Corlytics Launched Banking Risk Quantification Engine
The new platform automates risk and control self-assessments to cut costs for global banking institutions.
Updated on Sept. 23, 2026 in Financial Services
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Corlytics has released an Emerging Risk Quantification engine that translates banking risks into precise dollar figures. The system is designed to automate up to 70% of risk and control self-assessment (RCSA) processes for financial institutions.
Why it matters
Regulators are increasingly mandating that firms move away from subjective risk ratings, requiring more rigorous and quantifiable methods for compliance. This shift creates a need for tools that can ground risk assessment in external enforcement data rather than internal intuition.
Global banks spend between $10 million and $50 million annually on RCSA processes, which the new Corlytics engine aims to automate by 50% to 70%. The model integrates 12 years of enforcement data containing 150 individual points per event to calculate expected losses.
The players
Corlytics
A provider of regulatory technology and risk intelligence software for the global financial sector.
The details
The engine replaces subjective red, amber, and green risk ratings with standardized dollar-value outputs, including expected annual loss and a 95th-percentile tail figure. By integrating regulatory obligations, policy content, and historical enforcement records, the tool allows banks to benchmark their risks against a wider population of peer-weighted industry data. AI-driven traceability ensures that each risk figure is grounded in verifiable data points rather than manual estimates.
Timeline
September 23, 2026: Corlytics launched the Emerging Risk Quantification engine.
Past 12 years: The company captured the enforcement data used to build the model.
Past decade: Various underlying capabilities for the engine were developed.
Market Landscape
This development follows the trend of moving toward the Basel III operational risk framework by replacing subjective risk ratings with data-driven models. It marks a departure from traditional, manual RCSA methods that have long struggled to meet increasingly strict regulatory requirements.
Financial institutions should evaluate whether their current manual risk assessments can be benchmarked against the 95th-percentile tail figures provided by this type of data-driven model. Operators should assess if their compliance teams are equipped to transition from internal rating systems to automated, externalized industry benchmarks.
The takeaway
The move toward quantitative risk assessment signals an end to reliance on subjective internal scoring methods. Operators should begin tracking how much of their firm's RCSA processes rely on manual input versus how much could be replaced by traceable, AI-driven enforcement data.
Further reading
For more on evolving compliance standards, visit Financial Services.
Source note: This article includes information reported by FinTech Global.
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