Fraud Detection by Using Deep Learning in Mining the Information Technology for AI and BI

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The article discusses the use of #deeplearning and #datamining in business intelligence protocols to optimize data-driven decision-making and improve efficiency. The authors focus on the use of Graph Neural Network and Autoencoders Models to process large amounts of data and model #fraud behaviors. They suggest that deep learning can be used to control #moneylaundering in financial institutions and improve visibility and transparency in businesses.

Machine Learning for Automating Monitoring, Review and Testing at Financial Institutions

#financialinstitutions are increasingly using #machinelearningalgorithms for credit risk mgmt., #fraudprevention, and #aml. This paper presents robust evidence of using logistic regression, linear discriminant analysis, and neural networks for accurately predicting and classifying financial transactions for Volcker Rule #compliance. It provides a scalable minimum viable product to automate #controls testing.

Distrust Spillover on Banks: The Impact of Financial Advisory Misconduct

Local communities exposed to #fraudulent #investmentadvisory firms tend to withdraw deposits from their affiliated #banks, even though the banks are not involved in the #misconduct. The #reputationalrisk is more significant when banks share names with fraudulent advisory firms or are located in areas with high social norms. The author establishes causality by exploring a quasi-natural experiment in which #fraud is likely exogenously revealed.

Discretionary Decisions in Capital Requirements under Solvency II

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#insurers have discretion to determine #solvencyii #capitalrequirements. We find that long-term guarantees measures substantially influence the reported solvency ratios. The measures are chosen particularly by less solvent insurers and firms with high interest rate and credit spread sensitivities. Internal #models are used more frequently by large insurers and especially for #risks for which the firms have already found adequate immunization strategies.

Market Adoption of Cybersecurity: A Dynamic Analysis

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"This paper presents a continuous-time dynamic model of market adoption of #cybersecurity. Individuals choose whether and when to make a precautionary investment in self-protection against the evolving security #risk of direct attack and indirect contagion. The equilibrium adoption path has a ``tipping point'': individual users will invest to get protected all at once when a critical mass of the infected has been reached."