Banking Analytics

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.

Cox Proportional Hazards Model came into effect in 1972, when David Cox developed the proportional hazards model which derives robust estimates of covariate effects using proportional hazards assumption.

The Kaplan-Meier model, a non-parametric estimator, is one of the most widely used techniques in survival analysis, and it has found several applications in banking. From assessing loan defaults to predicting customer churn, the Kaplan-Meier estimator provides valuable insights that aid in effective decision-making. In the banking domain, understanding and predicting customer behavior, financial risks,

Generative AI in banking has spread in wings in the last two years as never before. It has become a game-changer not only in banking, but also in every other industry. Generative AI in banking has far-reaching impacts. While traditional machine learning and artificial intelligence have turned out to be efficient across various aspects of