Article
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Approximate Unlearning in Finance
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Version 1
: Received: 23 September 2024 / Approved: 24 September 2024 / Online: 24 September 2024 (04:06:07 CEST)
How to cite: Qi, C. Approximate Unlearning in Finance. Preprints 2024, 2024091840. https://doi.org/10.20944/preprints202409.1840.v1 Qi, C. Approximate Unlearning in Finance. Preprints 2024, 2024091840. https://doi.org/10.20944/preprints202409.1840.v1
Abstract
Approximate unlearning is an emerging field in machine learning that addresses the challenge of efficiently removing specific data points from models without the need for full retraining. In the financial sector, where models are built on sensitive customer data and market information, the ability to unlearn data is crucial for meeting privacy regulations like GDPR and ensuring model adaptability in real-time environments. This paper explores the categorization of approximate unlearning into two main approaches: data-driven approximation and model-driven approximation. Data-driven approximation focuses on selectively retraining parts of the dataset to simulate the removal of data, while model-driven approximation adjusts the model’s internal parameters to nullify the influence of unwanted data points. Both methods offer computational and memory-efficient ways to balance the need for privacy compliance with the performance demands of financial models. The paper discusses practical applications in algorithmic trading, fraud detection, and risk assessment, and highlights the challenges associated with each approach. Through case studies and literature references, we demonstrate how approximate unlearning can be applied to maintain system efficiency, data privacy, and regulatory compliance in the financial domain. This research provides a roadmap for future developments in approximate unlearning, particularly in the context of real-time financial systems and federated learning environments.
Keywords
Approximate Unlearning; Machine Learning; Deep Learning; Digital Asset; Cryptocurrency
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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