Impact Of Cyber Security And Forensic Accounting Techniques On Fraud Detection In Indian Business

Abstract

In the rapidly evolving digital economy, businesses increasingly rely on online financial transactions and digital infrastructures, which has significantly increased the risk of cyber fraud and financial irregularities. Fraudulent activities in business environments can cause severe financial losses, reputational damage, and legal complications for organizations. Traditional fraud detection methods often rely on manual auditing and rule-based systems, which are inefficient in handling large-scale transactional data and complex fraud patterns. Therefore, there is a growing need for intelligent systems capable of automatically identifying suspicious financial activities by analyzing large datasets and detecting hidden patterns associated with fraud. This study presents a machine learning–based framework that integrates cybersecurity insights and forensic accounting principles to enhance fraud detection in Indian businesses. The proposed system utilizes an Artificial Neural Network (ANN) model to analyze financial transaction data and identify potentially fraudulent activities. The dataset undergoes preprocessing steps such as data cleaning, normalization, and feature transformation to improve model performance. The trained ANN model learns patterns from historical transaction records and predicts whether a given transaction is legitimate or fraudulent. The system aims to assist financial institutions and organizations in strengthening their fraud prevention mechanisms by providing accurate and efficient detection of suspicious activities. By leveraging machine learning techniques and data-driven analysis, the proposed approach improves the accuracy and reliability of fraud detection compared to traditional methods. The model can analyze complex relationships among transaction attributes and identify anomalies that indicate possible fraudulent behavior. This intelligent framework contributes to strengthening cybersecurity measures and financial transparency within organizations while supporting decision-making processes for fraud investigation and risk management.