Combatting Digital Financial Fraud through Strategic Deep Learning Approaches

Tremendous growth of electronic finance arrived concurrently with financial fraud, in turn, requiring the adoption of altered methods of fraud detection. This research work is concerned with the comparative effectiveness of machine learning and deep learning models, crafting Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) as an illustration, in upgrading fraud detection power within digital finance infrastructures. Utilizing a rich database of transactional data, both supervised and unsupervised learning tools are employed to establish the suspicious ones. This methodology consists of data cleaning using automatic approaches, machine learning algorithms such as CNNs and RNNs for modeling, and key metrics that involve accuracy, sensitivity, specificity, AUC, and ROC curves when evaluating the model. The analysis shows, that the RNN architecture performs even better than the CNN model observing an incredible accuracy of 95.8%, sensitivity of 93.7%, and specificity of 97. 5 % with an AUC of 0. 972. Besides, analysis showed that the models consistently performed well across various transaction amounts, indicating robustness and applicability in various situations. This underlines the fact that deep learning models are most effective when dealing with the occurrences of financial transactions that are fraudulent. Financial institutions as well as other entities that accept money can enjoy the advantages that the use of highly developed analytical tools provides for instance in fraud prevention and this is happening in the era of rapidly inflating financial crime. The next steps for research projects can be provided by development of the integration of unsupervised learning models and real-time fraud detection systems through which the next generation of outstanding fraud detection systems will be created.