Citation :

Sumanth Tatineni, Anirudh Mustyala, 2024. "Enhancing Financial Security: Data Science's Role in Risk Management and Fraud Detection" ESP International Journal of Advancements in Computational Technology (ESP-IJACT)  Volume 2, Issue 2: 94-105.

Abstract :

Financial security is a critical concern for individuals, businesses, and governments alike. The increasing reliance on digital transactions and the interconnectedness of global financial systems have amplified the risks associated with fraud and financial crimes. In this context, the role of data science in enhancing financial security has become paramount. This paper explores the applications of data science in risk management and fraud detection within the financial sector. Data science techniques, including machine learning, statistical analysis, and big data processing, are being leveraged to analyze vast amounts of financial data in real-time. These techniques enable financial institutions to identify and mitigate risks more effectively, leading to improved decision-making processes. By utilizing historical transaction data, machine learning algorithms can detect anomalous patterns indicative of fraudulent activities. Moreover, by incorporating external data sources such as social media, weather data, and market trends, financial institutions can enhance their risk assessment models. Risk management in the financial sector is not limited to fraud detection but also includes assessing credit risks, market risks, and operational risks. Data science enables the development of models that can predict these risks with greater accuracy, thereby enabling proactive risk mitigation strategies. Furthermore, by employing natural language processing (NLP) techniques, financial institutions can analyze unstructured data, such as customer reviews and news articles, to gauge public sentiment and potential risks.

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Keywords :

Financial Security, Data Science, Risk Management, Fraud Detection.