Strategic Deployment of Deep Learning Algorithms to Mitigate Fraud in Online Finance
Rishabh Sharma, Shikhar Gupta · 2024
Today, the continuously growing number of clients and transactions that involve financial services over the internet makes it extremely vulnerable to fraud-related hazards. The conventional techniques of fraud-fighting enable a certain degree of fraud check yet these strategies prove to be considerably bland in the fight against fraud. This study seeks to determine the applicability of deploying deep learning algorithms for improving fraud identification within the online finance industry. Using a range of paradigms, including CNNs, LSTMs, and autoencoders, we examined various highly descriptive models with an extensive dataset of financial transactions. Our results demonstrate that the LSTM model achieved the highest performance, with an accuracy of 95.8%, precision of 86.3%, recall of 79.4%, F1-score of 82.7%, and AUC-ROC of 0.936. The CNN model also performed well, attaining an accuracy of 95.2%, precision of 84.1%, recall of 77.8%, F1-score of 80.8%, and AUC-ROC of 0.924. The autoencoder effectively identified anomalies, achieving an accuracy of 94.8%, precision of 83.2%, recall of 76.5%, F1score of 79.7%, and AUC-ROC of 0.918. Comparative analysis with state-of-the-art methods highlighted the superiority of deep learning models in detecting fraudulent transactions. The study shows that machine learning algorithms can revolutionize the approaches to fraud detection while increasing the efficiency of the approaches dramatically. However, the following limitations are observed; these include the difficulties experienced in model interpretability and scalability for large data sets. This work advances the field of interactions by enhancing the design and implementation of intelligent systems for the identification of fraud attempts and their minimization in the future, thus minimizing the level of losses of online financial services and strengthening user confidence in secure and reliable online platforms. Therefore, it remained evident that such concerns need to be addressed to apply these sophisticated methods in real-life operational financial situations in future research.