Banking supervisors and firms will gain more from fixing data, governance and explainability than from marginally better black‑box models; regulators should prioritise stress‑aware, auditable and privacy‑preserving analytics over raw predictive accuracy.

Main Finding

Predictive analytics has become central to banking regulation, supervision, and portfolio oversight. The paper argues that further gains will come less from incremental algorithmic improvements and more from strengthening governance, data infrastructure, model credibility under supervisory challenge, and deploying methods that are stress-aware, explainable, and privacy-preserving.

Key Points

• Scope: A broad survey across three interlocking domains — regulatory reporting, prudential supervision, and portfolio oversight — linking methods to institutional and regulatory use cases.
• Technical lineage: Traces evolution from classical statistical credit models (discriminant analysis, structural default frameworks) to modern machine-learning ensembles and hybrid approaches.
• Regulatory context: Situates methods within Basel prudential standards, IFRS 9 expected credit loss provisioning, BCBS 239 risk-data aggregation principles, and supervisory model-risk expectations.
• Seven application clusters surveyed:
• Credit and default prediction
• Expected credit loss (ECL) estimation
• Stress testing (macro-to-micro scenario analysis)
• RegTech and SupTech automated reporting
• Anti-money-laundering (AML) and fraud analytics
• Portfolio monitoring and early-warning systems
• Explainability and model risk management
• Deliverable: A structured mapping (table) linking techniques (statistical, ML, causal, privacy-preserving) to supervisory use cases and constraints.
• Open constraints highlighted: fragmented/low-quality data, tension between predictive accuracy and interpretability under regulatory scrutiny, procyclicality from point-in-time forecasts, model-risk governance shortfalls, and unclear accountability for automated decisions.
• Forward agenda: Emphasizes privacy-preserving supervisory analytics, machine-readable regulation, causal and stress-aware modelling, and human-in-the-loop governance as priority research and implementation areas.

Data & Methods

• Type of study: Survey and synthesis (conceptual, methodological, and regulatory), not an original empirical experiment.
• Methods used:
  • Literature synthesis across econometrics, credit risk modelling, machine learning, and regulatory practice.
  • Historical/technical tracing of model families (classical credit models → structural default models → ML ensembles).
  • Taxonomy and clustering of applications into seven use cases.
  • Construction of a technique-to-use-case mapping table to illustrate fit, trade-offs, and constraints.
  • Diagnostic discussion of deployment barriers and governance issues, and a forward-looking research agenda.
• Evidence base: Aggregated findings from academic literature, supervisory guidance (Basel/BCBS documents), accounting standards (IFRS 9), and industry practice examples (RegTech/SupTech deployments). The paper synthesises methodological results and regulatory texts rather than presenting new microdata analyses.

Implications for AI Economics

• Research priorities:
  • Move beyond pure predictive performance metrics to evaluate models on governance robustness, interpretability under audit, and resilience to macro shocks.
  • Develop causal and stress-aware modelling frameworks to reduce procyclicality and to link micropredictions with macro scenarios.
  • Advance privacy-preserving techniques (federated learning, secure multi-party computation, differential privacy) tailored to supervisor–bank interactions.
  • Design metrics and tests that capture model credibility under supervisory challenge (explainability diagnostics, adversarial/robustness testing, scenario-consistency checks).
  • Explore the economics of machine-readable regulation and automated compliance: cost-benefit trade-offs, adoption incentives, and market structure effects.
• Policy and supervisory implications:
  • Supervisors should invest in SupTech that combines predictive analytics with secure data-sharing and human oversight rather than full automation.
  • Regulatory frameworks need clearer expectations on interpretability, accountability, and model governance for ML-driven decisions (e.g., provisioning, capital, AML actions).
  • Address systemic risks from algorithmic procyclicality through stress-aware policy tools and macroprudential backstops.
• Industry and market design:
  • Banks’ returns to investing in advanced analytics will hinge on data quality, integration, and governance capacity; smaller institutions may need shared infrastructure or outsourced SupTech solutions.
  • Standardising model validation and machine-readable rule representations could lower compliance costs and improve cross-institution comparability.
• Broader economic effects:
  • Widespread deployment of predictive analytics affects credit allocation, provisioning cycles, and detection of systemic stress — with distributional consequences across firms and households depending on model design and regulatory calibration.
• Concrete research questions prompted by the paper:
  • How can causal inference methods be integrated into supervisory stress testing to produce counterfactual-safe policies?
  • What governance structures best trade off predictive accuracy and auditability in regulatory settings?
  • How do privacy-preserving supervisory architectures affect the precision of systemic risk measures and the incentives for banks to share data?
  • What are robust metrics for quantifying and limiting algorithmic procyclicality in provisioning and credit allocation?

Summary: For AI economics, the paper reframes the agenda: progress depends less on black‑box accuracy gains and more on creating institutional, data, and regulatory ecosystems that render predictive models reliable, interpretable, and safe for prudential decision-making.