This study analyses the evolving landscape of digital risk management within the banking sector. Based on responses from a diverse group of Italian banks, the research sheds light on key trends and strategic priorities in risk management. By soliciting insights from professionals and...

During the last decade, the increase in computational capacity, the consolidation of new data processing methodologies and the availability of access to new information concerning both individuals and organizations, aided by the widespread internet usage, has increased the development and...

This report presents the findings of a global survey on AI in Financial Services jointly conducted by the Cambridge Centre for Alternative Finance (CCAF) at the University of Cambridge Judge Business School and the World Economic Forum in Q2-Q3 2019. Representing one of the largest global...

This paper studies the interplay of machine learning and sampling scheme in an empirical analysis of money laundering detection algorithms. Using actual transaction data provided by a U.S. financial institution, we study five major machine learning algorithms including Bayes logistic regression,...

This paper discusses the benefits and issues associated with big data and artificial intelligence (AI) for financial inclusion. The benefits of artificial intelligence and big data for financial inclusion are: improved efficiency and risk management for financial services providers; the...

This study investigates the need for credit supervision as conducted by on-site banking supervisors. It builds on a real bank on-site credit examination to compare the performance of a hypothetical self-supervision approach, in which banks themselves assess their loan portfolios without external...

Big data and advanced algorithms using machine learning (ML) have changed the way financial products and services are produced, delivered, and consumed, and they have a potential to change the entire financial landscapes. Along with providing customers with better, faster, and cheaper services,...

We apply multiple machine learning (ML) methods to model loss given default (LGD) for corporate debt using a common dataset that is cross-sectional but collected over different time periods and shows much variation over time. We investigate the efficacy of three cross-validation (CV) schemes for...

The interest collected by the main borrowers is collected to pay back the principal borrowed from the depositary bank. In financial risk management, credit risk assessment is becoming a significant sector. For the credit risk assessment of client data sets, many credit risk analysis methods are...