Fraud in banks has been steadily growing over the past years and is a challenge to banks worldwide. The complexity involved in detection of such fraudulent activities further adds to the problem. A thorough examination of fraud and its possibilities is necessary to pinpoint and distinguish the few fraudulent cases within the vast volumes of banking data. In this paper we have discussed various ...

P2P lending directly connects borrowers and lenders without a financial institution as the intermediary. This new form of crowdfunding brings lenders more investment opportunities, but also poses unprecedented risks of default and fraud. This research-in-progress paper focuses on a specific type of fraud, loan request fraud, which may be unique to lenders on Chinese P2P lending sites due to the...

In recent years, with the rapid development of Internet technology, number credit card users has increased significantly. Subsequently, fraud caused a large amount economic losses to individual and related financial enterprises. At present, traditional machine learning methods (such as SVM, random forest, Markov model, etc.) have been widely studied in detection, but these are often difficulty ...

In the United States, when a consumer presents a payment to a merchant, the merchant typically makes a request for authorization before accepting the payment. Personal information, such as an account number, address, or telephone number, is often enough to initiate a payment. A serious weakness of this system is that criminals who obtain the correct personal information can impersonate an hones...

This paper proposes a method called autoencoder with probabilistic LightGBM (AED-LGB) for detecting credit card frauds. deep learning-based AED-LGB algorithm first extracts low-dimensional feature data from high-dimensional bank using the characteristics of an which has symmetrical network structure, enhancing ability representation learning. The fraud dataset comes real anonymized by and is hi...

Loan fraud is a critical factor in the insolvency of financial institutions, so companies make an effort to reduce the loss from fraud by building a model for proactive fraud prediction. However, there are still two critical problems to be resolved for the fraud detection: (1) the lack of cost sensitivity between type I error and type II error in most prediction models, and (2) highly skewed di...