Regulatory Risk Audit Analytics
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Regulatory Risk Audit Analytics
SQL control tests on simulated banking data cover capital and RWA, ICAAP, ILAAP liquidity, and IFRS 9 staging and ECL using full populations rather than samples.
SQLPythonPandasNumPyIFRS 9
Key Highlights
- Flags reported-versus-recalculated differences and risk-appetite breaches that were not escalated.
- Consolidates exceptions into risk-rated findings with recommended next steps.
Tech Stack
SQLPythonPandasNumPyIFRS 9
Overview
A data-driven internal audit review using simulated banking data. It applies targeted control tests to regulatory and risk-management data; the repository describes simplified checks rather than full regulatory models.
Methodology
- Generate or load simulated banking data.
- Run SQL control tests across capital and RWA, ICAAP and risk appetite, ILAAP liquidity, and IFRS 9 staging and expected credit loss.
- Consolidate test exceptions with Python into a findings summary and audit note.
Key Features
- Tests include missing risk weights, RWA variances, unowned or unreviewed ICAAP assumptions, un-escalated risk appetite breaches, liquidity concentration and buffer issues, and IFRS 9 delinquency staging or ECL variances.
- Exports a risk-rated findings summary with recommended next steps.
Getting Started
The repository documents a Python setup and scripts for generating synthetic inputs and producing the findings summary.
pip install -r requirements.txt
python 01_generate_synthetic_regulatory_data.py
python 02_generate_audit_findings_summary.py