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Volume 1 Number 1 2025

Article Contents

RESEARCH ARTICLE   (Open Access)
Contents Vol 1 (1)

AI in Fraud Detection and Forensic Accounting Detecting Anomalies and Fraudulent Transactions to Strengthen Financial Integrity

Muslima Jahan Diba1*, Md. Rezaul Haque2, Md Nazmuddin Moin Khan3

+ Author Affiliations

Paradise 1 (1) 1-8 https://doi.org/10.25163/paradise.1110494

Submitted: 05 March 2025 Revised: 07 May 2025  Published: 15 May 2025 

Abstract

Background: Financial operations in organizations face continuous threats from fraud which results in operational disruptions and damages the confidence of all stakeholders. Traditional auditing and manual forensic accounting methods often fail to detect complex anomalies in high-volume transactions.

Methods: A total of 210 financial professionals, including auditors, accountants, and financial analysts, were surveyed, and historical transaction data spanning 12 months were analyzed at January to December in 2024. The research team applied Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) and Random Forest (RF) and hybrid frameworks to detect anomalies through AI model implementation. The evaluation of model performance used detection share metrics together with precision and recall rates and correlation coefficients (r). The study used statistical methods including chi-square (χ²) tests and t-tests and p-values to determine how AI adoption levels affect model performance and fraud detection results.

Results: LSTM models achieved the highest detection share (24.5%), while hybrid AI frameworks reduced false positives by 15–20% and balanced precision and recall. Unauthorized transfers (21%) and inflated invoices (19%) were the most frequently detected anomalies. The chi-square analysis revealed that AI adoption links directly to anomaly detection success at χ² = 4.59 with p = 0.048 and t-tests showed that different adoption levels created distinct fraud reduction outcomes. The results showed positive correlations between AI intensity and fraud reduction with correlation coefficients (r = 0.28-0.56).

Conclusion: AI systems achieve superior results by integrating LSTM with hybrid frameworks which enhances financial integrity and advances anomaly detection systems.

Keywords: Artificial Intelligence, Forensic Accounting, Fraud Detection, Anomaly Detection, Financial Integrity

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