The Limits of AI in Financial Services
- URL: http://arxiv.org/abs/2503.22035v1
- Date: Thu, 27 Mar 2025 23:04:11 GMT
- Title: The Limits of AI in Financial Services
- Authors: Isabella Loaiza, Roberto Rigobon,
- Abstract summary: AI is transforming industries, raising concerns about job displacement and decision making reliability.<n>EPOCH framework highlights five irreplaceable human capabilities: Empathy, Presence, Opinion, Creativity, and Hope.<n>Challenge is ensuring professionals adapt, leveraging AI's strengths while preserving essential human capabilities.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: AI is transforming industries, raising concerns about job displacement and decision making reliability. AI, as a universal approximation function, excels in data driven tasks but struggles with small datasets, subjective probabilities, and contexts requiring human judgment, relationships, and ethics.The EPOCH framework highlights five irreplaceable human capabilities: Empathy, Presence, Opinion, Creativity, and Hope. These attributes are vital in financial services for trust, inclusion, innovation, and consumer experience. Although AI improves efficiency in risk management and compliance, it will not eliminate jobs but redefine them, similar to how ATMs reshaped bank tellers' roles. The challenge is ensuring professionals adapt, leveraging AI's strengths while preserving essential human capabilities.
Related papers
- Agentic AI: Autonomy, Accountability, and the Algorithmic Society [0.2209921757303168]
Agentic Artificial Intelligence (AI) can autonomously pursue long-term goals, make decisions, and execute complex, multi-turn.
This transition from advisory roles to proactive execution challenges established legal, economic, and creative frameworks.
We explore challenges in three interrelated domains: creativity and intellectual property, legal and ethical considerations, and competitive effects.
arXiv Detail & Related papers (2025-02-01T03:14:59Z) - Complement or substitute? How AI increases the demand for human skills [0.0]
This paper examines whether artificial intelligence (AI) acts as a substitute or complement to human labour.<n>It draws on 12 million online job vacancies from the United States spanning 2018-2023.<n>Results show that AI-focused roles are nearly twice as likely to require skills like resilience, agility, or analytical thinking.
arXiv Detail & Related papers (2024-12-27T17:26:30Z) - Follow the money: a startup-based measure of AI exposure across occupations, industries and regions [0.0]
Existing measures of AI occupational exposure focus on AI's theoretical potential to substitute or complement human labour on the basis of technical feasibility.
We introduce the AI Startup Exposure (AISE) index-a novel metric based on occupational descriptions from O*NET and AI applications developed by startups.
Our findings suggest that AI adoption will be gradual and shaped by social factors as much as by the technical feasibility of AI applications.
arXiv Detail & Related papers (2024-12-06T10:25:05Z) - Imagining and building wise machines: The centrality of AI metacognition [78.76893632793497]
We argue that shortcomings stem from one overarching failure: AI systems lack wisdom.
While AI research has focused on task-level strategies, metacognition is underdeveloped in AI systems.
We propose that integrating metacognitive capabilities into AI systems is crucial for enhancing their robustness, explainability, cooperation, and safety.
arXiv Detail & Related papers (2024-11-04T18:10:10Z) - Engineering Trustworthy AI: A Developer Guide for Empirical Risk Minimization [53.80919781981027]
Key requirements for trustworthy AI can be translated into design choices for the components of empirical risk minimization.
We hope to provide actionable guidance for building AI systems that meet emerging standards for trustworthiness of AI.
arXiv Detail & Related papers (2024-10-25T07:53:32Z) - Trustworthy and Responsible AI for Human-Centric Autonomous Decision-Making Systems [2.444630714797783]
We review and discuss the intricacies of AI biases, definitions, methods of detection and mitigation, and metrics for evaluating bias.
We also discuss open challenges with regard to the trustworthiness and widespread application of AI across diverse domains of human-centric decision making.
arXiv Detail & Related papers (2024-08-28T06:04:25Z) - AI Potentiality and Awareness: A Position Paper from the Perspective of
Human-AI Teaming in Cybersecurity [18.324118502535775]
We argue that human-AI teaming is worthwhile in cybersecurity.
We emphasize the importance of a balanced approach that incorporates AI's computational power with human expertise.
arXiv Detail & Related papers (2023-09-28T01:20:44Z) - AI Maintenance: A Robustness Perspective [91.28724422822003]
We introduce highlighted robustness challenges in the AI lifecycle and motivate AI maintenance by making analogies to car maintenance.
We propose an AI model inspection framework to detect and mitigate robustness risks.
Our proposal for AI maintenance facilitates robustness assessment, status tracking, risk scanning, model hardening, and regulation throughout the AI lifecycle.
arXiv Detail & Related papers (2023-01-08T15:02:38Z) - Trustworthy AI: A Computational Perspective [54.80482955088197]
We focus on six of the most crucial dimensions in achieving trustworthy AI: (i) Safety & Robustness, (ii) Non-discrimination & Fairness, (iii) Explainability, (iv) Privacy, (v) Accountability & Auditability, and (vi) Environmental Well-Being.
For each dimension, we review the recent related technologies according to a taxonomy and summarize their applications in real-world systems.
arXiv Detail & Related papers (2021-07-12T14:21:46Z) - Building Bridges: Generative Artworks to Explore AI Ethics [56.058588908294446]
In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society.
A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests.
This position paper outlines some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools.
arXiv Detail & Related papers (2021-06-25T22:31:55Z) - Trustworthy AI [75.99046162669997]
Brittleness to minor adversarial changes in the input data, ability to explain the decisions, address the bias in their training data, are some of the most prominent limitations.
We propose the tutorial on Trustworthy AI to address six critical issues in enhancing user and public trust in AI systems.
arXiv Detail & Related papers (2020-11-02T20:04:18Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.