Leveraging Data Mining for Enhanced Risk Assessment and Security Intelligence in Financial Institutions

EasyChair Preprint 14373

Abstract

Effective risk assessment and security intelligence are essential for protecting assets and upholding operational integrity in the ever-changing financial institution context. The integration of cutting-edge data mining tools to improve these vital roles is explored in this research. Financial organizations are able to recognize patterns and abnormalities that could point to possible dangers or security issues by utilizing large datasets and advanced algorithms. We suggest a methodology for creating predictive models for risk assessment that makes use of data mining to examine past transaction data, consumer behavior, and outside variables. We also look at the application of real-time data mining for ongoing security breach detection and continuous monitoring. We illustrate the usefulness and advantages of this strategy through case studies and empirical analysis, including increased risk prediction accuracy,

Keyphrases: Cybersecurity, Financial Institutions, Financial Risk Management, Predictive Analytics, Security Intelligence, fraud detection

@booklet{EasyChair:14373,
  author    = {Oluwaseun Abiade},
  title     = {Leveraging Data Mining for Enhanced Risk Assessment and Security Intelligence in Financial Institutions},
  howpublished = {EasyChair Preprint 14373},
  year      = {EasyChair, 2024}}