Abstract:

The proliferation of digital technology and online transactions has resulted in a surge in diverse fraud forms, particularly within the financial sector. Internet loans, although providing simple access to rapid financial aid, have also grown susceptible to fraudulent activity. Conventional fraud detection systems often depend on rule-based approaches and statistical models. Rule-based systems employ established criteria to identify transactions that correspond to particular patterns linked to fraud. Statistical methods, such logistic regression, examine previous transaction data to detect abnormalities. Although these algorithms have proven beneficial, they frequently encounter difficulties in identifying intricate, non-linear patterns typical of fraud in online loan applications. Consequently, it is imperative to address fraudulent actions with efficacy and efficiency. Identifying fraud in online loan applications is essential for financial organizations to uphold confidence, mitigate financial losses, and adhere to regulatory mandates. Deep learning, a branch of artificial intelligence (AI), has demonstrated significant potential in improving fraud detection due to its capacity to examine extensive datasets and recognize intricate patterns. These models employ advanced methodologies to analyze extensive datasets, facilitating the detection of nuanced and intricate fraud patterns that may elude conventional approaches. This research formulates a deep learning anti-fraud model for online loan applications, focusing on augmenting model accuracy via sophisticated neural network architectures, enhancing real-time processing capabilities, incorporating explainable AI techniques for improved transparency, and utilizing unsupervised learning methods to identify previously unrecognized fraud patterns. Furthermore, the future depends on the coordinated endeavors of data scientists, cybersecurity specialists, and financial institutions to outpace fraudsters and establish a safe digital lending landscape.

Description:

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