ADVANCED NEURAL NETWORK ARCHITECTURE FOR DETECTING FRAUD IN INTERNET LOAN APPLICATIONS

Abstract

The rise of digital technology and online transactions has led to an increase in various types of fraud, especially in the financial sector. Internet loans, being a convenient way for people to access quick financial assistance, have also become a target for fraudulent activities. Traditional fraud detection systems typically rely on rule-based methods and statistical models. Rule-based systems use predefined rules to flag transactions that match specific patterns associated with fraud. Statistical models, such as logistic regression, analyze historical transaction data to identify anomalies. While these methods have been useful, they often struggle with detecting complex, non-linear patterns that are characteristic of fraud in internet loan applications. Therefore, it is necessary to combat fraudulent activities effectively and efficiently. Detecting fraud in internet loan applications is crucial for financial institutions to maintain trust, reduce financial losses, and comply with regulatory requirements. Deep learning, a subset of artificial intelligence (AI), has shown great promise in enhancing fraud detection capabilities due to its ability to analyze large volumes of data and identify complex patterns. These models offer advanced techniques to process vast amounts of data, enabling the identification of subtle and sophisticated fraud patterns that might be undetectable by traditional methods. Thus, this research develops a deep learning anti-fraud model for Internet loan applications, which includes improving model accuracy through advanced neural network architectures, enhancing real-time processing capabilities, integrating explainable AI techniques for better transparency, and leveraging unsupervised learning methods for detecting previously unknown fraud patterns. Additionally, the future lies in collaborative efforts between data scientists, cybersecurity experts, and financial institutions to stay ahead of fraudsters and create a secure digital lending environment.