Practices, Challenges, and Opportunities When Inferring Requirements From Regulations in the FinTech Sector - An Industrial Study
- URL: http://arxiv.org/abs/2405.02867v1
- Date: Sun, 5 May 2024 09:39:08 GMT
- Title: Practices, Challenges, and Opportunities When Inferring Requirements From Regulations in the FinTech Sector - An Industrial Study
- Authors: Parisa Elahidoost, Daniel Mendez, Michael Unterkalmsteiner, Jannik Fischbach, Christian Feiler, Jonathan Streit,
- Abstract summary: Understanding and interpreting regulatory norms and inferring software requirements from them is a critical step towards regulatory compliance.
This study investigates the complexities of requirement engineering in regulatory contexts, pinpointing various issues and discussing them in detail.
We have identified key practices for managing regulatory requirements in software development, and have pinpointed several challenges.
- Score: 1.0936851319953484
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: [Context and motivation]: Understanding and interpreting regulatory norms and inferring software requirements from them is a critical step towards regulatory compliance, a matter of significant importance in various industrial sectors. [Question/ problem]: However, interpreting regulations still largely depends on individual legal expertise and experience within the respective domain, with little to no systematic methodologies and supportive tools to guide this practice. In fact, research in this area is too often detached from practitioners' experiences, rendering the proposed solutions not transferable to industrial practice. As we argue, one reason is that we still lack a profound understanding of industry- and domain-specific practices and challenges. [Principal ideas/ results]: We aim to close this gap and provide such an investigation at the example of the banking and insurance domain. We conduct an industrial multi-case study as part of a long-term academia-industry collaboration with a medium-sized software development and renovation company. We explore contemporary industrial practices and challenges when inferring requirements from regulations to support more problem-driven research. Our study investigates the complexities of requirement engineering in regulatory contexts, pinpointing various issues and discussing them in detail. We highlight the gathered insights and the practical challenges encountered and suggest avenues for future research. [Contribution]: Our contribution is a comprehensive case study focused on the FinTech domain, offering a detailed understanding of the specific needs within this sector. We have identified key practices for managing regulatory requirements in software development, and have pinpointed several challenges. We conclude by offering a set of recommendations for future problem-driven research directions.
Related papers
- A Survey on Large Language Models for Critical Societal Domains: Finance, Healthcare, and Law [65.87885628115946]
Large language models (LLMs) are revolutionizing the landscapes of finance, healthcare, and law.
We highlight the instrumental role of LLMs in enhancing diagnostic and treatment methodologies in healthcare, innovating financial analytics, and refining legal interpretation and compliance strategies.
We critically examine the ethics for LLM applications in these fields, pointing out the existing ethical concerns and the need for transparent, fair, and robust AI systems.
arXiv Detail & Related papers (2024-05-02T22:43:02Z) - On Developing an Artifact-based Approach to Regulatory Requirements Engineering [18.256422026527986]
Regulatory acts are a challenging source when eliciting, interpreting, and analyzing requirements.
No existing approach considers explicating and managing legal domain knowledge and engineering-legal coordination.
We introduce the first version of our Artifact Model for Regulatory Requirements Engineering (AM4RRE) and its conceptual foundation.
arXiv Detail & Related papers (2024-05-01T09:51:56Z) - Accelerating Radio Spectrum Regulation Workflows with Large Language Models (LLMs) [0.0]
This paper demonstrates example applications of Large Language Models (LLMs) to expedite spectrum regulatory processes.
We explore various roles that LLMs can play in this context while identifying some of the challenges to address.
arXiv Detail & Related papers (2024-03-26T15:54:48Z) - Designing NLP-based solutions for requirements variability management:
experiences from a design science study at Visma [4.063380369801306]
This experience report outlines the insights gained from applying design science in requirements engineering research in industry.
We show and evaluate various strategies to tackle the issue of requirement variability.
arXiv Detail & Related papers (2024-02-11T10:12:01Z) - Industrial Challenges in Secure Continuous Development [0.7734726150561089]
The intersection between security and continuous software engineering has been of great interest since the early years of the agile development movement.
This paper summarizes a relevant part of our endeavors in which we validated challenges with several practitioners of different roles.
More than framing a set of challenges, we conclude by presenting four key research directions we identified for practitioners and researchers to delineate future work.
arXiv Detail & Related papers (2024-01-12T12:02:16Z) - Regulation and NLP (RegNLP): Taming Large Language Models [51.41095330188972]
We argue how NLP research can benefit from proximity to regulatory studies and adjacent fields.
We advocate for the development of a new multidisciplinary research space on regulation and NLP.
arXiv Detail & Related papers (2023-10-09T09:22:40Z) - Challenges in aligning requirements engineering and verification in a
large-scale industrial context [7.92131557859946]
This paper presents preliminary findings of interviews that identify key challenges in aligning requirements and verification processes.
The findings of this study can be used by practitioners as a basis for investigating alignment in their organizations.
arXiv Detail & Related papers (2023-07-23T20:08:49Z) - Machine Unlearning: A Survey [56.79152190680552]
A special need has arisen where, due to privacy, usability, and/or the right to be forgotten, information about some specific samples needs to be removed from a model, called machine unlearning.
This emerging technology has drawn significant interest from both academics and industry due to its innovation and practicality.
No study has analyzed this complex topic or compared the feasibility of existing unlearning solutions in different kinds of scenarios.
The survey concludes by highlighting some of the outstanding issues with unlearning techniques, along with some feasible directions for new research opportunities.
arXiv Detail & Related papers (2023-06-06T10:18:36Z) - Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey [100.24095818099522]
Large language models (LLMs) have significantly advanced the field of natural language processing (NLP)
They provide a highly useful, task-agnostic foundation for a wide range of applications.
However, directly applying LLMs to solve sophisticated problems in specific domains meets many hurdles.
arXiv Detail & Related papers (2023-05-30T03:00:30Z) - Fairness in Recommender Systems: Research Landscape and Future
Directions [119.67643184567623]
We review the concepts and notions of fairness that were put forward in the area in the recent past.
We present an overview of how research in this field is currently operationalized.
Overall, our analysis of recent works points to certain research gaps.
arXiv Detail & Related papers (2022-05-23T08:34:25Z) - Artificial Intelligence for IT Operations (AIOPS) Workshop White Paper [50.25428141435537]
Artificial Intelligence for IT Operations (AIOps) is an emerging interdisciplinary field arising in the intersection between machine learning, big data, streaming analytics, and the management of IT operations.
Main aim of the AIOPS workshop is to bring together researchers from both academia and industry to present their experiences, results, and work in progress in this field.
arXiv Detail & Related papers (2021-01-15T10:43:10Z)
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.