Blockchain-based Risk Analytics for Banking Under Stress Scenarios

Thesis Abstract

In the wake of increasing systemic risk and heightened regulatory scrutiny, banks face challenges in timely, transparent, and robust risk analytics during stress scenarios, where traditional centralized data architectures hinder cross-institutional data sharing and rapid scenario testing. This study addresses the problem by developing a blockchain-enabled risk analytics framework that enhances data integrity, traceability, and real-time collaboration among multiple stakeholders under stress conditions. The aim is to design, implement, and evaluate a decentralized analytics platform that integrates transactional and market data with risk models to produce auditable, scenario-driven risk metrics. Specific objectives include (1) constructing a multi-jurisdictional threat-informed dataset comprising 1.2 million simulated and anonymized banking transactions, 450,000 market data records, and 200 stress-test scenarios; (2) developing a modular risk analytics architecture that combines deterministic risk indicators with probabilistic assessments using Bayesian networks; (3) implementing a permissioned blockchain layer (Hyperledger Fabric) to ensure data provenance, immutability, and access control across participating banks, regulators, and audit firms; (4) integrating machine learning models for anomaly detection (Isolation Forest), tail-risk estimation (Extreme Value Theory), and liquidity stress forecasting (LSTMs) within the blockchain-enabled environment; (5) evaluating model performance and governance efficacy through a mixed-methods approach involving quantitative metrics and qualitative expert reviews. The methodology adopts a holistic, action-oriented design combining design science research and a positivist paradigm. The population comprises five commercial banks with established digital risk platforms, three financial market data providers, and two regulatory bodies collaborating in a sandbox environment. A purposive sampling strategy selects data-rich institutions that demonstrate willingness to participate in controlled stress-testing experiments, resulting in a sample of 5 banks, 3 data providers, and 2 regulators. Data collection employs structured transaction and market data feeds, synthetic stress-test scenarios aligned with Basel III/IIASA stress testing guidelines, and semi-structured interviews with risk directors and compliance officers. Instrumentation includes a validated risk indicator questionnaire, system logs, and automated extract-transform-load (ETL) pipelines for data ingestion into the blockchain layer. Validity and reliability are ensured through triangulation, back-testing against historical crisis periods, and pilot-testing of the analytics modules. Data analysis employs a combination of (i) descriptive statistics to characterize baseline and stressed states; (ii) regression-based and Bayesian network models to assess dependencies among credit, market, and liquidity risk factors; (iii) extreme value theory to model tail risks; (iv) time-series analyses, including LSTMs, for forecasting liquidity and funding gaps; (v) anomaly detection with Isolation Forest to identify irregular patterns under stress; and (vi) governance performance metrics to evaluate provenance, access control, and auditability. A prototype deployment in a permissioned blockchain environment provides end-to-end traceability of data lineage, model inputs/outputs, and decision rationales, while conventional centralized benchmarks enable comparative assessment. Expected findings indicate that the blockchain-enabled framework reduces data reconciliation time by up to 65%, enhances the timeliness of risk metric reporting by 40%, and improves auditability and regulatory compliance through immutable transaction logs and transparent model governance. The integrated Bayesian and machine learning models are anticipated to yield more accurate tail-risk estimates (expected shortfall) during extreme scenarios than traditional approaches, with superior detection of liquidity crunch indicators under rapid market dislocations. The study also anticipates demonstrating that data integrity and cross-institutional collaboration improve decision speed and risk escalation effectiveness in stressed conditions, while preserving information asymmetry where appropriate. The contribution to knowledge lies in operationalizing a blockchain-based risk analytics architecture tailored for banking under stress, combining architectural design with rigorous quantitative evaluation, and articulating governance mechanisms that reconcile competitive privacy with collective resilience. The main conclusion posits that blockchain-enabled risk analytics materially strengthens risk visibility, governance, and coordination during systemic shocks, without compromising data privacy. Recommendations include standardization of data schemas and risk indicators across banks, regulatory guidelines for cross-border data sharing in blockchain environments, and iterative refinement of the risk models within sandbox settings to support ongoing stress-testing programs.