Fraud Detection and Financial Data Protection in Digital Banking by Shanaya Desai

Fraud Detection and Financial Data Protection in Digital Banking by Shanaya Desai

ABSTRACT:

As banking becomes more digital to increase convenience of online transactions, the risk of financial scams increases noticeably because banks now have to store huge amounts of personal and sensitive financial data in their systems. This increases the need for effective fraud detection in order to protect user privacy. Conventional detection methods are not as effective as those that can be enforced using newer technologies. Artificial Intelligence and Machine Learning models can be helpful in stopping potential scams by identifying patterns. The aim of this research paper is to propose a decision tree based machine learning model that can help identify suspicious transactions and terminate them before any significant harm is caused, while also protecting user data. A decision tree algorithm is the most ideal for this scenario since its decision making process is the easiest to interpret compared to other algorithms. The proposed model considers data minimisation, which only provides the required data to the algorithm while maintaining complete confidentiality of sensitive information. This model has not been experimentally tested and hence the degree of its effectiveness may vary under various circumstances. This research paper wishes to evaluate how fraudulent transactions can be detected while still considering the importance of customer data privacy.

KEYWORDS:

Machine Learning, Artificial Intelligence, Digital Banking, Fraud Detection, Decision

Tree, Cybersecurity, Financial Data Privacy

INTRODUCTION:

The evolution of online banking has revolutionised how people and organisations make

payments. Financial fraud can be defined as any intentional act of deception involving

financial transactions for unlawful gain. (Lopez, 2025) [1] In today’s time, digital banking

continues to become prevalent as both individuals and organisations rely on these systems for faster, efficient and more accessible methods of payment. 

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