STLGOct 8, 2021

Machine Learning in Finance-Emerging Trends and Challenges

arXiv:2110.11999v16 citations
Originality Synthesis-oriented
AI Analysis

It addresses adoption issues for financial sector organizations, but is incremental as it reviews existing trends without new solutions.

This introductory chapter discusses the challenges and barriers financial services organizations face in adopting machine learning and AI models for daily operations, without presenting specific results or numbers.

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.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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