Pallav Kumar Kaulwar's research addresses the escalating crisis of financial fraud, emphasizing the shortcomings of traditional detection systems. His paper outlines how generative AI technologies like GANs and Bayesian neural networks can revolutionize real-time fraud detection. With billions lost annually to financial fraud, conventional methods relying on fixed rules and historical data can't keep pace with increasingly sophisticated cybercriminal strategies. Kaulwar asserts that fraud detection must evolve from being reactive to predictive, utilizing dynamic models that adapt to new patterns and emerging threats.
"The industry needs a shift from reaction to prediction," says Kaulwar. "Today's fraud detection must be dynamic, data-rich, and capable of identifying new patterns as they emerge-before they escalate into large-scale breaches."
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