Explainable AI for Personalized Financial Advice: Building Trust and Transparency in Robo-Advisory Platforms

EasyChair Preprint 14333

Abstract

In the evolving landscape of financial services, robo-advisory platforms have emerged as powerful tools, providing automated, algorithm-driven financial advice with minimal human intervention. However, the black-box nature of many AI algorithms poses significant challenges to trust and transparency, crucial elements for client acceptance and regulatory compliance. This paper explores the integration of Explainable AI (XAI) into robo-advisory platforms to enhance personalized financial advice. By employing XAI techniques, we aim to demystify AI decision-making processes, offering clear and interpretable insights into how recommendations are generated. This transparency is essential for building client trust, enabling users to understand and validate the advice given, thereby fostering a more engaging and reliable advisory experience. Additionally, XAI facilitates compliance with financial regulations that require clarity in automated decision-making. Through case studies and technical evaluations, this paper demonstrates the efficacy of XAI in improving user satisfaction and regulatory adherence, ultimately advocating for its broader adoption in the financial advisory sector. By making AI-driven advice more accessible and understandable, we pave the way for a more inclusive and trustworthy financial ecosystem.

Keyphrases: Artificial Intelligence (AI), Explainable AI (XAI), assets under management (AUM)

@booklet{EasyChair:14333,
  author    = {Abi Litty},
  title     = {Explainable AI for Personalized Financial Advice: Building Trust and Transparency in Robo-Advisory Platforms},
  howpublished = {EasyChair Preprint 14333},
  year      = {EasyChair, 2024}}