Published Paper Details:

Analytics has become a key player in finance, helping with things like managing risks and catching fraud. This study looks into how effective data analytics really is when it comes to evaluating financial risks, detecting fraud, and meeting regulatory standards. It also talks about some of the challenges we face with current tools. The paper emphasizes important parts of risk assessment models--like stress testing, scenario analysis, and statistical techniques--that help organizations manage financial risks effectively. It also explores different methods used to spot fraud, such as recognizing patterns, finding unusual anomalies, and using predictive models. A big point is how we need to scale up computer analysis to handle the huge amounts of financial data out there. The study also dives into the tricky issues that come with big data--like making sure systems can handle larger datasets, keeping data private, and finding the right structure for preventing fraud. As fraud cases in finance continue to grow and become more sophisticated, securely sharing information across different platforms is more important than ever. That's why blockchain technology is brought up as a promising tool for fraud detection, because it offers transparency, unchangeable records, and strong security for transactions. Overall, this research tries to fill some gaps in what we already know by proposing data-driven, scientifically sound ways to manage risks, fight fraud, and promote ethical use of data. The goal is to help financial companies improve their fraud detection systems, stay compliant with regulations, and reduce risks, which finally makes the financial system safer for everyone.