Some private lenders are using real-time and alternative data to help inform a potential borrower's risk profile.​ ...

This article examines the work of data scientist Sai Prashanth Pathi in AI for credit risk, focusing on explainable machine learning in regulated finance, governance alignment, fairness, compliance, ...

The financial landscape of 2026 is defined by a paradox: machine learning systems are now more powerful and autonomous than ever, yet they operate under the strictest regulatory scrutiny in history.

A visionary business analyst and product owner with 18 years of proven track record in driving industry-transforming financial solutions in the UK, Olubunmi Martins-Afolabi possesses exceptional ...

India's formal credit system has a structural blind spot. Approximately 500 million adults — nearly the entire working ...

In May 2026, the financial industry is grappling with a paradox: the very machine learning tools driving efficiency are becoming sources of systemic risk. With the Treasury releasing new AI risk ...

Discover how predictive analytics uses data-driven models like decision trees and neural networks to forecast outcomes and ...

The key function of banks in the real world is endogenously creating (inside) money. But they do so facing solvency, liquidity and maturity risks and being subject to regulatory and demand constraints ...