Neural Network Algorithm in Financial Services: The Effect of Fraud Risk Management and Fraud Prevention

The current financial crisis has made it such that microfinance institutions, in their fight for self-sufficiency, financial sustainability, and survival, cannot afford to ignore fraud risk management strategies. Many people throughout the world who are struggling financially would not have the means to obtain financial services if it weren't for microfinance institutions. This proposed system is a theoretical investigation on how microfinance institutions in Ghana might effectively manage the risk of fraud. By surveying and evaluating fraud risk management experts' and researchers' perspectives, the study takes an experimental approach. There are three steps to the suggested technique, which is preprocessing, feature selection, and training the model. The following procedures comprise data preprocessing, which aim to improve data quality and guarantee model performance: The following methods are employed: hybrid sampling strategy, outlier detection, duplicate removal, multiple imputation. Machine learning, pattern recognition, and statistics are just a few of the fields that have done extensive research on the feature selection problem. The proposed approach used the CNN-BiGRU to train the model. Compared to state-of-the-art alternatives like CNN and BiGRU, the proposed model obtains an average accuracy rate of 95.19%.