring Adjustments: A Framework to Respond to Covid-19

The COVID-19 pandemic has caused unprecedented model failure and forecasting concerns across the financial industry. The widespread model deterioration cannot not be easily resolved by immediate redevelopment and recalibration effort, largely due to the unavailability of the outcome data before economic recovery reaches a stable state. Banks typically resort to overlays and management adjustments to correct large forecasting variances. This attempt could introduce additional noise and cause model estimates to become more volatile if shocks to the model cannot be self-absorbed in time or the overlay is poorly formed. In this article, I present a framework to evaluate overlay necessity using existing performance monitoring results and other factors, and identify opportunities for performance monitoring adjustments as transitory means for response to model deterioration before more permanent solutions can be implemented.

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How the World’s Biggest Lenders Use Machine Learning

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Community Banks and Fintech: A Complex Relationship

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Key Takeaways from GCOR XV

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