Artificial intelligence (AI) can play a critical role in fraud management by accurately detecting fraud. AI technologies, such as machine learning (ML) algorithms, can analyze substantial data and detect anomalies and patterns that indicate fraudulent activities. This article discusses the role of AI in fraud detection and recent developments in this field.
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Importance of AI in Fraud Detection
The advent of AI and ML has created new opportunities to detect fraud more effectively. Fraud management systems powered by AI can prevent and identify different types of fraud, including phishing attacks, payment fraud, and credit card transaction fraud.
These systems can also learn from and adapt to new fraud trends and patterns to improve their detection ability over time. AI-based fraud detection solutions can be integrated with other security systems, such as biometric authentication and identity verification, to adopt a comprehensive fraud prevention approach.
Using AI in fraud detection can lead to more efficient, accurate, and faster fraud detection without affecting the customer experience. AI algorithms can enable organizations to monitor real-time transactions to immediately detect and respond to potential fraud attempts.
Additionally, the learning feature of AI techniques can decrease the occurrence of false positives, a major challenge in fraud detection where legitimate transactions are erroneously labeled as fraudulent. Repetitive tasks in the fraud detection process, such as verifying identities and reviewing transactions, can be automated using AI algorithms to reduce the need for manual intervention.
Moreover, fraudulent activities can lead to significant reputational damage and financial losses for organizations. Thus, organizations can employ AI techniques to decrease the number of fraudulent cases to prevent these losses.
AI-based Strategies to Detect Fraud
Integrating Unsupervised and Supervised AI Algorithms: The rising adaptability and sophistication of organized crime techniques will significantly reduce the effectiveness of fraud defense approaches based on single analytics and necessitate the incorporation of combined unsupervised and supervised ML algorithms into comprehensive fraud defense techniques.
Supervised ML models are trained using substantial amounts of accurately labeled transactions, with every transaction classified as non-fraudulent or fraudulent. Thus, these models can uncover patterns that properly depict legitimate activity.
The accuracy of the supervised ML model primarily depends on the amount of appropriate, quality training data used to train the model. Unsupervised ML models are utilized to detect anomalous behavior without labeled transaction data. Self-learning must be used in this approach to identify patterns masked by standard analytics in the data.
Applied Behavioral Analytics: In behavioral analytics, ML forecasts and evaluates behavior across all parts of a transaction at the micro level. The data is analyzed to develop profiles explaining the behaviors of each smartphone, account, retailer, and customer.
These identities/profiles are updated continuously during every transaction, which allows for analytic feature computation that provides accurate projections of future behavior. The profiles cover both non-financial and financial transactions.
Non-financial transaction data includes recent password requests, duplicate card requests, and address changes. Data from financial transactions enables building patterns that display the average spending frequency of an individual, the hours and days when they typically transact, and the length of time between geographically distributed payment venues.
Thus, profiles are highly beneficial as they maintain a precise representation of activities, which can decrease transaction failures due to false positives. An effective corporate fraud solution must contain a set of modeling techniques and profiles that provide the data required to evaluate the transaction patterns in real time.
Model Creation Using Massive Datasets: The richness and volume of data substantially impact the ML algorithm's effectiveness. Thus, the prediction accuracy of ML algorithms can be improved by expanding the dataset utilized to generate the predictive characteristics in ML algorithms.
Similarly, an ML model can become more effective in fraud detection when they gain experience by collecting millions/billions of both unauthorized and genuine transaction examples. A better understanding and estimation of risks for each person can only be realized by analyzing a large volume of transactions.
Adaptive Analytics: Adaptive analytics systems can be used by fraud detection professionals to ensure more effective and prompt responses, specifically on marginal judgments involving a false positive activity and false negative activity, for continual improvement in detection performance.
These systems can increase their sensitivity to emerging fraud patterns by responding dynamically to newly established case dispositions, resulting in a more precise differentiation of frauds from non-frauds.
The outcome of an investigation into a transaction performed by an analyst to determine whether the transaction is fraudulent or valid is reported back into the system, which enables analysts to represent the fraud environment correctly, including the hidden fraud behaviors lying dormant for an extended period. Thus, applying adaptive modeling techniques can continuously update the weights of predictive parameters in fraud detection algorithms, improving fraud detection and limiting the emergence of new forms of fraud.
Challenges of Incorporating AI in Fraud Detection
An imbalanced dataset is a critical challenge that reduces the fraud detection accuracy of ML algorithms. Unbalanced data distribution arises when the number of actual fraud transactions is rare/extremely low out of all transactions.
Stable patterns and no systematic omission/manipulation of important data are crucial to ensure the effectiveness of ML algorithms. In multiple ML applications, parties cooperate to facilitate the learning of ML algorithms or remain neutral in the learning process. However, perpetrators of fraud can make efforts to prevent this learning to hinder effective fraud detection. For instance, they can open accounts in several financial institutions in various jurisdictions to prevent an effective network analysis.
Similarly, detecting accounting fraud using fraud detection algorithms is extremely difficult as the distinction between a creative, legitimate practice and a fraud cannot be ascertained easily. Firms committing accounting fraud tend to misstate accounting reports for several years until their fraud is detected. However, this serial fraud feature is not always considered during fraud detection model building.
Most of the existing models typically treat fraud committed by each firm in a year as an independent observation and avoid the time series dependence of serial fraud cases. The fraud detection models/algorithms can be biased and produce inaccurate results if the data used to train these models is biased.
Several AI algorithms cannot be interpreted easily, which increases the challenges of deciphering the outcomes of these algorithms, such as identifying the reasons that led them to label a specific transaction as potentially fraudulent. Explainable AI can partly address this lack of transparency of AI algorithms in decision-making by providing interpretable and clear explanations of the decision-making processes that can be understood by humans.
Recent Developments
In recent years, state-of-the-art (SOTA) ML methods and algorithms have been developed to detect credit card transaction fraud. These include risk-induced Bayesian inference bagging, hyper-heuristic evolutionary algorithm-based Bayesian network classifier (BNC), an approach based on balanced random forest and k-means, decision tree-based intuitionistic fuzzy logic, random forest classifier with derived features created from hidden Markov model (HMM), and prudential multiple consensus model that represents an ensemble of multi-layer perceptron, Gaussian naïve bayes, adaptive boosting, gradient boosting, and random forest.
In a study recently published in the journal Decision Support Systems, researchers proposed a novel deep boosting decision trees (DBDT) fraud detection algorithm based on neural networks and gradient boosting. Neural networks were embedded into gradient boosting to enhance its representation learning ability while maintaining interpretability.
Additionally, researchers proposed a compositional area under the curve (AUC) maximization approach to address the data imbalances at the algorithm level during the model training phase due to the rarity of detected fraud cases. Experiments on multiple real-life fraud detection datasets demonstrated that DBDT can improve detection performance significantly while maintaining good interpretability.
In another recent study published in The International Journal of Analytical And Experimental Modal Analysis, researchers developed Anti-fraud-Tensorlink4cheque (AFTL4C), a novel AI/ML-based solution, to detect cheque frauds in real time. The AFTL4C solution utilizes the generative adversarial network (GAN) technique to compare different factors on scanned cheque images to detect potential counterfeits during real-time transactions.
This ability of the model, coupled with its configurability and scalability, makes AFTL4C more accurate in detecting genuine behavior and can reduce false positives. Additionally, the solution can also accelerate cheque counterfeit verification, reduce cheque processing costs, and minimize the number of cheques requiring manual review.
References and Further Reading
Yadav, D. C., Uyyala, P. (2023). The advanced proprietary AI/ML solution as Anti-fraud- Tensorlink4cheque (AFTL4C) for Cheque fraud detection. The International journal of analytical and experimental modal analysis, 15, 1914 - 1921. https://www.researchgate.net/publication/371123538_The_advanced_proprietary_AIML_solution_as_Anti-fraud-_Tensorlink4cheque_AFTL4C_for_Cheque_fraud_detection
Xu, B., Wang, Y., Liao, X., Wang, K. (2023). Efficient fraud detection using deep boosting decision trees. Decision Support Systems, 114037. https://doi.org/10.1016/j.dss.2023.114037
Bao, Y., Hilary, G., Ke, B. (2020). Artificial Intelligence and Fraud Detection. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3738618
Alhaddad, M. M. (2018). Artificial Intelligence in Banking Industry: A Review on Fraud Detection, Credit Management, and Document Processing. ResearchBerg Review of Science and Technology, 2(3), 25–46. https://researchberg.com/index.php/rrst/article/view/37
Yazıcı, Y. (2020). Approaches to Fraud Detection on Credit Card Transactions using Artificial Intelligence Methods. Computer Science and Information Technology, 235-244. https://doi.org/10.5121/csit.2020.101018
Bassi, E. (2023). How is artificial intelligence used in fraud detection? [Online] (Accessed on 13 August 2023).