One Step Ahead Using Data Mining Techniques Loan Fraud Investigation/Analytics
Abstract
Background: loan fraud refers to any deceptive or dishonest activity to obtaining or misusing a loan it involves intentionally providing false information or manipulating the loan process for personal gain. Loan fraud is illegal and can have serious consequences, including financial loss, damaged credit, and potential legal action. Lenders employ various measures, such as background checks, verification processes, and fraud detection systems, to mitigate the risks of loan fraud.
Many businesses across various industries have expressed worry about loan fraud; this causes billion- rupees losses every year. Thus, in order to deal with this ongoing and expanding issue, firms use data mining approaches. The purpose of this paper is to analyze studies that have been done in the last ten years to identify financial fraud using data mining techniques and to inform academic researchers and business professionals of the latest developments.
Financial organizations place a high value on loan fraud detection because of the losses that may be prevented by using an effective fraud detection system. This is a topic that is regularly brought up in scientific study.
Objective: The aim of this research is to describe a machine learning method to loan identification of fraud using data mining techniques while comparing a machine learning algorithm using statistical frameworks in fraud detection.
Methods: The study suggests a method for enhancing performance through machine learning and data mining approaches. The writers employed two datasets for the training phase. The first set of data is unprocessed, whereas the second set has been pre-processed using techniques for feature engineering and selection. All classifiers' performance significantly improved after being evaluated on pre-processed data, according to the results.
Result: Finally, they draw the conclusion that, when compared to machine algorithms for learning, using machine learning algorithms straight to raw data produces poor results for statistical models, and that the suggested approach—which makes use of feature design and selection techniques—helps to enhance performance. A variety of financial applications, including credit cards and health insurance, were found to have fraud using data mining techniques.
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