The Role Of AI And Machine Learning In Financial Services: A Neural Networkbased Framework For Predictive Analytics And Customercentric Innovations

Abstract

In this time of rapid technological advancements, the financial services industry is undergoing significant transformation due to the explosive growth in data and advances in algorithms and computational processes. AI and machine learning technologies such as neural networks enable significant improvements in predictive analytics across traditional, alternative, and behavioral customer-related financial services, covering traditional offerings such as banking, lending, credit scoring, financial trading, asset management, wealth management, and insurance. This overview of predictive analytics in financial services embraces a new perspective that shifts the focus of predictive analytics-based AI and machine learning functions from being traditionally enterprise-centric to now more focused on being customer-centric, thus leading to customer-centric innovations within the financial services industry. Customers are the cornerstones of financial services firms, and being customer-centric leads to improved AI and machine learning models, which result in long-term customer satisfaction, customer loyalty, and emotional attachments.
Competition between financial services firms is fierce due to imperative requirements driven by disruptive innovations in technology and operational processes. However, financial services firms must balance their determination to grow and thrive with increasing regulatory scrutiny and more guaranteed consumer protections. AI and machine learning predictive technologies motivated by the explosive gains in digital data, cloud storage, and GPU-computing-based scalable platforms have become the de facto standard; they enable innovative financial services and their institutional providers that cater to retail and institutional clients. With existing neural network model-building tools achieving standing within the domain of financial services, an accelerated approach bridges the gap between bank management and data scientists through model interpretation tools, resulting in broader neural network models for predicting bank customers' behaviors targeted for internal decision-making as well as for satisfying regulatory requirements.