Optimization of Deep Learning-Based Model for Identification of Credit Card Frauds

Abstract

With the growth of the internet, the landscape of commercial transactions has undergone significant changes. The digital revolution has permeated various aspects of our daily lives, notably transforming the financial system through digital money transfers across global distances. Digital transactions, including online purchases, money transfers, banking, and investing, have become routine. This shift has provided many advantages and opened the door for illegal activities such as money laundering. This research aims to identify transaction patterns to help algorithms recognize and flag scammers. We propose a deep learning-based sequential model framework to optimize fraud and non-fraud transaction classification. Proposed approach incorporates methods of ensemble learning including Gradient Boost, Random Forest, Logistic Regression, and Voting Classifiers, along with hyperparameter tuning to prevent overfitting. To solve the dataset inconsistency common in credit card dataset, we used techniques like under-sampling and the Synthetic Minority Over-sampling Technique (SMOTE) with specific machine learning (ML) algorithms. Our methodology involves data pre-processing, feature engineering, model selection, and evaluation. We leveraged the computational power of Google Colab for efficient model training and testing. Temporal features were extracted to capture transaction patterns over time, such as frequency and timing, along with features based on transaction amounts, merchant types, and geographic locations to detect subtle anomalies indicative of fraud. These features were input into a supervised learning algorithm, resulting in a model significantly outperforming baseline methods. The model was tested using a sizable dataset of anonymized transactions made with credit cards, and its effectiveness was measured using metrics such as accuracy, precision, recall, and F1-score. While it cannot entirely prevent fraudulent transactions, it effectively manages false positives through careful review, achieving an accuracy of 99.59%, surpassing other innovative models.

Keywords

• Deep learning
• sequential model
• ensemble learning
• fraud detection