Effectiveness Of Natural Language Processing Based Security Tools In Strengthening The Security Over Fin-Tech Platforms

The rapid evolution of financial technology (Fin-Tech) platforms has revolutionized the financial landscape buthas simultaneously introduced significant cybersecurity challenges. Natural Language Processing (NLP)-basedsecurity tools have emerged as a promising solution to enhance security measures in these platforms. This studyexplores the effectiveness of NLP in identifying, mitigating, and preventing cyber threats, such as Fraudulent,Anomaly, and Unauthorized Access. This study explores the efficiency of NLP-based security tools inenhancing the security of fin-tech platforms by leveraging machine learning techniques such as Support VectorMachines (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN). By analyzing patterns in textualdata, these tools aim to detect vulnerabilities, predict threats, and mitigate risks in real-time. In this study,researchers used the financial data and textual data. The research evaluates the performance of these MLmodels, focusing on their accuracy (Aaccuracy), precision (Pprecision), recall (Rrecall), and F1-score (F1score).The experimental result shows that the proposed RF model shows the highest Aaccuracy (98.83%), Pprecision(97.70%), Rrecall (98.64%), and F1score (98.17%).Keywords: NLP, Financial technology (Fin-Tech), Machine Learning (ML), Cyber security