Authors: Ganesh Racha
Abstract: Financial technology is advancing rapidly, especially now since standard credit scoring methods are becoming obsolete. With scoring methods being archaic and out of touch, countless valuable behavioral data are not captured. In this study, the author discusses how possible behavioral data can be found in financial and transaction data using an AI-powered financial insight engine. It aims to change the predict and prescriptive analytics to enhance the better credit decision processes, beyond the usual finance means. Rather than referring to historical financial data and comparing it, behavioral data that is not ordinary are looked into particularly in expenditure. The result is a changing credit score that is indicative of the dynamic character of credit management. The use of advanced machine learning methods like Random Forest, Neural Networks and Gradient Boosting are remarkable in evaluating the above standard behavioral data and relationships, which are usually deemed to be irrelevant. The experimental results show that these models compared with other traditional methods like Logistic Regression are more accurate, precise and has better recall and score f1. In addition, the analysis of spending behavior has been integrated to introduce common financial user behavioral patterns and improve risk assessment and measurement of financial stability. An improved system demo is integrated that use cases widely for companies and banks. To similar how the companies were formed with tech such as Netflix, Samsung, Google and Uber changing the algorithm of credit check by enhancing AI algorithms along with blockchain records based validation, are used to analyse paticipants in this open eco-system.
DOI: https://doi.org/10.5281/zenodo.19479408
