Fraud detection in banking using deep reinforcement learning

Bouchti, Abdelali El; Chakroun, Ahmed; Abbar, Hassan; Okar, Chafik

doi:10.1109/intech.2017.8102446

Cited by 16 publications

(9 citation statements)

References 13 publications

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“…The results showed the efficacy of the new approach on the given FFD problems, including two real-life situations. The authors of [59] applied the theory of DRL through two applications in banking and discussed its implementation for fraud detection. Using a DT with a combination of the Luhn algorithm and the Hunt algorithm, Save et al [62] proposed a system that detects fraud in the processing of credit card transactions.…”

Section: Card Transactions From An Indonesian Bankmentioning

confidence: 99%

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Fraud Detection Using the Fraud Triangle Theory and Data Mining Techniques: A Literature Review

2021

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Fraud entails deception in order to obtain illegal gains; thus, it is mainly evidenced within financial institutions and is a matter of general interest. The problem is particularly complex, since perpetrators of fraud could belong to any position, from top managers to payroll employees. Fraud detection has traditionally been performed by auditors, who mainly employ manual techniques. These could take too long to process fraud-related evidence. Data mining, machine learning, and, as of recently, deep learning strategies are being used to automate this type of processing. Many related techniques have been developed to analyze, detect, and prevent fraud-related behavior, with the fraud triangle associated with the classic auditing model being one of the most important of these. This work aims to review current work related to fraud detection that uses the fraud triangle in addition to machine learning and deep learning techniques. We used the Kitchenham methodology to analyze the research works related to fraud detection from the last decade. This review provides evidence that fraud is an area of active investigation. Several works related to fraud detection using machine learning techniques were identified without the evidence that they incorporated the fraud triangle as a method for more efficient analysis.

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Section: Card Transactions From An Indonesian Bankmentioning

confidence: 99%

“…[45,46,49,56,57,60,63,67,69] obtained the highest score of 2.5, which represents 83.33% of the maximum score that a preliminary study could obtain; on the other hand, Refs. [38,39,41,44,48,[50][51][52][53]55,59,65] obtained a score of 2, that represents 66.67% of the maximum score. Refs.…”

Section: Quality Assessmentmentioning

confidence: 99%

Fraud Detection Using the Fraud Triangle Theory and Data Mining Techniques: A Literature Review

2021

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“…We compare the performance of our reward function with the reward function proposed in this paper. In [10], authors present application of DRL in financial risk analysis and fraud detection while [39] proposes alert threshold selection policy in fraud systems using Deep Q-Network.…”

Section: Related Workmentioning

confidence: 99%

Application of Deep Reinforcement Learning to Payment Fraud

Siddharth¹,

Kayathwal²,

Wadhwa³

et al. 2021

Preprint

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The large variety of digital payment choices available to consumers today has been a key driver of e-commerce transactions in the past decade. Unfortunately, this has also given rise to cybercriminals and fraudsters who are constantly looking for vulnerabilities in these systems by deploying increasingly sophisticated fraud attacks. A typical fraud detection system employs standard supervised learning methods where the focus is on maximizing the fraud recall rate. However, we argue that such a formulation can lead to sub-optimal solutions. The design requirements for these fraud models requires that they are robust to the high-class imbalance in the data, adaptive to changes in fraud patterns, maintain a balance between the fraud rate and the decline rate to maximize revenue, and be amenable to asynchronous feedback since usually there is a significant lag between the transaction and the fraud realization. To achieve this, we formulate fraud detection as a sequential decision-making problem by including the utility maximization within the model in the form of the reward function. The historical decline rate and fraud rate define the state of the system with a binary action space composed of approving or declining the transaction. In this study, we primarily focus on utility maximization and explore different reward functions to this end. The performance of the proposed Reinforcement Learning system has been evaluated for two publicly available fraud datasets using Deep Q-learning and compared with different classifiers. We aim to address the rest of the issues in future work. CCS CONCEPTS• Applied computing → Online banking; Secure online transactions; Online shopping.

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“…In [197], the connection between deep AEs and Singular Value Decomposition (SVD) were discussed and compared using stocks from iShares Nasdaq Biotechnology ETF (IBB) index and the stock of Amgen Inc. Bouchti et al [198] explained the details of DRL and mentioned that DRL could be used for fraud detection/risk management in banking.…”

Section: Theoretical or Conceptual Studiesmentioning

confidence: 99%

Deep Learning for Financial Applications : A Survey

Özbayoğlu¹,

Gudelek²,

Sezer³

2020

Preprint

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Computational intelligence in finance has been a very popular topic for both academia and financial industry in the last few decades. Numerous studies have been published resulting in various models. Meanwhile, within the Machine Learning (ML) field, Deep Learning (DL) started getting a lot of attention recently, mostly due to its outperformance over the classical models. Lots of different implementations of DL exist today, and the broad interest is continuing. Finance is one particular area where DL models started getting traction, however, the playfield is wide open, a lot of research opportunities still exist. In this paper, we tried to provide a state-of-the-art snapshot of the developed DL models for financial applications, as of today. We not only categorized the works according to their intended subfield in finance but also analyzed them based on their DL models. In addition, we also aimed at identifying possible future implementations and highlighted the pathway for the ongoing research within the field.

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Fraud detection in banking using deep reinforcement learning

Cited by 16 publications

References 13 publications

Fraud Detection Using the Fraud Triangle Theory and Data Mining Techniques: A Literature Review

Fraud Detection Using the Fraud Triangle Theory and Data Mining Techniques: A Literature Review

Application of Deep Reinforcement Learning to Payment Fraud

Deep Learning for Financial Applications : A Survey

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