Leveraging data mining and cybersecurity techniques to enhance algorithmic trading performance and forensic investigations in financial markets

Kenneth Chukwujekwu Nwafor 1, *, Daniel O. T. Ihenacho 2, and Paul William Nyanda 3

1 Management Information Systems, University of Illinois, Springfield, USA.
2 Department of Management Information Systems, University of Illinois Springfield. USA.
3 Financial Analyst, Comprehensive Community Based Rehabilitation in Tanzania, Tanzania.
 
Review
International Journal of Science and Research Archive, 2024, 13(01), 3091–3106.
Article DOI: 10.30574/ijsra.2024.13.1.2039
Publication history: 
Received on 08 September 2024; revised on 22 October 2024; accepted on 24 October 2024
 
Abstract: 
In the evolving landscape of financial markets, the convergence of data mining, cybersecurity, and algorithmic trading plays a pivotal role in enhancing trading performance and forensic investigations. This study investigates how data mining techniques are leveraged to extract meaningful patterns and trends from vast financial datasets, improving the accuracy and profitability of algorithmic trading strategies. By identifying historical trends, price movements, and trade margins, data mining enables traders to optimize decision-making processes and manage risks more effectively. Cybersecurity emerges as a critical factor in safeguarding both trading algorithms and sensitive financial data from cyber threats. The integration of advanced cybersecurity measures ensures the integrity, confidentiality, and availability of trading systems, reducing vulnerabilities that could be exploited by malicious actors. Additionally, forensic investigation techniques are employed to detect fraudulent trading activities, such as insider trading and market manipulation, thereby protecting market participants and maintaining regulatory compliance. This research highlights the importance of combining secure data mining practices with robust cybersecurity measures to enhance the overall performance of algorithmic trading systems. Furthermore, it explores how forensic methodologies can help detect anomalies and ensure the transparency and fairness of financial markets. Through an integrated approach, this study emphasizes the potential of data mining and cybersecurity in transforming trading operations while mitigating risks associated with cyber threats and financial fraud.
 
Keywords: 
Algorithmic trading; Data mining; Cybersecurity; Forensic investigations; Financial fraud; Detection; Trade margins analysis
 
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