A Review on Financial Fraud Detection using AI and Machine Learning
Creators: name: Kamuangu, Paulin
Publisher: name: figshare
Year: 2024
Subjects: classificationCode: 350101
subject: Accounting theory and standards
classificationCode: 350102
subject: Auditing and accountability
classificationCode: 350103
subject: Financial accounting
classificationCode: 350104
subject: International accounting
classificationCode: 350105
subject: Management accounting
classificationCode: 350107
subject: Sustainability accounting and reporting
classificationCode: 350199
subject: Accounting, auditing and accountability not elsewhere classified
classificationCode: 350202
subject: Finance
classificationCode: 350201
subject: Environment and climate finance
classificationCode: 350203
subject: Financial econometrics
classificationCode: 350204
subject: Financial institutions (incl. banking)
classificationCode: 350207
subject: International finance
classificationCode: 350208
subject: Investment and risk management
classificationCode: 350299
subject: Banking, finance and investment not elsewhere classified
classificationCode: 460201
subject: Artificial life and complex adaptive systems
classificationCode: 460202
subject: Autonomous agents and multiagent systems
classificationCode: 460203
subject: Evolutionary computation
classificationCode: 460204
subject: Fuzzy computation
classificationCode: 460205
subject: Intelligent robotics
classificationCode: 460206
subject: Knowledge representation and reasoning
classificationCode: 460207
subject: Modelling and simulation
classificationCode: 460208
subject: Natural language processing
classificationCode: 460210
subject: Satisfiability and optimisation
classificationCode: 460211
subject: Speech production
classificationCode: 460212
subject: Speech recognition
classificationCode: 460299
subject: Artificial intelligence not elsewhere classified

description: This study thoroughly explores advanced approaches for addressing financial fraud, focusing on the effectiveness of Machine Learning (ML) and Artificial Intelligence (AI). Recognizing the drawbacks of outdated methods, the examination aims to analyze the current situation, closely examining the efficiency and limitations of ML and AI techniques while mapping out intricate directions for future research. We delve into the intricate history of financial fraud, uncovering the inherent constraints embedded in conventional rule-based and manual detection approaches. Then, machine learning (ML) and artificial intelligence (AI) are discussed, highlighting significant research and successful implementations that have transformed the field of fraud detection. While analyzing the assessment metrics, we use various measures such as accuracy, precision, recall, F1 score, and the enigmatic ROC-AUC. Then, diverse ML and AI algorithms are introduced, including the mysterious Random Forest, the reliable Support Vector Machines (SVM), and the complex neural networks. As comparative analysis unfurls, uncovering the strengths and weaknesses inherent in distinct ML and AI systems. Beyond the limits of performance measures, our interpretation transcends, diving into the real-world ramifications and the labyrinth of possible routes for refinement and advancement.