AI and Machine Learning for Predictive Banking: Infrastructure Challenges and Opportunities

Bharath Somu

Abstract: The volumes of data are unprecedented, both in numbers and in the variety of their formats. Such a vast amount of data has opened up many opportunities in many fields including finance. In the financial services space, the rise of high-frequency trading accounts for the explosion of data combining price, liquidity, and news indicators reserves to explore trading opportunities. On a longer horizon, the aggregation of individual investor behavior and their sentiment in association with liquidity, volatility and expected returns constitutes a potential pool for risk assessment, equity market studies, and financial marketing. Such big data also bring before any analyst a serious challenge, namely the question of how to process, analyze and extract useful information out of it in real-time at some point. A line of answers to this pressing question comes from the area of Machine Learning (ML) and more generally, Artificial Intelligence (AI) [1]. Banks face profound challenges from the new competitive landscape and the abundance of financial technology (fintech) businesses. Traditional financial institutions have become the victims of these ?fast wolves,? shrinking profit margins and losing clients. No matter what kind of bank it is, changes are inevitable. Banks are eager to boost the development of intelligence-related goods and services to deal with external competitiveness and the demand for internal transformation. The Industrial and Information Technology Ministry has stated that the finance industry?s firms are encouraged to employ AI to improve digital levels [2]. Deep Learning (DL) has made tremendous achievements in image recognition, voice recognition, natural language processing, and many other fields related to social life. As the core technique for AI, researchers have been focusing their attention on Deep Neural Networks (DNNs) in data training. With the help of DNNs, a multitude of products have emerged. Commercial banks? data are continuous, dimension-large, and temporal-variability, which are more harmonious with the DL model. The complexity of the DL model affords commercial banks novel applications in the fields of risk management and intelligent services. Exploring the scenario application of DL technologies in financial businesses may become a sharp weapon for improving the intelligent service level.

Keywords: AI in Banking, Machine Learning, Predictive Banking, Infrastructure Challenges, Banking Technology, Data Analytics, Real-Time Insights, Scalable Infrastructure, Cloud Computing, Financial Forecasting, IT Infrastructure, Banking Innovation, Big Data in Finance, Digital Transformation, Risk Prediction, Personalized Banking, Operational Efficiency, FinTech Solutions, Smart Banking, AI Opportunities in Finance

How to Cite?: Bharath Somu, "AI and Machine Learning for Predictive Banking: Infrastructure Challenges and Opportunities", Volume 11 Issue 12, December 2022, International Journal of Science and Research (IJSR), Pages: 1441-1456, https://www.ijsr.net/getabstract.php?paperid=MS2212141805, DOI: https://dx.doi.org/10.21275/MS2212141805