IIARD International Institute of Academic Research and Development

Journal of Accounting and Financial Management (JAFM )

E-ISSN 2504-8856
P-ISSN 2695-2211
VOL. 9 NO. 12 2023
DOI: 10.56201/jafm.v9.no12.2023.pg206.217

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Enhancing Fraud Detection in Finance and Insurance: Harnessing the Power of Machine Learning and Advanced Data Analytics for Real-Time Insights

Adetola Akintola

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Abstract

This study did an exploration of the combination of the application of cross cutting-edge technologies in the finance and insurance sectors’ financial fraud detection mechanisms to identify, analyze, and prevent fraudulent activities efficiently and effectively, thereby appealing to a global community by demonstrating the cross-industry benefits of these innovations. The paper presents a review of some critical literature on the application of machine learning and advanced data analytics for real-time insights techniques to enable the detection of financial fraud and proposes a structure for fraud detection. The systematic and comprehensive literature review of techniques applicable to enhancing fraud detection in finance and insurance fraud detection may provide a foundation to future research in this field. The adoption of the findings of Sharma and Panigrahi (2013) show the most frequently used techniques includes but not limited to Regression Models; Neural Networks; Bayesian Belief Network; Decision Trees; Naïve Bayes; Nearest Neighbour Method; Fuzzy logic, Genetic Algorithm; and Expert Systems. They all fall into the ?classification category that fundamentally makes provision for remedial measures to the prevalent challenges of financial and insurance fraud data that the research discussed. The paper recommend that the identified possibilities in technology and data-driven solutions by this study should be effectively purified to possess the ability to face the hydra- headed tact and dynamics in financial and insurance fraud against the perpetrators as they always strive to innovate in the world of advancement.

keywords:

Data Analysis; Fraud Detection; Finance; Insurance; Machine Learning

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