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Firms that embrace AI take more financial risk because AI strengthens their innovation resilience; the boost fades under high environmental uncertainty but grows when firms disclose more environmental information.

Can Artificial Intelligence Enhance Corporate Financial Risk-Taking Capacity? A Perspective on Innovation Resilience and the Environment
Kelin Du, Yubing Wei, Shanyue Jin · February 11, 2026 · Sustainability
openalex correlational medium evidence 7/10 relevance Summary only summary available; pdf_status=paywall DOI Source PDF

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Using firm-year panel data and text-mined AI indicators, the paper finds greater AI adoption is associated with higher corporate financial risk-taking, mediated by improved innovation resilience, with environmental uncertainty weakening and environmental information disclosure strengthening this effect.

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In the current global competition, innovation-driven strategies are considered crucial to enhance corporate productivity. Financial risk-taking serves as the baseline for corporate survival. This study scrutinizes the consequences of artificial intelligence on corporate financial risk-taking capacity and elucidates the pathways and mechanisms involved. Using data on local publicly traded entities for the period spanning 2015–2024, this study employs text mining methods and fixed-effects regression analysis to investigate the influence of artificial intelligence on corporate financial risk-taking capacity. The outcomes suggest that AI advances corporate financial risk-taking capacity; specifically, it improves corporate innovation and strengthens innovation resilience (i.e., stability dimension). Furthermore, environmental uncertainty suppresses the constructive influence of AI on monetary risk-taking, whereas high-level environmental information disclosure exerts a positive impact. This study uncovers the underlying processes through which machine learning boosts business financial risk-taking capacity and provides theoretical and practical insights for balancing innovation and risk during corporate digital transformation. In summary, this study makes three key contributions: First, it develops a novel theoretical chain linking AI adoption to enhanced corporate financial risk-taking through the mediating mechanism of innovation resilience. Second, it reveals that the positive effect of AI is attenuated by environmental uncertainty but amplified by environmental information disclosure, integrating external factors into the framework. These findings offer strategic insights for managers and policymakers in the digital era.

Summary

Main Finding

AI adoption increases firms' financial risk-taking capacity. It does so by improving innovation and strengthening firms' innovation resilience (particularly the stability dimension). The positive effect of AI is weakened by environmental uncertainty but strengthened when firms provide high levels of environmental information disclosure.

Key Points

  • Sample and period: local publicly traded firms, 2015–2024.
  • Primary methods: text-mining to measure AI-related activity and panel fixed-effects regression for causal inference.
  • Core result: greater AI-related activity is associated with higher corporate financial risk-taking capacity.
  • Mechanism: AI → improved innovation performance and greater innovation resilience (stability) → increased willingness/capacity to take financial risk.
  • Moderation: environmental uncertainty dampens the AI → risk-taking link; richer environmental information disclosure amplifies it.
  • Contributions claimed:
  • A theoretical chain tying AI adoption to financial risk-taking via innovation resilience.
  • Integration of external context (environmental uncertainty and disclosure) as moderators of AI’s effect.
  • Managerial and policy-relevant insights for balancing innovation and risk in digital transformation.

Data & Methods

  • Data: Firm-level observations from publicly listed local firms over 2015–2024.
  • AI measurement: text-mining of firm disclosures/ documents to quantify AI-related adoption/activity (paper reports using text mining but does not specify the exact dictionary/metric here).
  • Outcome variable: corporate financial risk-taking capacity (definition/precise metric not provided in summary).
  • Empirical strategy: panel fixed-effects regressions (controls likely include firm and year fixed effects to absorb time-invariant heterogeneity and common shocks).
  • Mediation analysis: tests whether innovation and innovation resilience (stability) mediate the AI → risk-taking relationship.
  • Moderation tests: interaction terms or subgroup analysis with environmental uncertainty and environmental information disclosure to assess conditional effects.

Implications for AI Economics

  • Theory: Positions AI as a productivity and capability-enhancing general-purpose technology that alters firms' risk preferences/capacities through innovation resilience, enriching models that link technology adoption to firm-level risk behavior.
  • Measurement: Demonstrates practical use of text-mining to quantify AI adoption in firm disclosures — useful for empirical work where direct measures of AI investment are sparse.
  • Policy: Encourages policies that reduce environmental uncertainty (e.g., clearer regulation, stable policy regimes) and promote transparent environmental information disclosure to maximize AI’s beneficial effects on productive risk-taking.
  • Managerial: Suggests managers can leverage AI investments not only to boost innovation output but also to build resilient innovation processes that enable bolder financial decisions; disclosure practices matter for realizing these gains.
  • Research directions: Calls for further work identifying precise measures of risk-taking and resilience, testing causal identification strategies (e.g., instrumental variables, natural experiments), and exploring sectoral heterogeneity in AI’s risk-related effects.

Assessment

Paper Typecorrelational Evidence Strengthmedium — The analysis uses a large firm-year panel and fixed effects which help control for time-invariant firm heterogeneity and common shocks, and it explores mediation and interaction mechanisms; however, causal interpretation is limited by potential time-varying omitted confounders, reverse causality (risk-taking may drive AI investment or disclosure), and measurement error from text-mined AI proxies. Methods Rigormedium — Employs contemporary tools (text mining, panel fixed-effects, mediation and interaction tests) appropriate for observational corporate data and likely runs robustness checks, but lacks a clearly exogenous identification strategy (e.g., instrument, natural experiment, or difference-in-differences exploiting plausibly exogenous variation) and depends on indirect proxies for AI adoption and innovation resilience. SamplePanel of publicly listed firms observed annually from 2015–2024; AI adoption proxied via text mining of corporate disclosures/reports; outcome is firm-level financial risk-taking (corporate monetary risk-taking measures); mediators include innovation output/resilience measures; heterogeneity examined by firm-level environmental uncertainty and environmental information disclosure. (Exact country/market not specified in the summary.) Themesinnovation adoption IdentificationText-mined, firm-level measure of AI adoption combined with panel (firm-year) fixed-effects regression to estimate the association between AI intensity and corporate financial risk-taking; mediation analysis tests whether measures of innovation and 'innovation resilience' transmit the effect; interactions with environmental uncertainty and environmental information disclosure probe heterogeneity. No experimental variation or externally valid instrument is described. Generalizabilitypublicly_traded_firms_only (excludes private firms, startups, SMEs), country_or_market_context_may_limit_transferability (regulatory, disclosure norms vary), 2015-2024_time_window (findings may not hold as AI matures further), AI_measurement_via_text_mining_may_misclassify true AI investment/usage intensity, potential_selection_bias (firms that disclose more may differ systematically)

Claims (7)

ClaimDirectionOutcomeConfidence & EvidenceDetails
Artificial intelligence advances corporate financial risk-taking capacity. Organizational Efficiency positive corporate financial risk-taking capacity
Reading fidelity high
Study strength medium
not reported
0.3
AI improves corporate innovation. Innovation Output positive corporate innovation
Reading fidelity high
Study strength medium
not reported
0.3
AI strengthens innovation resilience (the stability dimension of innovation). Innovation Output positive innovation resilience (stability dimension)
Reading fidelity high
Study strength medium
not reported
0.3
AI increases corporate financial risk-taking indirectly via the mediating mechanism of enhanced innovation resilience. Organizational Efficiency positive corporate financial risk-taking capacity (mediated by innovation resilience)
Reading fidelity high
Study strength medium
not reported
0.3
Environmental uncertainty suppresses (attenuates) the positive influence of AI on monetary/financial risk-taking. Organizational Efficiency negative corporate financial risk-taking capacity (AI × environmental uncertainty interaction)
Reading fidelity high
Study strength medium
not reported
0.3
High levels of environmental information disclosure amplify (positively moderate) the constructive effect of AI on corporate financial risk-taking. Organizational Efficiency positive corporate financial risk-taking capacity (AI × environmental information disclosure interaction)
Reading fidelity high
Study strength medium
not reported
0.3
This study used data on local publicly traded entities for 2015–2024 and employed text mining methods and fixed-effects regression analysis to investigate AI's influence on corporate financial risk-taking. Research Productivity null_result study design / data and methods (2015–2024 panel; text mining; fixed-effects regressions)
Reading fidelity high
Study strength high
not reported
0.5

Notes