Predictive Machine Learning Models for Financial Fraud Detection Leveraging Big Data Analysis
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V4I1P105Keywords:
Big Data Analytics, Financial Fraud, Fraud Detection, Machine Learning (ML), Predictive Analytics, Fraud Prevention, Transaction DataAbstract
Maintaining the integrity of financial systems and preventing people and organizations from suffering financial losses depends heavily on the ability to spot fraudulent financial transactions. Recognizing complicated patterns and developing fraud methods is a challenge for conventional rule-based fraud detection methods. Using the Credit Card Fraud Detection (CCFD) dataset, this research aims to compare and analyze different prediction models to accurately identify fraudulent transactions. The anonymized dataset was preprocessed thoroughly, i.e., missing values, outlier elimination, min-max normalization, and relevant feature selection were addressed. A modified Deep Neural Network (DNN) model, Multi-Layer Perceptron (MLP), Naive Bayes (NB), as well as Decision Tree (DT), were among the classification models that were trained and evaluated. The suggested DNN model performed better than the others, achieving 99.89% accuracy, a 99.87% F1-score, 99.99% recall, as well as 99% precision. These results demonstrate that deep learning, when combined with an efficient preprocessing pipeline, may greatly improve fraud detection in extremely unbalanced financial data, and they also show that the DNN model is capable of learning complicated, non-linear patterns in the data
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