International Journal on Science and Technology

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Using Artificial Intelligence and Data Analytics to combat Financial Frauds

Author(s) Mitesh Bagwe
Country United States
Abstract Fraud related to financial transactions is one of the most imminent dangers that affect individual states and international economies with billions of dollars. Conventional approaches to fraud prevention and control are sometimes hard-pressed to adapt to the fraudsters’ ever-growing sophistication. Due to the advancements in technology, especially the implementation of AI and data analytics, the detection of financial fraud in real-time, predictive and analytical analysis, and the identification of complicated patterns have emerged. Two key technologies include; machine learning models that employ a mix of algorithms to analyze large data sets to find outflows, and data analytics that offer ways of mitigating risk. All of these technologies are developed to provide better accuracy, fewer false positives, and faster work. Nevertheless, problems like data protection or ownership, the principles of fairness, and the presence of bias stay actual. This paper aims to reveal the benefits of using AI and data analytics in fraud prevention, describe the practical uses of this approach, and possible further enhancements to the technique. Through these modern technologies, financial institutions can prevent the loss of their assets as well as enhance consumer confidence in the digital age.
Keywords Artificial Intelligence in Fraud Detection, Data Analytics for Financial Fraud, Machine Learning and Fraud Prevention, Real-Time Fraud Detection, Predictive Analytics in Finance, AI-Powered Risk Mitigation, Financial Technology (FinTech) Solutions
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 16, Issue 2, April-June 2025
Published On 2025-05-01
Cite This Using Artificial Intelligence and Data Analytics to combat Financial Frauds - Mitesh Bagwe - IJSAT Volume 16, Issue 2, April-June 2025. DOI 10.71097/IJSAT.v16.i2.4290
DOI https://doi.org/10.71097/IJSAT.v16.i2.4290
Short DOI https://doi.org/g9g7zr
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