Banking Analytics

Agentic AI in banking transforms complex workflows from AML to underwriting. Multi-agent architecture and safety guardrails required for compliance. If you’ve spent any time working at the intersection of finance and technology over the last decade, you’ve witnessed a predictable cycle. Every few years, a new acronym promises to finally kill off the mountain of […]

Everything you need to know about SR 11-7 vs SR 26-2, key differences, practical implications, and how to future-proof model risk management. The world of model risk management (MRM) is undergoing a critical transformation. The transition from SR 11-7 to SR 26-2 is not just a regulatory update—it represents a paradigm shift in how financial

Agentic AI in Credit Risk is a shift to a system that can actively investigate, synthesize, and manage risk workflows alongside human underwriters. The Agentic AI in Credit Risk: Reality For decades, the evolution of credit risk management has been a story of better mathematics. We moved from rudimentary character-based lending to statistical scorecards, and

Building a Basel model is one of the most important and regulated activities in banking. Unlike generic predictive models, Basel models directly influence regulatory capital, portfolio strategy, pricing, and risk appetite. Regulators expect these models to be conceptually sound, statistically robust, well-governed, and auditable. The Journey of Building a Basel Model Building a Basel-compliant credit

The implementation of IFRS 9 introduced a fundamental shift in the measurement of credit risk across financial institutions. Unlike the incurred-loss framework under IAS 39, IFRS 9 requires the estimation of Expected Credit Losses (ECL) using a forward-looking, probability-weighted approach that incorporates both current conditions and reasonable future forecasts. This change has heightened the importance

Threat modelling in banking industry is one of the most important measure to protect from cybercriminals. With billions of digital transactions happening every day and an ever-growing attack surface—from mobile banking apps to cloud-based services—traditional security models are struggling to keep pace. This is where Artificial Intelligence (AI) is rapidly transforming the landscape, particularly in

Generative AI in credit risk modelling is emerging as the next frontier. With its ability to create synthetic data, simulate scenarios, and enhance model interpretability, it offers exciting opportunities—while also raising regulatory and ethical challenges. From scorecards to sophisticated probability of default (PD), loss given default (LGD), and exposure at default (EAD) models, financial institutions

Legacy models in banking generally refers to older or pre-existing models that were originally built for credit risk management, regulatory capital (Basel II/III), or internal risk purposes, and which were later adapted for Expected Credit Loss (ECL) estimation under IFRS9. In the banking world of financial risk management, regulatory frameworks like IFRS9 (International Financial Reporting