Financial Fraud Detection in Banking Transactions

Abstract

The study focuses on detecting anomalies and fraudulent activities in banking transactions using a variety of machine learning and deep learning models. It utilizes a dataset, often referred to as the Financial Anomaly Detection dataset, which is typically sourced from a reliable dataset repository in either '.csv' or '.xlsx' format. This dataset contains transaction records that include features such as transaction amounts, timestamps, customer details, and other transactional metadata, making it ideal for identifying patterns that deviate from typical behavior. Detecting fraud and anomalies is crucial for enhancing the security of financial systems, and the models implemented in this study aim to address this challenge effectively. To achieve anomaly detection, the study employs a range of machine learning algorithms. One of the primary models used is the Random Forest algorithm, which is particularly effective in handling large datasets and complex feature interactions. Random Forest is trained to classify transactions as either legitimate or potentially fraudulent, based on historical patterns of known fraudulent transactions. Another key model utilized is the Isolation Forest, which is designed to identify anomalies by isolating outliers within the dataset. This model is well-suited for detecting rare, unusual, or malicious activity that deviates from the majority of transactions. In addition to traditional machine learning models, the study also explores the use of deep learning techniques, specifically Long Short-Term Memory (LSTM) networks. LSTMs are a type of recurrent neural network (RNN) that excels in capturing sequential patterns in time-series data, such as transactions over time. By analyzing sequences of transaction records, LSTMs are able to identify subtle patterns and predict anomalous or fraudulent transactions with a high level of accuracy. These deep learning models are particularly advantageous when dealing with large amounts of time-dependent transactional data, as they can retain long-term dependencies and capture complex relationships that simpler models might miss.