A FEDERATED LEARNING PERSPECTIVE IN AI-DRIVEN ANALYSIS

Artificial intelligence (AI) systems are the major components that the core financial activities are relying on. These
are mainly consumer lending and credit scoring, fraud detection, and algorithmic trading. To a great extent, these
models are said to be able to perform decisions rapidly and to increase the efficiency of the processes, however, they
also have the ability to inherit and in consequence to increase the biases, from the training data they are fed. Moreover,
the healthcare sector has been investigating the concept of federated learning, a privacy-preserving paradigm in which
organizations can train a model together without sharing raw data. Financial institutions can use Federated approaches
as a powerful tool to see through data-related bias while they are abiding by strict privacy rules.
This paper locates bias sources in AI-driven finance, studies credit scoring, fraud detection, and algorithmic trading
scenarios, weighs the effects on equity and consumer trust, and considers the strategies including federated learning
that could be used for bias removal. A comparison chart of fairness metrics such as statistical parity, equal opportunity,
and equalized odds is introduced. Figures indicate the stages of federated learning and the compromises involved in
the selection of fairness metrics. The article finishes with pointing the next steps in research that would facilitate
responsible AI in finance and, additionally, the significance of ethical governance and the healthcare sector's crosssector lessons.