Machine Learning in Finance-Emerging Trends and Challenges

TL;DR

This paper discusses the growing integration of machine learning and AI in finance, highlighting recent trends, challenges, and barriers faced by organizations in adopting these advanced technologies for operational and business solutions.

Contribution

It provides an overview of emerging trends and identifies key challenges and barriers in implementing machine learning and AI in the financial services sector.

Findings

Organizations are increasingly adopting ML and AI in finance.

Challenges include data quality, regulatory issues, and model interpretability.

Barriers hinder widespread adoption of AI in financial operations.

Abstract

The paradigm of machine learning and artificial intelligence has pervaded our everyday life in such a way that it is no longer an area for esoteric academics and scientists putting their effort to solve a challenging research problem. The evolution is quite natural rather than accidental. With the exponential growth in processing speed and with the emergence of smarter algorithms for solving complex and challenging problems, organizations have found it possible to harness a humongous volume of data in realizing solutions that have far-reaching business values. This introductory chapter highlights some of the challenges and barriers that organizations in the financial services sector at the present encounter in adopting machine learning and artificial intelligence-based models and applications in their day-to-day operations.