Know Your Customer: Balancing Innovation and Regulation for Financial
Inclusion
- URL: http://arxiv.org/abs/2112.09767v2
- Date: Tue, 18 Oct 2022 16:31:59 GMT
- Title: Know Your Customer: Balancing Innovation and Regulation for Financial
Inclusion
- Authors: Karen Elliott, Kovila Coopamootoo, Edward Curran, Paul Ezhilchelvan,
Samantha Finnigan, Dave Horsfall, Zhichao Ma, Magdalene Ng, Tasos
Spiliotopoulos, Han Wu, Aad van Moorsel
- Abstract summary: We study how tension impacts the deployment of privacy-sensitive technologies aimed at financial inclusion.
We build and demonstrate a prototype solution based on open source decentralized identifiers and verifiable credentials software.
We consider the policy implications stemming from these tensions and provide guidelines for the further design of related technologies.
- Score: 8.657646730603098
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Financial inclusion depends on providing adjusted services for citizens with
disclosed vulnerabilities. At the same time, the financial industry needs to
adhere to a strict regulatory framework, which is often in conflict with the
desire for inclusive, adaptive, and privacy-preserving services. In this
article we study how this tension impacts the deployment of privacy-sensitive
technologies aimed at financial inclusion. We conduct a qualitative study with
banking experts to understand their perspectives on service development for
financial inclusion. We build and demonstrate a prototype solution based on
open source decentralized identifiers and verifiable credentials software and
report on feedback from the banking experts on this system. The technology is
promising thanks to its selective disclosure of vulnerabilities to the full
control of the individual. This supports GDPR requirements, but at the same
time, there is a clear tension between introducing these technologies and
fulfilling other regulatory requirements, particularly with respect to 'Know
Your Customer.' We consider the policy implications stemming from these
tensions and provide guidelines for the further design of related technologies.
Related papers
- In-House Evaluation Is Not Enough: Towards Robust Third-Party Flaw Disclosure for General-Purpose AI [93.33036653316591]
We call for three interventions to advance system safety.
First, we propose using standardized AI flaw reports and rules of engagement for researchers.
Second, we propose GPAI system providers adopt broadly-scoped flaw disclosure programs.
Third, we advocate for the development of improved infrastructure to coordinate distribution of flaw reports.
arXiv Detail & Related papers (2025-03-21T05:09:46Z) - Regulating Ai In Financial Services: Legal Frameworks And Compliance Challenges [0.0]
Article examines the evolving landscape of artificial intelligence (AI) regulation in financial services.
It highlights how AI-driven processes, from fraud detection to algorithmic trading, offer efficiency gains yet introduce significant risks.
The study compares regulatory approaches across major jurisdictions such as the European Union, United States, and United Kingdom.
arXiv Detail & Related papers (2025-03-17T14:29:09Z) - Data Traceability for Privacy Alignment [1.1970748626806043]
We offer a new privacy approach for the growing ecosystem of services dependent on personal data sharing between individuals and third parties.
We introduce the concept of covert-accountability, which addresses the risk from adversaries that may act dishonestly but nevertheless face potential identification and legal consequences.
We present the OTrace protocol, designed to provide traceable, accountable, consumer-control in third-party data sharing ecosystems.
arXiv Detail & Related papers (2025-03-12T20:42:23Z) - Data Sharing, Privacy and Security Considerations in the Energy Sector: A Review from Technical Landscape to Regulatory Specifications [49.567747749614924]
Decarbonization, decentralization and digitalization are the three key elements driving the twin energy transition.
This paper conducts a comprehensive review of the data-related issues for the energy system by integrating both technical and regulatory dimensions.
We classify the issues into three categories: (i) data-sharing among energy end users and stakeholders (ii) privacy of end users, and (iii) cyber security.
arXiv Detail & Related papers (2025-03-05T14:23:56Z) - FSCsec: Collaboration in Financial Sector Cybersecurity -- Exploring the Impact of Resource Sharing on IT Security [0.9374652839580183]
This research aims to provide insights that can help financial institutions make better decisions to protect.
By using simple theories to understand these factors, this research aims to provide insights that can help financial institutions make better decisions to protect.
arXiv Detail & Related papers (2024-10-19T20:03:27Z) - DPFedBank: Crafting a Privacy-Preserving Federated Learning Framework for Financial Institutions with Policy Pillars [0.09363323206192666]
This paper presents DPFedBank, an innovative framework enabling financial institutions to collaboratively develop machine learning models.
DPFedBank is designed to address the unique privacy and security challenges associated with financial data, allowing institutions to share insights without exposing sensitive information.
arXiv Detail & Related papers (2024-10-17T16:51:56Z) - Enhancing Financial Inclusion and Regulatory Challenges: A Critical Analysis of Digital Banks and Alternative Lenders Through Digital Platforms, Machine Learning, and Large Language Models Integration [0.0]
This paper explores the dual impact of digital banks and alternative lenders on financial inclusion and the regulatory challenges posed by their business models.
It discusses the integration of digital platforms, machine learning (ML), and Large Language Models (LLMs) in enhancing financial services accessibility for underserved populations.
arXiv Detail & Related papers (2024-04-18T05:00:53Z) - Shortchanged: Uncovering and Analyzing Intimate Partner Financial Abuse in Consumer Complaints [10.746634884866037]
Digital financial services can introduce new digital-safety risks for users, particularly survivors of intimate partner financial abuse (IPFA)
Drawing from a dataset of 2.7 million customer complaints, we implement a bespoke workflow that utilizes language-modeling techniques and expert human review to identify complaints describing IPFA.
Our contributions are twofold; we offer the first human-labeled dataset for this overlooked harm and provide practical implications for technical practice, research, and design for better supporting and protecting survivors of IPFA.
arXiv Detail & Related papers (2024-03-20T19:32:21Z) - Service Level Agreements and Security SLA: A Comprehensive Survey [51.000851088730684]
This survey paper identifies state of the art covering concepts, approaches, and open problems of SLA management.
It contributes by carrying out a comprehensive review and covering the gap between the analyses proposed in existing surveys and the most recent literature on this topic.
It proposes a novel classification criterium to organize the analysis based on SLA life cycle phases.
arXiv Detail & Related papers (2024-01-31T12:33:41Z) - The AI Revolution: Opportunities and Challenges for the Finance Sector [12.486180180030964]
The application of AI in the financial sector is transforming the industry.
However, along with these benefits, AI also presents several challenges.
These include issues related to transparency, interpretability, fairness, accountability, and trustworthiness.
The use of AI in the financial sector further raises critical questions about data privacy and security.
Despite the global recognition of this need, there remains a lack of clear guidelines or legislation for AI use in finance.
arXiv Detail & Related papers (2023-08-31T08:30:09Z) - Auditing and Generating Synthetic Data with Controllable Trust Trade-offs [54.262044436203965]
We introduce a holistic auditing framework that comprehensively evaluates synthetic datasets and AI models.
It focuses on preventing bias and discrimination, ensures fidelity to the source data, assesses utility, robustness, and privacy preservation.
We demonstrate the framework's effectiveness by auditing various generative models across diverse use cases.
arXiv Detail & Related papers (2023-04-21T09:03:18Z) - Advancing Differential Privacy: Where We Are Now and Future Directions for Real-World Deployment [100.1798289103163]
We present a detailed review of current practices and state-of-the-art methodologies in the field of differential privacy (DP)
Key points and high-level contents of the article were originated from the discussions from "Differential Privacy (DP): Challenges Towards the Next Frontier"
This article aims to provide a reference point for the algorithmic and design decisions within the realm of privacy, highlighting important challenges and potential research directions.
arXiv Detail & Related papers (2023-04-14T05:29:18Z) - Trustworthy AI Inference Systems: An Industry Research View [58.000323504158054]
We provide an industry research view for approaching the design, deployment, and operation of trustworthy AI inference systems.
We highlight opportunities and challenges in AI systems using trusted execution environments.
We outline areas of further development that require the global collective attention of industry, academia, and government researchers.
arXiv Detail & Related papers (2020-08-10T23:05:55Z) - Regulation conform DLT-operable payment adapter based on trustless -
justified trust combined generalized state channels [77.34726150561087]
Economy of Things (EoT) will be based on software agents running on peer-to-peer trustless networks.
We give an overview of current solutions that differ in their fundamental values and technological possibilities.
We propose to combine the strengths of the crypto based, decentralized trustless elements with established and well regulated means of payment.
arXiv Detail & Related papers (2020-07-03T10:45:55Z) - Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable
Claims [59.64274607533249]
AI developers need to make verifiable claims to which they can be held accountable.
This report suggests various steps that different stakeholders can take to improve the verifiability of claims made about AI systems.
We analyze ten mechanisms for this purpose--spanning institutions, software, and hardware--and make recommendations aimed at implementing, exploring, or improving those mechanisms.
arXiv Detail & Related papers (2020-04-15T17:15:35Z)
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.