Financial Fraud Detection with AI: A Machine Learning-Based Approach for Securing Digital Transactions

Financial fraud is one of the most crucial challenges in the digital economy, with growing cutting-edge attacks threatening the security of online transactions. Since conventional fraud detection models are often inadequate in identifying advanced fraud schemes, Advanced fraud detection approaches are required to prevent and recognize fraud in real time. In recent years, the progression of AI methods has received considerable attention in the financial sector, specifically in Financial Fraud Detection (FFD). Furthermore, Explainable AI (XAI) has become a prerequisite for building trust and driving acceptance of AI methods in high-stakes domains, namely credit risks, financial crime, and healthcare, which require reliability, fairness, and safety. This study presents an Optimization-Based Deep Belief Network for Fraud Detection in Financial Transaction Systems (ODBN-FDFTS) approach, aiming to develop a reliable system for detecting financial transaction fraud. The ODBN-FDFTS approach begins with data preprocessing using a standard scaler to normalize financial transaction records and enhance data quality. The Artificial Rabbit Optimization (ARO) technique is employed for Feature Selection (FS), and a Deep Belief Network (DBN) is used for the financial fraud classification process, tuning its hyperparameters using the Butterfly Optimization Algorithm (BOA). LIME is integrated to provide transparency and interpretability in the fraud detection process. The comparison study of the ODBN-FDFTS model showed a superior accuracy of 97.95% over other methods on the FFD dataset.

The purpose of this study is to respond to research gaps and inconsistencies in research findings by empirically testing how financial targets and external pressures affect the detection of financial statement fraud. The study utilizes a descriptive research design with a quantitative approach. A method of purposive sampling was used, focusing on companies listed on the Jakarta Islamic Index (JII) from 2019 to 2023. Statistical analysis was done using multiple regression analysis. The results show that financial targets have a significant effect on the detection of financial statement fraud, while external pressures do not have a significant effect. However, both financial targets and external pressures have a simultaneous effect on the detection of financial statement fraud. The practical implications, financial targets, and external pressures can have an impact on the detection of fraud within the organization. Under these conditions, fraud may be more difficult to detect because the organization's or individual's focus is more on achieving targets or meeting external expectations. Therefore, companies need to have a strong internal control system and independent audits to help identify and manage potential risks of financial statement fraud.

SCN_GNN: A GNN-based fraud detection algorithm combining strong node and graph topology information

Artificial Intelligence in Financial Fraud Detection: A Comprehensive Approach to Enhancing Financial Security The rise of artificial intelligence (AI) offers an opportunity to significantly strengthen financial security by combating financial fraud, which has become increasingly complex and widespread. Traditional detection methods are often insufficient in identifying and preventing fraudulent activities, prompting a shift towards AI-based solutions. This study explores the application of AI, particularly machine learning algorithms, in improving the accuracy and efficiency of fraud detection. By analyzing large financial datasets, AI can detect anomalies that may indicate fraudulent behavior more effectively than traditional approaches. This research adopts a two-phase methodology. The first phase involves a thorough review of existing financial fraud detection methods, comparing traditional techniques with AI-based models to identify gaps. Various machine learning approaches, including supervised, unsupervised, and deep learning algorithms, are reviewed for their effectiveness in detecting fraud. The second phase involves developing and testing an AI model to identify fraudulent patterns within transactional data. The model uses machine learning algorithms to process vast datasets and detect deviations from typical financial behaviors, flagging potentially fraudulent activities. The expected results indicate that AI systems can outperform traditional fraud detection methods by significantly reducing false positives and improving the detection rate of genuine fraud. This reduction in false positives is vital for financial institutions, as it reduces unnecessary investigations and saves valuable resources. Additionally, enhanced fraud detection protects both institutions and consumers from financial losses.The findings of this study aim to provide financial institutions with practical insights into the implementation of AI-driven fraud detection systems. Furthermore, the research highlights the need for continuous refinement of AI models to adapt to the evolving nature of financial fraud. By leveraging AI technologies, financial institutions can revolutionize their approach to fraud detection, making financial systems more secure and responsive to emerging threats.

The purpose of this study was to empirically examine and accurately synthesise the effect of auditor quality, internal auditor professional ethics and internal auditor independence on the fraud detection process with information technology as a moderating variable. The process of detecting financial and economic fraud in a company plays an important role in the internal control system. The population of this study consists of internal auditors from companies located in Jakarta and Bogor. The results of this study were conducted using reliability and validity tests, with data analysis performed through descriptive analysis with three dependent variables: the Quality of Auditors as the first dependent variable (X1), the Professional Ethics as the second dependent variable (X2), and the Independence as the third dependent variable (X3). Fraud Detection serves as the independent variable (Y), while Information Technology acts as the moderating variable (Z). The results of this study indicate that the influence of auditor quality on fraud detection is positive and significant, the influence of professional ethics on fraud detection is positive and significant, the influence of independence on fraud detection is positive and significant, and the role of information technology in the influence of audit quality is positive but not significant, the role of information technology in the influence of professional ethics on fraud detection is positive but not significant, and the influence of independence on fraud detection through information technology as a moderating variable is positive and significan. This research can have implications for improving fraud prevention and detection by comprehensively improving audit quality, professional ethics and internal auditor independence by maximising the use of information technology Keywords: Auditor Quality, Profesional Ethics, Independenc and Information Technology

The exponential rise in digital transactions has made credit card fraud a critical threat to financial institutions and consumers worldwide. Traditional rule-based fraud detection systems are increasingly ineffective against sophisticated and evolving fraudulent patterns due to their inability to adapt to new tactics and handle large-scale transaction data. This paper proposes an end-to-end FraudX that classifies transactions as legitimate or fraudulent using ensemble machine learning and real-time predictive analytics. The system utilizes historical transaction data in CSV format, applies advanced preprocessing and feature engineering techniques, and employs XGBoost as the primary classifier, alongside comparative models such as Logistic Regression and Random Forest]. Experimental results on a publicly available credit card fraud dataset demonstrate that XGBoost achieves superior performance with an accuracy of 99.8%, precision of 92.5%, and recall of 88.3%. The system is implemented as a modular web application with a Flask backend and SQLite database, offering an interactive dashboard for transaction screening, detailed reporting, and administrator-led model retraining. This work provides a scalable, efficient, and reliable solution for real-time fraud detection in financial ecosystems. Keywords: Credit card fraud detection, ensemble learning, XGBoost, machine learning, Flask web application, financial security, predictive analytics, imbalanced data.

The rise of financial fraud, not only in India but also around the world, is a major problem. Credit card transactions have been steadily increasing in recent years, as have internet payments like Unified Payments Interface and debit card transactions, which have been increasing every day. As a result, fraud is increasing, and it has become easier for fraudsters to commit fraud. Recent research indicates the practicality of machine learning algorithms for recognizing payment fraud. Since credit cards are a common target, online fraudsters simply commit fraud because of excess use of e-commerce and other platforms, which has led to an increase in online payment methods, resulting in a greater risk of online fraud. Many researchers have started using machine learning algorithms to detect fraud. The primary objective of this study is to create a better fraud detection algorithm by analyzing payment transactional data, focusing on customers' transactional data and their payment behavioral patterns. The study proposes a framework that groups cardholders based on the volume of their transactions. Financial fraud is the biggest threat to world economies in terms of financial losses each year. The task of accurately and efficiently detecting fraud is, therefore, very challenging due to the high volume of transaction data and its complex nature. This paper presents a hybrid machine learning approach using logistic regression, decision trees, and random forests that could help enhance the accuracy and reliability of fraud detection systems. The study used an available credit card fraud dataset, with a focus on feature engineering and model evaluation to compare individual algorithms versus their ensemble. Experimental results show that the ensemble outperformed the individual classifiers in terms of precision, recall, and overall accuracy. This paper underlines the potential of hybrid machine learning techniques in improving fraud detection systems and proposes avenues for further research.

Personal, commercial, and economic impacts of financial fraud are significant. Traditional rule-based fraud detection systems often generate false positives and struggle to adapt to new fraud methods. ML algorithms are examined as an alternative to traditional financial fraud detection methods in this chapter. It explores how supervised, unsupervised, and hybrid models may identify financial data abnormalities and fraud patterns. Money laundering, payments fraud, and other financial crimes are covered in this chapter. Fraud detection is complicated by data imbalance, privacy concerns, and model scalability. It also shows how AI, blockchain, and predictive analytics will fight fraud and secure financial institutions. This chapter reviews how new technology is changing financial crime prevention, using examples from credit card and online retail fraud.

With the prevalence of Internet AI technology, financial fraud becomes an imperative problem, especially in the context of machine learning. Technologies such as deep learning and natural language processing provide effective tools for detecting fraud with the guidance of financial statements, improving the efficiency and accuracy of data analysis, and helping to ensure financial safety. In this study, we propose a sophisticated representation learning method to detect financial statement fraud by tracking the detailed changes in the company’s Management Discussion and Analysis (MD&A) documents over time. Unlike traditional word frequency methods, we align paragraphs between consecutive disclosures based on their similarity at the paragraph level and categorize them into three types: added, deleted, and matched. Next, we create multivariate change trajectory representations based on fraud-related word categories. Finally, we use these word-level change trajectories to design a fraud detection model and compare it with several traditional models as well as the latest Time-Series Foundation Models. Experiments on 24 years of financial report data, from 1995 to 2019, show that our representation learning method significantly improves the performance of financial statement fraud detection across 11 different machine learning models, consistently outperforming traditional word frequency methods. Our method opens a new paradigm for feature engineering in financial statement fraud detection. Our code can be found at https://github.com/LittelStudent/Financial-Statement-Fraud-Detection-ParaEmb-FraudW2V .

Detecting evolutionary financial statement fraud

This paper addresses the critical challenge of fraud detection in medical insurance claims-a pervasive issue causing significant financial losses in healthcare-using Graph Neural Networks (GNNs). Given the intricate nature of healthcare data, traditional fraud detection methods do not inherently capture the complex relationships and patterns among different entities. We explore the potential of GNNs to effectively identify fraudulent claims by modeling the interactions among various entities-such as patients, healthcare providers, diagnoses, and services-as a heterogeneous graph. We employ two state-of-the-art heterogeneous GNN architectures, HINormer (Heterogeneous Information Network Transformer) and HybridGNN, along with a modified homogeneous GNN, RE-GraphSAGE (GraphSAGE "Graph Sample and Aggregate" with relation embeddings), adapted to handle the heterogeneity of healthcare data. The models are evaluated on real-world claims datasets of different sizes, comprising millions of medical activities. For the small-size claims dataset, the HINormer architecture followed by the RE-GraphSAGE architecture achieved the highest F-score (84% and 83%, respectively). For the medium-sized claims dataset, RE-GraphSAGE followed by HINormer achieved the highest F-score (84% and 81%, respectively), and for the large-size claims dataset, HINormer followed by RE-GraphSAGE achieved the highest F-score (82% and 79%, respectively). Additionally, we apply explainability techniques, namely GNNExplainer and PGExplainer, to provide insights into the models' decision-making processes and to examine their medical significance.

The paper examine the effect of digital accounting software on fraud detection and prevention in financial institution. Digital accounting software has been introduced as a tool to enhance fraud prevention and detection by automating financial processes, improving accuracy, and strengthening internal controls. However, despite its advantages, the adoption of digital accounting software in Nigerian deposit money banks faces several challenges. Forensic accounting is essential for fraud control and internal audit quality since financial fraud is largely caused by the underutilization of digital forensic techniques. Nigeria's increasing rate of electronic fraud emphasizes the necessity of regulatory action to stop additional losses, due to the increase in fraudulent activity in Nigeria's banking industry, digital forensic accounting tools are now required for fraud detection. This study adopted a survey research design, which the few listed financial institution that have high rating in Nigeria. It was concluded that capacity building and training is low in the sector, technology integrated needed to be practices by staff always while it was recommended that regular training for staff on the effective use of digital accounting software, particularly in fraud detection features such as anomaly monitoring and audit trail analysis and banks should not rely solely on digital tools but integrate them with strong governance frameworks, internal controls, and accountability structures to reduce opportunities for fraud.

Fraud and money laundering continue to threaten financial stability around the world, thereby creating a need for detection systems that are both trustworthy, accurate, and reliable. Regulators increasingly require fraud detection systems to be transparent, accountable, and reliable. To address these challenges, this paper presents an Integrated Transparency and Confidence Framework (ITCF) for automated fraud detection. The framework combines local explainability using Local Interpretable Model-Agnostic Explanations (LIME) with calibrated uncertainty quantification using split Conformal Prediction (CP), aiming to improve both the understanding and the confidence in artificial intelligence-driven decisions. The framework was evaluated using the PaySim mobile-money dataset, which contains 6, 362, 620 transactions. Random Forest (RF) and XGBoost (XGB) models were benchmarked for fraud detection. LIME provided clear, case-specific explanations and aggregated feature importance, helping financial crime analysts understand why individual transactions were flagged. Conformal prediction measures model uncertainty and offers distribution-free coverage guarantees under independent and identically distributed (IID) assumptions. When targeting 90% coverage (α = 0.1), both models achieved empirical coverage close to 0.90. However, XGBoost outperformed Random Forest by achieving a higher F1-score and recall while also producing smaller prediction sets, making it more effective for risk-sensitive decision-making. Overall, the ITCF supports risk-based workflows by triaging high-uncertainty transactions for human review, reducing alert fatigue, and improving auditability. By integrating explainability and uncertainty estimation, the framework enhances transparency, strengthens operational trust, and supports the responsible, regulation-aligned use of AI in financial fraud and money laundering detection.

Background: Fraud in financial transaction is at the root of corruption issues recorded in organization. Detecting fraud practices has become increasingly complex and challenging. As a result, auditors require precise analytical tools for fraud detection. Grouping financial transaction data using K-Means Clustering algorithm can enhance the efficiency of applying Benford Law for optimal fraud detection. Objective: This study aimed to introduce Multiple Benford Law Model for the analysis of data to show potential concealed fraud in the audited organization financial transaction. The data was categorized into low, medium, and high transaction values using K-Means Clustering algorithm. Subsequently, it was reanalyzed through Multiple Benford Law Model in a specialized fraud analysis tool. Methods: In this study, the experimental procedures of Multiple Benford Law Model designed for public sector organizations were applied. The analysis of suspected fraud generated by the toolkit was compared with the actual conditions reported in audit report. The financial transaction dataset was prepared and grouped into three distinct clusters using the Euclidean distance equation. Data in these clusters was analyzed using Benford Law, comparing the frequency of the first digit’s occurrence to the expected frequency based on Benford Law. Significant deviations exceeding ±5% were considered potential areas for further scrutiny in audit. Furthermore, the analysis were validated by cross-referencing the result with the findings presented in the authorized audit organization report. Results: Multiple Benford Law Model developed was incorporated into an audit toolkit to automated calculations based on Benford Law. Furthermore, the datasets were categorized using K-Means Clustering algorithm into three clusters representing low, medium, and high-value transaction data. Results from the application of Benford Law showed a 40.00% potential for fraud detection. However, when using Multiple Benford Law Model and dividing the data into three clusters, fraud detection accuracy increased to 93.33%. The comparative results in audit report indicated a 75.00% consistency with the actual events or facts discovered. Conclusion: The use of Multiple Benford Law Model in audit toolkit substantially improved the accuracy of detecting potential fraud in financial transaction. Validation through audit report showed the conformity between the identified fraud practices and the detected financial transaction. Keywords: Fraud Detection, Benford’s Law, K-Means Clustering, Audit Toolkit, Fraudulent Practices.

Semi-supervised node classification on graph-structured data has many applications such as fraud detection, fake account and review detection, user’s private attribute inference in social networks, and community detection. Various methods such as pairwise Markov Random Fields (pMRF) and graph neural networks were developed for semi-supervised node classification. pMRF is more efficient than graph neural networks. However, existing pMRF-based methods are less accurate than graph neural networks, due to a key limitation that they assume a heuristics-based constant edge potential for all edges. In this work, we aim to address the key limitation of existing pMRF-based methods. In particular, we propose to learn edge potentials for pMRF. Our evaluation results on various types of graph datasets show that our optimized pMRF-based method consistently outperforms existing graph neural networks in terms of both accuracy and efficiency. Our results highlight that previous work may have underestimated the power of pMRF for semi-supervised node classification.