Citation :

Harish Janardhanan, 2024. "AI-Powered Fraud Detection: Leveraging Machine Learning to Combat Financial Crimes" ESP International Journal of Advancements in Computational Technology (ESP-IJACT)  Volume 2, Issue 3: 1-11.

Abstract :

Digital banking and other online-based financial services have nevertheless emerged to bring the most expensive and easy to use services to users globally. However, with the evolution of digital processing it has resulted in increased risks such as fraud, money laundering and identity theft hence requiring enhanced methods of fraud detection. The previous approaches to detecting fraud whereby an organization uses exclusionary checks and an individual answer to detect fraud are in most cases ineffective to deal with new styles of fraud by fraudsters. Thus, financial institutions are beginning to focus on utilizing advanced technologies such as Machine Learning (ML) and Artificial Intelligence (AI) to effectively address these new and more complex threats. This paper aims to discuss the idea of utilizing ML approaches in fraud detection of financial schemes with an evaluation of various algorithms on the usefulness of ML in detecting outliers in transaction analysis. Artificial intelligence is a broad category, one of its types is known as machine learning, where algorithms are fed data and learn to make decisions on their own based on past results. For the given problem of fraud detection, ML techniques can help in real-time processing of a large amount of transaction data and detect patterns or irregularities that point towards fraud. In contrast to other approaches the ML algorithms are not static and can be improved each day thus making them more accurate. This is especially useful in fighting fraud, as organizations must constantly change their strategies due to tactics employed by the criminals. Many types of ML approaches have been tested for fraud detection, and we study their features and drawbacks in this section. Some algorithms like decision trees for example are used because they are easy to implement and comprehend. They function by defining data branches in accordance with the feature value and decisions are made between nodes until the classification is complete. This method applies especially well in the case of two classes of problems, for example the problem of fraud detection where a transaction cans either be fraudulent or genuine. Nonetheless, the decision trees have the problem of overfitting where the model is good at predicting in the training data but has a poor outcome when tested on the unseen data. Neural networks, another variant of ML, have emerged as powerful tools in a variety of applications mainly due to their ability to solve sophisticated problems in pattern recognition.

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Keywords :

Fraud Detection, Machine Learning, Financial Crimes, Neural Networks, Decision Trees, Ensemble Methods.