Risk diversification is the basis of insurance and investment. It is thus crucial to study the effects that could limit it. One of them is the existence of systemic risk that affects all of the policies at the same time. We introduce here a probabilistic approach to examine the consequences of...

The application of industry 4.0 in banking presents many challenges, with several operational risks related to downtime and timeout services due to system failures. One of the operational risk management steps is to estimate the value of the maximum potential losses. The purpose of this study is...

The aim of the research presented in the article was to analyse the legitimacy of the use of scoring models in banking activities, together with the assessment of the effectiveness of this tool in reducing the high value of the NPL ratio in Polish cooperative banks on the example of banks...

Risk taking is an inherent element of the banking business. Banks make conscious decisions regarding risk taking as they expect to make more return if they take more risk. The primary objective of this study is to empirically investigate the efficiency of Indian banks in generating return...

This edited collection comprehensively addresses the widespread regulatory challenges uncovered and changes introduced in financial markets following the 2007-2008 crisis, suggesting strategies by which financial institutions can comply with stringent new regulations and adapt to the pressures...

This book offers 14 contributions that examine key questions in bank decision-taking, constitution of confidence in banks and risk management practices from Early Modernity to the twentieth century. It explores how the various mechanisms of bank decision taking changed over time. Chapters also...

Christian Hugo Hoffmann undermines the citadel of risk assessment and management, arguing that classical probability theory is not an adequate foundation for modeling systemic and extreme risk in complex financial systems. He proposes a new class of models which focus on the knowledge dimension...

The practice of quantitative risk management has reached unprecedented levels of refinement. The pricing, the assessment of risk as well as the computation of the capital requirements for highly complex transactions are performed through equally complex mathematical models, running on advanced...

There is an increasing influence of machine learning in business applications, with many solutions already implemented and many more being explored. Since the global financial crisis, risk management in banks has gained more prominence, and there has been a constant focus around how risks are...