Utilization of AI for Streamlining and Optimizing Credit Decision Process and Security Access Loan Risks in the Banking Sector

The rapid growth of credit card service in banking is a challenge because of user level decision to provide loan risk. More specific real-time data analysis through Artificial Intelligence (AI), banking, and malware penetration application data analysis through other areas needs security identification. The efficiency gain of the method has been verified by experiments on real data sets from commercial banks. Representative behavior models can help improve online banking credit fraud detection performance significantly. Previous approaches of individual and population models find the single level of detecting fraud deposits, access levels, loan deployment etc. It will not efficient in containing customer behavior faced with online payment fraud detection. The proposed TRUBSV rule-based fraud detection is carried out by a precision algorithm and behavior-based group-level or general-level calculation system to provide loans. The recommended method is to evaluate by doing behavioral factors depends on security analysis in user level to provide loan. It can be easily discovered that the original and fraudulent transactions. Differential analysis is used to gather Group level evidence where fraud indicates significant deviations from normal behavior. Evaluating fraud possibilities is a fraud detection method based on effective identification by tracking the number of different accounts, accessing our main contributing accounts, and accessing each other's devices. Provide a systematic and detailed overview of these issues and challenges that may disrupt Target Request Based User Behavioral Session Verification (TRUBSV). The TRUBSVs promising for the e-commerce system. These e-commerce systems have been introduced closely to the types of fraud that are widely used. Most of the e-commerce systems have been able to process financial transactions. Behavior-based methods are considered a promising method for online payment fraud detection. It is to develop a high-resolution behavior model using the details of low-quality behavior.