AI/ML-Based Retail Banking Transactions Forecast Application using Complex Neural Networks Optimization Algorithm

Abstract

The study is based on the AI and ML-based retail banking transaction forecast application which includes different models and processes. AI and ML have changed the whole banking industry with its effects. AI helps the banking industry to become more efficient by detecting fraud, quicker response, and customer services and many more. Models such as RNN and LSTM have helped in predicting transactions by analysing the patterns and trade. Different case studies have been discussed that entail the use of AI, ML, and complex models such as RNN and LSTM, which improve the efficiency of the industry.