Abstract

The article develops a regulatory model for implementing whitelist mechanisms in financial monitoring as an instrument of state financial security under conditions of digitalization and algorithmic transaction control. The study focuses on false-positive block-ing of legitimate and legally mandatory transactions, especially public-law payments whose recipient, purpose, and legal basis can be verified through authoritative data. The methodology combines systemic, comparative, structural-functional, institutional, and conceptual modelling approaches. The article defines false-positive blocking as an automated or semi-automated high-risk classification or suspension of a financial trans-action that is subsequently confirmed as legitimate on the basis of its legal nature, verified recipient status, payment purpose, customer due diligence data, or post-audit review. The study identifies the principal mechanism of such errors as a mismatch be-tween behavioral risk indicators and the legal classification of transactions. It argues that large amounts, unusual frequency, atypical geography, new recipients, or cus-tomer-profile mismatches are not causes of illegitimacy in themselves, but risk indicators that may generate false-positive decisions when legal-context validation is absent. Based on this mechanism, the article proposes a three-level regulatory model consisting of institutional, informational, and algorithmic components: a centralized registry of ver-ified public-law payment recipients, dual validation of risk andlegal classification, real-time pre-blocking validation, low-risk eligibility testing, audit logging, human-in-the-loop review, and post-audit safeguards. The whitelist mechanism is not treated as an ex-emption from AML/CFT monitoring; rather, it functionsas a validated processing regime for transactions that meet independent eligibility criteria and contain no critical risk flags. The proposed model is designed to reduce unjustified automatic blocking, pre-serve the continuity of state financial flows, andstrengthen the explainability, propor-tionality, and accountability of algorithmic financial monitoring.

Keywords: financial monitoring, financial security, artificial intelligence, whitelist mechanism, public-law payments, false-positive blocking, risk-based approach, AML/CFT, RegTech

JEL Classification: G18, G28, H26, K22, K42

Dryha, Zh., Trynchuk, V., Levchenko, O., Sereda, O., Kochubei, L., & Domashenko, O. Regulatory Whitelist Mechanisms in Financial Monitoring and State Financial Security. Financial and Credit Activity: Problems of Theory and Practice. 2026. Vol. 3, No. 68, pp. 235-252. Published June 30, 2026 DOI: https://doi.org/10.55643/fcaptp.3.68.2026.5248

Abstract

The study evaluates whether explainable machine-learning models can provide reliable and operationally interpretable short-horizon delinquency monitoring in U.S. auto lease securitization panels. Six public SEC ABS-EE trust-family panels were harmonized at the contract-month level. The primary outcome is one-month-ahead incident escalation to 30 or more days past due among contracts below 30 days past due at the feature month. Fully tuned penalized logistic regression (M1), unconstrained gradient boosting (M2), and governance-constrained gradient boosting (M3/X-LEASE) were assessed through chronological development, calibration, locked out-of-time testing, contract-held-out validation, six leave-one-issuer-out experiments, and later temporal evaluation. The locked test comprised 198,301 observations and 768 events. M2 produced the strongest discrimination (AUC-ROC 0.8151; PR-AUC 0.0269), while M3 exceeded the logistic benchmark by PR-AUC but did not outperform M2. At an exact 1% review capacity, each model generated 1,984 alerts; M2 detected 104 events, compared with 61 for M3, so the hypothesized recall advantage of X-LEASE was not supported. Full-sample TreeSHAP analysis showed stable feature rankings across tested issuers, later periods, and contract-level resampling. M3 relied more heavily on credit score and issuer controls and had a more concentrated explanation structure, but this does not establish superior auditability or fairness. The results support human-reviewed early-warning monitoring within the tested securitization panels, not lifetime default prediction, automated adverse decisions, or universal transferability.

Keywords: auto lease securitization, delinquency monitoring, explainable artificial intelligence, model governance, probability calibration, rare-event prediction, SHAP

JEL Classification: C53, C55, G23, G28

Dryha, Zh., Levchenko, O., Sergiienko, L., Kochubei, L., Domashenko, O., & Shcheglova, K. Explainable AI for delinquency risk monitoring in U.S. auto lease securitizations. Investment Management and Financial Innovations. 2026. Vol. 23, No. 3, pp. 215-234. Published August 13, 2026 DOI: https://doi.org/10.21511/imfi.23(3).2026.16

Abstract

Virtual assets are integrated into financial markets, but legal recognition and tradability do not determine whether an instrument can support regulated liabilities in non-bank financial institutions (NBFIs). This theoretical study develops the Conceptual Prudential Compatibility Index (CPCI) as an ordinal framework for prudential screening. It combines peer-reviewed research with primary-source analysis of the European Union, the United States, and Ukraine, using a regulatory cut-off date of August 4, 2026. To avoid unsupported precision, the CPCI does not assign scalar scores. It separates a core economic-prudential profile – liquidity, value stability, and predictability/valuation reliability – from a non-compensatory regulatory overlay, R(a, j, s, u), defined by instrument, jurisdiction, NBFI sector, and intended prudential use. Each dimension is classified through fixed documentary anchors as high, moderate, limited, very low, or insufficient evidence. Traditional reference archetypes show stronger core profiles than large-cap unbacked crypto-assets, with stress-sensitive liquidity and very low value stability and predictability. Fiat-backed stablecoins require separate assessment of redemption rights, reserve quality, and segregation. Tokenized securities and real-world claims require look-through assessment of the underlying asset and legal, custody, and operational risks. The regulatory comparison confirms that market regulation does not create reserve eligibility: MiCA is distinct from Solvency II and IORP rules; U.S. insurance accounting treats directly held crypto-assets as non-admitted under the cited NAIC guidance; and reviewed Ukrainian sectoral rules do not expressly identify virtual assets as eligible prudential assets. The CPCI is a documentary screening framework, not an externally validated quantitative risk model.

Keywords: asset tokenization, crypto-assets, insurance solvency, pension funds, prudential compatibility, regulatory admissibility, reserve eligibility, stablecoins

JEL Classification: G22, G23, G28, K23

Dryha, Zh., Levchenko, O., Polinkevych, O., Grytsyshen, D., Kochubei, L., & Domashenko, O. Virtual assets in non-bank financial institutions: A conceptual prudential compatibility index. Insurance Markets and Companies. 2026. Vol. 17, No. 2, pp. 58-75. Published August 19, 2026 DOI: https://doi.org/10.21511/ins.17(2).2026.05

Abstract

Ukraine's transition to risk-based prudential supervision requires non-bank financial in-stitutions to connect formal regulatory obligations with measurable financial resilience, reliable data, governance, and professional competence. The purpose of the study is to assess the financial transformation and prudential compliance of selected Ukrainian non-bank financial institution segments, systematize applicable regulatory requirements, and develop an integrated framework linking financial, digital, legal, governance, and pro-fessional mechanisms. The analysis combines official National Bank of Ukraine data for insurance companies, finance companies, and credit unions, descriptive period compar-isons, author-verified regulatory coding, and comparative analysis of continuing profes-sional development frameworks. The results reveal market consolidation across all three segments but divergent balance-sheet trajectories: insurers and finance companies ex-panded their aggregate assets, whereas credit unions contracted. Governance and legal obligations dominate the primary-code structure of the selected regulatory require-ments, while multiple coding shows that financial, digital, legal, governance, and pro-fessional components are interdependent. The study develops a financial-first concep-tual framework in which prudential assessment forms the evidence-based core, digital tools support monitoring and traceability, legal mechanisms translate external require-ments into internal obligations, and governance and professional competence operate as cross-cutting layers. The findings support differentiated implementation rather than a uniform compliance template for all non-bank financial institutions. The results may assist the National Bank of Ukraine, non-bank financial institutions, compliance, risk-management and internal-audit functions, professional associations, researchers, and policy specialists in designing proportionate control, reporting, governance, and profes-sional-development systems.

Keywords: non-bank financial institutions, regulatory compliance, prudential supervision, market consolidation, financial resilience, asset quality, RegTech, corporate governance, continuing professional development, AI governance

JEL Classification: G23, G28, G32, M42, O33

Dryha, Zh., Levchenko, O., Kochubei, L., Domashenko, O., Shcheglova, K., & Pylypets, A. Financial, Digital and Legal Mechanisms for Regulatory Compliance in Non-Bank Financial Institutions. Financial and Credit Activity: Problems of Theory and Practice. 2026. Vol. 4, No. 69, pp. 81-102. Published August 31, 2026 DOI: https://doi.org/10.55643/fcaptp.4.69.2026.5278

Abstract

Introduction. The digital transformation of public finance is increasing the role of artificial intelligence (AI) in government auditing, financial monitoring, data management, and risk forecasting. At the same time, the active implementation of AI in public and financial institutions is accompanied not only by potential economic benefits, but also by new regulatory, operational, and algorithmic risks associated with data quality, cybersecurity, algorithmic bias, false positives, and transparency of automated decision-making. This creates the need for a comprehensive scientific analysis of international AI governance models and approaches to their adaptation in Ukraine under conditions of digital transformation and geopolitical instability.

The purpose of the article is to systematize international experience in the application of AI in public and financial institutions, conduct a comparative analysis of U.S. and EU approaches to AI governance, and substantiate approaches to the development of an adaptive AI regulatory model for Ukraine’s public finance system.

Results. The study demonstrates that AI is gradually being integrated into key public finance functions, including government auditing, financial monitoring, transaction analysis, budget data management, and risk forecasting. AI implementation contributes to faster financial data processing, automation of control procedures, and optimization of selected managerial decisions. At the same time, significant differences between the U.S. innovation-oriented model and the EU regulation-oriented model based on risk-oriented governance and algorithmic accountability principles are identified.

The study substantiates that alongside the positive effects of AI implementation, new systemic risks emerge, including operational risk, model risk, algorithmic bias, and false-positive blocking of legitimate financial operations in AI-driven financial monitoring systems. The concept of algorithmic financial disruption is proposed as a separate analytical direction for studying digital finance risks associated with the potential impact of algorithmic errors on public finance stability, budgetary flows, and national financial security.

Conclusions. The study substantiates the feasibility of developing a hybrid AI governance model combining innovation capacity with regulatory oversight, algorithmic accountability, and human oversight mechanisms. For Ukraine, strategic importance is attached to harmonization with EU approaches to AI regulation and virtual assets regulation, development of digital financial monitoring systems, and protection of critical public financial flows. The results may be used in shaping public policy in the fields of digital finance, AI governance, and financial security.

Keywords: AI governance, algorithmic accountability, data governance, government audit, public finance, financial monitoring, operational risk, model risk, digital finance, artificial intelligence

JEL Classіfіcatіon: H61, H83, O33, G28, C55

Dryha, Zh. V. (2026). International experience in the application of artificial intelligence in public and financial institutions. World of Finance, 2(87), 242–251. Published September 10, 2026. DOI: https://doi.org/10.35774/SF2026.02.242