AI in Money Matters

Nadine Sandjo Tchatchoua (Roskilde University); Richard Harper (Lancaster University)

REVIEW 4 major objections 4 minor 69 references

AI in Money Matters

T0 review · 4 major / 4 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read The paper claims that Fintech professionals in Denmark and the UK are guarded about adopting LLMs like ChatGPT, limiting use to routine tasks until regulation becomes clearer.

desk verdict Small qualitative study with honest limitations; the findings are about six informants, not the Fintech industry, and that gap should be fixed before publication. read the letter →

arxiv 2505.07393 v1 pith:BRNFPTXW submitted 2025-05-12 cs.CY cs.AI

Nadine Sandjo Tchatchoua (Roskilde University) , Richard Harper (Lancaster University) This is my paper · ORCID

classification cs.CYcs.AI

keywords largelanguagemodelsChatGPTFintechAIadoptionfinancialregulationqualitativeinterviewsprofessionalattitudesgreen

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to establish that Fintech professionals in Denmark and the UK, despite the hype around ChatGPT, are cautious and guarded about adopting large language models. Based on six interviews, it reports four shared views: LLMs are acceptable for routine clerical and analysis tasks but not for consequential decisions; existing regulations are seen as unfit for purpose; firms want bespoke in-house models to control data; and green or sustainability concerns are not a current priority. If true, this matters because it suggests that in a regulated financial industry, regulatory uncertainty rather than model capability is the main barrier to LLM adoption, and that practitioner priorities diverge from academic ethical concerns.

What carries the argument

The machinery is a small qualitative interview study: six semi-structured interviews with senior professionals representing six Fintech organisations, conducted by the first author and analysed through a general inductive approach in which the two authors grouped responses into themes over three rounds. Fintech is treated as a 'perspicuous setting'—a context where accountability to customers and regulators is unusually explicit—so the observed caution can be interpreted as institutional prudence rather than mere resistance. The four resulting themes carry the argument from interview quotes to the conclusion about industry-wide caution.

What would settle it

A representative survey of Fintech companies in Denmark, Sweden, and the UK showing widespread deployment of LLMs in customer-facing financial advice or discretionary investment decisions would contradict the paper's central claim. So would evidence that existing GDPR or EU AI Act rules are being used as clear, workable adoption guidelines rather than described as unfit.

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Extended reading notes

Core claim

The paper's central claim is that the Fintech industry—at least as represented by professionals in Denmark and the United Kingdom—remains cautious and guarded about adopting large language models such as ChatGPT. The authors ground this claim in four themes from their interviews: adoption is guarded and limited to routine tasks, existing regulations are seen as not fit for purpose, there is a universal desire to build bespoke in-house models to control data, and green concerns are deprioritised. They conclude that LLM potential is recognised, but real-world use is constrained by accountability and legal risk rather than by scepticism about the technology itself. The paper positions this practitioner view as a corrective to hype-driven discussions.

Load-bearing premise

The paper's industry-level conclusion rests on six self-selected informants, all male, based in Denmark or the UK and already using or planning to use LLMs, whose interview accounts are taken as describing their firms' actual practices.

Editorial extensions

If this is right

If the paper is right, LLM adoption in Fintech will initially stay confined to back-office and routine tasks, such as drafting documents, summarising data, and customer service analysis, while high-stakes advice and investment decisions remain human-led.
Regulatory clarity, not model performance, will be the decisive factor in speeding up or slowing down adoption; respondents saw current rules as ambiguous and reactive.
Fintech firms are likely to invest in bespoke, in-house models trained or adapted from general LLMs to control data flows and meet compliance requirements.
Sustainability and energy concerns will not, by themselves, deter Fintech adoption of LLMs in the near term, despite the green Fintech agenda.
The gap between academic worries about bias, privacy, and environment and practitioner priorities suggests that industry-focused AI governance should start from accountability and legal risk.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The authors do not say this, but the near-universal desire for bespoke in-house models suggests a commercial niche for fine-tuned, domain-specific LLMs where compliance and data control are the selling points.
A testable extension would be a larger survey of Fintech compliance officers to see whether 'regulation is unfit' is a stable industry belief or an artefact of this six-interview sample.
If regulatory uncertainty is the main brake, then countries with clearer AI rules should show measurably different adoption patterns; a cross-jurisdiction comparison could test that.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Colleague,

Worth a look if you care about LLM adoption in regulated sectors. This is a small interview study (six Fintech professionals in Denmark/UK) that extends Woodruff et al.'s finding that knowledge workers see generative AI as a tool for menial tasks, not decision-making. The new bits are domain-specific: regulation is the binding constraint, and all six want bespoke in-house models to control data flow. Those are legitimate, plausibly transferable observations, and the paper is honest about its exploratory character.

The analysis itself is reasonable. The quotes support the stated themes, the coding process is described, and the authors cite Woodruff et al. as the closest precedent. They do not oversell the novelty; if anything, the literature review is heavy on background and light on prior empirical work.

The soft spots are real but addressable. The biggest is that the conclusions repeatedly refer to "the Fintech industry" when the data are six self-selected men recruited through networks, all already using or planning to use LLMs. The paper's own "small scale, exploratory" framing does not license the generalizing language in the conclusion. That should be relaxed to "our informants" or the sample enlarged. Second, the audio recording chain has a wrinkle: Section 3.1 says only 3 of 6 interviews were recorded, yet Section 3.2 says all interviews were transcribed and translated. Direct quotes from unrecorded interviews are then presented as verbatim. The authors need to clarify whether notes were used and whether quotes are reconstructions. Third, the text promises five main themes in the analysis section and delivers four in the findings. Table 1's anonymized names don't fully match the names used later (Morten, Nicholai, Martin, Ola). These are mechanical problems, not misconduct, but they need cleanup.

I would not block publication on the sample size alone—exploratory work has a place—but the industry-level claims must be scaled back. The citation list also has a few errors (e.g., a reference to Fuchs 2108, and a dangling "Linden and Fenn" entry). A serious referee could prompt a solid revision.

Who is this for? CSCW/HCI people interested in how regulated industries talk about AI, and as a small counterpoint to hype. Not a foundation result, but a reasonable empirical data point.

Recommendation: send to peer review with the expectation of major revisions, mainly to align claims with the evidence and fix the internal inconsistencies.

Referee Report

4 major / 4 minor

Summary. The paper presents a qualitative, interview-based study of how professionals in the Fintech industry view the adoption and use of large language models (LLMs), particularly ChatGPT. Six informants from Fintech companies in Denmark and the UK were recruited via the authors' social networks and snowball sampling, with each informant already using or intending to use LLMs. The analysis produces four themes: (1) adoption is cautious and limited to routine tasks, (2) existing regulations are seen as not fit for purpose, (3) there is widespread interest in bespoke in-house LLMs, and (4) green/sustainability concerns are deprioritized in LLM development. The paper concludes that 'the Fintech industry remains cautious and guarded in relation to the adoption of LLMs such as ChatGPT.'

Significance. If the findings are considered as a bounded exploratory study, the paper makes a useful contribution by giving voice to practitioners in a regulated industry at an early stage of LLM adoption, complementing hype-driven discourse with empirical interview data. The CSCW framing is appropriate, and the paper is transparent about its small-scale, exploratory nature and its inductive analytic approach. The four themes, if confirmed in broader samples, would offer a valuable starting point for understanding adoption barriers in Fintech. However, the current significance is undercut by the gap between the evidence (six non-random informants, only half of the interviews recorded) and the industry-level conclusions. The paper's value depends on carefully rescoping its claims and providing a more auditable evidentiary chain.

major comments (4)

[Section 3.1, Discussion and conclusion] The central conclusion that 'the Fintech industry remains cautious and guarded in relation to the adoption of LLMs such as ChatGPT' overreaches the evidence. The sample consists of six self-selected informants recruited through the authors' social networks and snowball sampling, all male, all based in Denmark or the UK, and all already using or planning to use LLMs (Section 3.1, Table 1). These characteristics make the sample unrepresentative of the industry as a whole, and the paper itself describes the study as 'small scale, exploratory.' Please revise all industry-level statements, including the conclusion and the opening of Section 4, so that they refer explicitly to the six informants or their organizations, or provide a clear methodological argument for why this purposive sample can support broader inference.
[Sections 3.1 and 3.2] The evidentiary chain for the interview quotes is not auditable. Section 3.1 states that only 3 of the 6 interviewees allowed recording, yet Section 3.2 says 'the first author first transcribed and translated the interview data into English.' Direct quotations in Sections 4.1-4.4 cannot be verified as verbatim for the three unrecorded interviews. Please indicate which quotes come from recorded versus unrecorded interviews, provide a mapping between respondent IDs and the quotes used, and clarify how the unrecorded interviews were documented (for example, contemporaneous notes) and how this affects the use of direct quotations.
[Section 3.2 vs Section 4] The analysis section states 'After three such rounds, we established five main categories,' but the Findings section (4.1-4.4) presents only four themes. This internal inconsistency needs to be corrected. If a fifth category was identified and then dropped, explain why; otherwise, align the reported number of categories with the findings actually presented.
[Section 4.3 and Discussion] Since each organization is represented by a single informant, claims such as 'All 6 fintech companies welcomed the advent of ChatGPT' (Section 4.3) and 'our respondents were uniformly cautious' (Discussion) conflate individual attitudes with organization-level positions. Please specify that the findings reflect the views of individual respondents, not necessarily the policies or positions of their companies, and adjust the language accordingly.

minor comments (4)

[Table 1] Respondent O's 'Organisation base' is listed as 'UK and Sweden,' which contradicts Section 3.1's statement that all participants lived and worked either in Denmark or the United Kingdom. Please clarify the correct geographic scope.
[Throughout] There are several typographical and citation inconsistencies, including 'Kurtzweil' vs. 'Kurzweil,' 'Zanzotti' vs. 'Zanzotto,' 'Annany' vs. 'Ananny,' and 'Mclnerney' vs. 'McInerney.' Please correct these and ensure all reference names are spelled consistently.
[References] The reference list contains formatting issues, including an unnumbered entry for 'Linden, A., & Fenn, J. (2003) Understanding Gartner's hype cycles' and a duplicate reference for Kurzweil (entries [36] and [37]). These should be cleaned up and renumbered properly.
[Section 2.2] The sentence 'Nothing that looks like real-world evaluation in use has been published, to our knowledge, and very little which solicits expert views...' is too strong given that the paper cites Woodruff et al. (2024), which solicits expert views from knowledge workers. Please soften the claim to avoid an apparent contradiction.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper's claims are grounded in new interview data, not derived from its own inputs or self-citations.

full rationale

This paper is an exploratory qualitative interview study, not a formal derivation or prediction exercise. There are no equations, no fitted parameters, and no quantity constructed from the data that is then presented as an independent result. The four reported themes (guarded adoption, inadequate regulation, desire for bespoke models, deprioritized green concerns) are presented as inductive summaries of the six interviews, not as predictions derived from a model. The authors' citations to Harper and Randall provide an analytical framing from CSCW, but the load-bearing evidence for the findings is the new interview material, and no central claim is justified solely by those citations. The concerns raised by the skeptic about sample size, representativeness, and the unrecorded interviews are validity and evidentiary limitations, not circularity. A sample that is too small or unrepresentative may weaken the external generalization of the conclusion, but it does not make the reasoning circular, because the conclusion is not equivalent by construction to the interview protocol or to any prior publication. Therefore, under the stated criteria, the appropriate finding is no significant circularity.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

This is qualitative empirical research, so the ledger contains no fitted parameters, no mathematical axioms, and no invented entities. The load-bearing assumptions are the standard interpretive premises of interview-based research: that informants' self-reports reflect organizational practice, and that six networked and self-selected informants can stand for an industry. The paper flags its exploratory scale in Section 3.2, but the industry-level wording in Sections 4 and 5 goes beyond the evidence those premises can carry.

assumptions (3)

domain assumption Self-reported interview accounts reflect actual adoption practices within the informants' firms.
The findings in Section 4 are built entirely on what six informants said about their organizations; no observational or documentary data is used to cross-check their accounts.
domain assumption Six informants recruited via the authors' social networks and snowball sampling are sufficient to characterize the Fintech industry's stance.
The paper calls the study 'small scale, exploratory' (Section 3.2) yet the findings and conclusion generalize to 'the Fintech industry' (Sections 4.1 and 5), which goes beyond what the sample can support.
domain assumption The general inductive approach described by Thomas (2003) yields themes that correspond to the informants' actual views.
Coding was informal, done by the first author with discussion in three rounds (Section 3.2), and no inter-rater reliability check or coding audit trail is provided.

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Pith. "Pith review of AI in Money Matters." pith.science (2026). https://pith.science/paper/BRNFPTXW

@misc{pith2026250507393,
  author       = {Pith},
  title        = {Pith review of: AI in Money Matters},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/BRNFPTXW}},
  note         = {Machine review of arXiv:2505.07393}
}

read the original abstract

In November 2022, Europe and the world by and large were stunned by the birth of a new large language model : ChatGPT. Ever since then, both academic and populist discussions have taken place in various public spheres such as LinkedIn and X(formerly known as Twitter) with the view to both understand the tool and its benefits for the society. The views of real actors in professional spaces, especially in regulated industries such as finance and law have been largely missing. We aim to begin to close this gap by presenting results from an empirical investigation conducted through interviews with professional actors in the Fintech industry. The paper asks the question, how and to what extent are large language models in general and ChatGPT in particular being adopted and used in the Fintech industry? The results show that while the fintech experts we spoke with see a potential in using large language models in the future, a lot of questions marks remain concerning how they are policed and therefore might be adopted in a regulated industry such as Fintech. This paper aims to add to the existing academic discussing around large language models, with a contribution to our understanding of professional viewpoints.

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