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Privacy-Enhanced Machine Learning Algorithms for Financial Services
Subject area: Science,Engineering and Technology · Area of research: Privacy-Enhanced Machine Learning in Finance
Abstract
The increasing reliance on machine learning (ML) in financial services for fraud detection, risk assessment, and personalized banking introduces significant privacy and security challenges. Traditional ML models operate on centralized financial data, making them susceptible to cyber threats, data breaches, and regulatory non-compliance. Privacy-enhancing machine learning (PEML) techniques, including differential privacy, homomorphic encryption, and federated learning, offer solutions by allowing financial institutions to leverage AI-driven insights while maintaining data confidentiality and regulatory compliance. This paper explores the strengths, weaknesses, and use cases of various privacy-preserving ML methods and examines their role in secure cross-institutional data collaboration. Additionally, emerging trends such as blockchain-integrated identity verification, quantum-safe encryption, and AI-driven compliance automation are analyzed to highlight the future direction of privacy-enhanced AI in financial services. Despite their advantages, PEML techniques face challenges related to scalability, computational overhead, and adversarial security risks, necessitating further research and regulatory standardization. By implementing privacy-focused AI solutions, financial institutions can achieve a balance between innovation, security, and ethical data governance, ensuring a more resilient and transparent financial ecosystem.
Keywords
Privacy-preserving machine learning, financial data security, federated learning, homomorphic encryption and AI-driven compliance.
How to cite this paper
@article{1708631,
author = {Felix Amakye, Cleopatra U. Douglas, Muhammed Raji Moshood},
title = {Privacy-Enhanced Machine Learning Algorithms for Financial Services},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {8},
number = {11},
pages = {1555-1566},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1708631.pdf},
abstract = {The increasing reliance on machine learning (ML) in financial services for fraud detection, risk assessment, and personalized banking introduces significant privacy and security challenges. Traditional ML models operate on centralized financial data, making them susceptible to cyber threats, data breaches, and regulatory non-compliance. Privacy-enhancing machine learning (PEML) techniques, including differential privacy, homomorphic encryption, and federated learning, offer solutions by allowing financial institutions to leverage AI-driven insights while maintaining data confidentiality and regulatory compliance. This paper explores the strengths, weaknesses, and use cases of various privacy-preserving ML methods and examines their role in secure cross-institutional data collaboration. Additionally, emerging trends such as blockchain-integrated identity verification, quantum-safe encryption, and AI-driven compliance automation are analyzed to highlight the future direction of privacy-enhanced AI in financial services. Despite their advantages, PEML techniques face challenges related to scalability, computational overhead, and adversarial security risks, necessitating further research and regulatory standardization. By implementing privacy-focused AI solutions, financial institutions can achieve a balance between innovation, security, and ethical data governance, ensuring a more resilient and transparent financial ecosystem.},
keywords = {Privacy-preserving machine learning, financial data security, federated learning, homomorphic encryption and AI-driven compliance.},
month = {May},
}