AI-enabled FinTech for innovative sustainability: promoting organizational sustainability practices in digital accounting and finance
Bibliographic record
Abstract
Purpose The purpose of this study is to delve into the intricate terrain of assimilating sustainability practices into digital accounting and finance, centring on the transformative dynamics introduced by artificial intelligence (AI)-enabled FinTech. The primary objective is to scrutinize critical lacunae in existing literature, exploring how organizations can meticulously construct comprehensive sustainability frameworks. Simultaneously, the study investigates the protracted repercussions of AI-enabled FinTech on the enduring sustainability paradigms. Design/methodology/approach Executing a systematic literature review, the research engaged in the meticulous identification and assessment of a voluminous pool of 1,158 articles. Using a judicious two-phase strategy, the scrutiny distilled a mere 64 pertinent articles, subjecting them to rigorous evaluation encompassing methodologies, contributions and overall quality. The Fuzzy Delphi method was used to elicit expert opinions and facilitate consensus-building, leveraging fuzzy logic to accommodate uncertainties in the data. Findings The review navigates the convoluted impact of AI across diverse sectors, accentuating its transformative imprint on realms such as health care, finance and transportation. Specifically, in the financial domain, the discerning eye of AI-enabled FinTech optimizes investment portfolios, augments risk assessment, propels financial inclusion and streamlines the intricate landscape of sustainability reporting. The study meticulously pinpoints research gaps encompassing investment optimization, risk management, financial inclusion, sustainability reporting and ethical considerations within the intricate milieu of AI-enabled FinTech. This research contributes to the existing body of knowledge by synthesizing intricate thematic strands, discerning overarching trends and spotlighting critical voids in the synthesis of sustainability practices and AI-enabled FinTech. The findings resonate with far-reaching implications, emphasizing the exigency of comprehensive investigations into the longitudinal sustainability ramifications instigated by AI-enabled FinTech. Originality/value The study underscores the imperative of crafting robust ethical frameworks for the equitable and transparent deployment of AI solutions within the intricate landscape of FinTech. Moreover, this research stands poised to shape organizational strategies, inform regulatory frameworks and guide investment decisions, thereby catalyzing the cultivation of conscientious and sustainable financial practices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".