Author name: Tanuka

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

Model risk management refers to the management of risks that occur from the potential adverse consequences of decisions based on incorrect or misused models. In the modern banking ecosystem, models have become foundational to virtually every critical function. Whether it’s approving a mortgage application, pricing a derivative product, or calculating capital reserves, banks rely on

Mortgages play a central role in financial systems. In Probability of Default or PD model for mortgages are evaluated for likelihood of borrower to fail

Sentiment Analysis in Banking is taking a pivotal role to improve customer experience in the face of increasing competition and digital transformation.