Integration and Risk in the European Banking Union: Study Through Complex Networks and Machine Learning
We define the degree of #banking integration in the #eurozone through different phases of the #economic cycle, from 2006 to 2020, with #complexnetworks and #clusteralgorithms … Regarding the nodes of the network, #germany yields the position of centrality in favor of #france. Lire
Models of Accounting Disclosure by Banking Institutions
Monograph on accounting disclosure by banking institutions explores banking specificities, presents workhorse models, and illustrates specific applications of the models to inform policy. Lire
Is Bank CEO Pay Sensitive to Operational Risk Event Announcements?
This study reveals how operational risk events affect US bank CEO compensation from 1992-2016. Results indicate that compensation committees take operational risk into account & that recent regulations have enhanced this process. Additionally, operational risk events have a detrimental effect on options-based compensation. Lire
Geopolitical Uncertainty and Banking Risk: International Evidence
“The increased banking risk mainly attributed to reduction in bank capital and escalated fluctuations in bank profitability.” Lire
Machine Learning for Categorization of Operational Risk Events Using Textual Description
“… an overview of how machine learning can help in categorizing textual descriptions of operational loss events into Basel II event types. We apply PYTHON implementations of support vector machine and multinomial naive Bayes algorithms to precategorized Öffentliche Schadenfälle OpRisk (ÖffSchOR) data to demonstrate that operational loss events can be automatically assigned to one of […]
The Information Value of Past Losses in Operational Risk
“… the information provided by past losses results from them capturing hard to quantify factors such as the quality of operational risk controls, the risk culture, and the risk appetite of the bank.” Lire
Using Knowledge Distillation to improve interpretable models in a retail banking context
” Predictive machine learning algorithms used in banking environments, especially in risk and control functions, are generally subject to regulatory and technical constraints limiting their complexity. Knowledge distillation gives the opportunity to improve the performances of simple models without burdening their application, using the results of other – generally more complex and better-performing – models.” Lire
Operational Loss Recoveries and the Macroeconomic Environment: Evidence from the U.S. Banking Sector
“Our findings offer new evidence on how economic shocks transmit to banking industry losses with implications for risk management and supervision.” Lire
Big Data-Driven Banking Operations: A Review on Opportunities, Challenges, and Data Security Perspective
“The findings include data innovation creating opportunities for a well-developed banking supply chain, effective risk management and financial fraud detection, bank customer analytics, and bank decision-making.” Lire
Strategy and Business Models in Retail Banking: Why They Also Matter to Supervisors
“Retail banking is a distinct part of the banking industry. It has been undergoing important changes in recent decades mainly due to technological innovations and deregulation.” Lire
Operational Research and Artificial Intelligence Methods in Banking
“The article reviews the main topics of this research, including bank efficiency, risk assessment, bank performance, mergers and acquisitions, banking regulation, customer-related studies, and fintech in the banking industry. “ Lire