Big Data and AI boost efficiency, risk assessment and inclusion across finance, but their unchecked adoption can amplify systemic risk, cyber‑vulnerability and BigTech dependence; coordinated regulation, strong data governance and investment in specialised human capital are required to secure financial stability.

Implications of Big Data Technologies for the Resilience of Financial Institutions

Serhii Zadvornykh, Dmytro Ihorovych Martjanov · March 31, 2026 · Acta Academiae Beregsasiensis Economics

openalex review_meta low evidence 7/10 relevance DOI Source PDF

A qualitative review finds Big Data and AI improve efficiency, risk assessment accuracy, fraud detection and financial inclusion across finance, but uncoordinated or poorly governed adoption raises systemic risk, cybersecurity vulnerabilities, regulatory fragmentation and third-party dependence unless matched by regulation, data governance and workforce investment.

The research examines the impact of Big Data-based FinTech and AI technologies on the development of the financial sphere and financial stability. The subject of the research is the transformation of key areas of financial activity under the influence of large-scale data analytics, including banking, risk management, insurance, stock markets, accounting, auditing and taxation. The purpose of the study is to identify main benefits, risks and structural consequences of Big Data implementation in financial institutions, and to assess regulatory, ethical, security and human resource challenges arising from this process. The research is based on a qualitative analysis of recent academic literature, comparative analysis of sector-specific applications of Big Data technologies, and synthesis of empirical findings from international studies. The methodological approach combines systemic and structural analysis, allowing the interconnections between technological innovation, financial stability, institutional adaptation to be identified. The results demonstrate that Big Data and AI technologies significantly improve efficiency, risk assessment accuracy, fraud detection and financial inclusion. At the same time, their unbalanced or poorly governed adoption contributes to increased systemic risk, cybersecurity vulnerability, regulatory fragmentation and third-party dependence on BigTech platforms. It is also established that the effectiveness of Big Data solutions varies across financial sphere and depends critically on data quality, regulatory alignment and organisational readiness. Significant changes in human resource needs and requirements are identified, with growing demand for analysts and specialists combining financial and technological competencies. The results of the study may be applied in the development of financial institution strategies, regulatory frameworks, risk management systems and professional training programmes. It is concluded that Big Data-based FinTech can contribute to financial stability only when its implementation is strategically justified, ethically grounded and supported by effective regulation, robust data governance and investment in human capital.

Summary

Citation

Zadvornykh, S. & Martjanov, D. (2026). Implications of Big Data Technologies for Resilience of Financial Institutions. Acta Academiae Beregsasiensis. Economics, Vol. 12 (2026), pp. 29–44. DOI: 10.58423/2786-6742/2026-12-29-44. JEL: C88, G28, G32, J24, O33.

Main Finding

Big Data–driven FinTech and AI materially improve operational efficiency, credit and risk assessment accuracy, fraud detection and financial inclusion across financial sectors — but if adoption is unbalanced or poorly governed, these technologies can raise systemic risk, increase cyber- and third‑party vulnerabilities (notably dependence on BigTech/cloud providers), fragment regulation, and create significant human‑capital mismatches. Net contribution to financial stability is conditional on data quality, regulatory alignment, organisational readiness, robust data governance and investment in skills.

Key Points

Core benefits
- Improved credit scoring and default risk reduction (especially in banking).
- Enhanced fraud detection, more granular risk modelling (insurance, risk management).
- Operational automation (accounting/auditing) and greater financial transparency (tax administration).
- Expanded financial inclusion via alternative credit products based on new data sources.
Major risks and constraints
- Increased systemic risk from altered liquidity profiles, shifting loan distributions, higher risk appetite, and shadow activities.
- Cybersecurity exposure and concentration risk from reliance on BigTech/cloud platforms.
- Regulatory fragmentation and uneven legal/ethical frameworks across jurisdictions.
- Data quality problems, model errors, and misalignment between automated outputs and regulatory/legislative changes.
- Low adoption or mistrust in some professions (many auditors still rely on spreadsheets); effectiveness varies by task complexity and user needs.
- Human capital shifts: rising demand for hybrid finance–tech specialists; potential displacement where automation is feasible.
Sector‑specific observations
- Banking: Big Data lowers default risk and improves product personalization, but may reduce liquidity and change leverage/risk profiles; external FinTech competition can erode bank returns.
- Risk management & insurance: richer datasets enable superior screening and fraud controls but require new risk frameworks and governance.
- Stock markets: digital tools speed routine tasks but currently fall short of fully satisfying complex global exchange analytics; local analyst expertise remains valuable.
- Accounting/auditing: distributed ledgers and AI can deepen audits, but adoption is limited and mismatch between tool outputs and complex regulatory contexts can create errors.
- Taxation: Big Data boosts oversight and transparency but can induce corporate behavioral responses (riskier investments) if enforcement becomes overly punitive.
Conditional effectiveness: benefits depend critically on data quality, regulatory coherence, and organisational preparedness.

Data & Methods

Methodological approach: qualitative synthesis and systemic/structural analysis.
Evidence base: qualitative review of recent academic literature, comparative analysis of sectoral Big Data applications, and synthesis of international empirical findings cited in the literature.
Nature of results: conceptual and literature‑driven rather than new primary quantitative estimation; identifies patterns and trade‑offs reported across empirical studies.
Limitations noted by authors: literature is fragmented and often sector‑specific; effectiveness heterogeneity across institutions and countries; need for more coordinated empirical and causal analysis.

Implications for AI Economics

Policy and regulation
- Need for coordinated, technology‑aware regulatory frameworks (covering data governance, competition/BigTech dependence, model validation, cyber resilience).
- RegTech approaches should be encouraged to foster consistent supervision and cross‑jurisdictional coherence.
Systemic risk measurement and macroprudential design
- Macroprudential models need updating to capture liquidity changes, altered loan distribution, concentration risk from platform providers, and endogenous risk amplification via algorithmic strategies.
- Stress tests should include scenarios of data/model failures, supply‑chain/cloud outages, and correlated algorithmic behaviors.
Market structure and competition
- Monitor platform/BigTech market power and third‑party dependence; consider policies to limit operational concentration and ensure interoperability.
Labor and human‑capital economics
- Expect growing demand for workers with combined finance + data/AI skills; policy should support reskilling and curricula that integrate finance, data science, and ethics.
- Anticipate heterogeneous displacement across tasks — policy and firms should plan transitional pathways.
Research directions for AI economics
- Causal, micro‑level studies of Big Data/AI adoption effects on bank liquidity, risk taking, and firm outcomes.
- Cross‑country comparisons to evaluate how regulatory regimes mediate benefits/risks.
- Modeling of systemic effects from correlated algorithmic decisions and third‑party outages.
- Welfare analyses weighing gains in inclusion and efficiency against concentration, privacy, and systemic fragility.
Practical guidance for firms
- Adopt strategic, ethically grounded Big Data deployments with emphasis on data quality, robust model governance, third‑party risk management, and workforce development.

Summary conclusion: Big Data and AI can strengthen financial institutions and inclusion, but the net macroeconomic and systemic outcomes hinge on governance, regulation, data quality and human capital investments — areas where economists and policymakers should prioritize measurement, model updating and coordinated interventions.

Assessment

Paper Typereview_meta Evidence Strengthlow — The paper is a qualitative literature synthesis and comparative analysis of secondary sources rather than original causal empirical work; claims rely on heterogeneous international studies of varying rigor and there is no systematic causal identification or meta-analytic aggregation. Methods Rigormedium — Uses a structured comparative and systemic/structural analytic approach to synthesize recent literature and sectoral applications, but the description does not indicate a pre-registered or fully systematic search protocol, transparent inclusion/exclusion criteria, quantitative synthesis, or robustness checks, leaving room for selection bias and variable study quality. SampleSecondary data: recent academic literature, international empirical studies and sector-specific case examples across banking, risk management, insurance, stock markets, accounting, auditing and taxation; no primary data collection or original microdata analysis. Themesgovernance adoption innovation org_design skills_training productivity GeneralizabilityFindings aggregate diverse jurisdictions and sectors, so sector- and country-specific conditions may differ substantially, Rapidly evolving AI/Big Data technologies mean results may be time-sensitive and quickly outdated, Conclusions depend on the quality and selection of cited studies (publication bias and heterogeneity), Lacks causal estimates applicable to specific policy interventions or firm-level decisions, Organizational, regulatory and data-governance contexts vary, limiting transferability across institutions

Claims (7)

Claim	Direction	Confidence	Outcome	Details
Big Data and AI technologies significantly improve efficiency, risk assessment accuracy, fraud detection and financial inclusion. Organizational Efficiency	positive	high	efficiency; risk assessment accuracy; fraud detection; financial inclusion	0.24
Unbalanced or poorly governed adoption of Big Data and AI contributes to increased systemic risk, cybersecurity vulnerability, regulatory fragmentation and third-party dependence on BigTech platforms. Fiscal And Macroeconomic	negative	high	systemic risk; cybersecurity vulnerability; regulatory fragmentation; third-party dependence on BigTech	0.24
The effectiveness of Big Data solutions varies across the financial sphere and depends critically on data quality, regulatory alignment and organisational readiness. Adoption Rate	mixed	high	effectiveness of Big Data solutions	0.24
Significant changes in human resource needs are occurring, with growing demand for analysts and specialists combining financial and technological competencies. Skill Acquisition	positive	high	demand for combined financial-technological specialists	0.24
Big Data-based FinTech can contribute to financial stability only when its implementation is strategically justified, ethically grounded and supported by effective regulation, robust data governance and investment in human capital. Governance And Regulation	mixed	high	contribution of Big Data-based FinTech to financial stability conditional on governance and investment	0.04
The study is based on a qualitative analysis of recent academic literature, comparative analysis of sector-specific applications of Big Data technologies, and synthesis of empirical findings from international studies using a systemic and structural analysis approach. Other	null_result	high	methodological approach (literature synthesis, comparative analysis, systemic/structural analysis)	0.4
Results may be applied in the development of financial institution strategies, regulatory frameworks, risk management systems and professional training programmes. Governance And Regulation	positive	high	applicability of study results to strategy, regulation, risk management and training	0.04