Data Solutions

Advanced Stress Testing & NIM Analytics for a National Mortgage Portfolio

Challenge

Following the successful mortgage performance analytics uplift (Case #6), the bank’s finance and risk leadership requested a deeper technical build-out of stress testing methodologies and Net Interest Margin (NIM) analytics, aligned to current APRA expectations and Australian market dynamics.

Key asks:

• Industrialise scenario, sensitivity, reverse, and liquidity stress testing for a mortgage-heavy balance sheet.

• Integrate IRRBB (APS 117), capital adequacy (APS 110), and liquidity (APS 210) views into a single governed modelling stack.

• Extend scenario coverage to climate risk (CPS 190/320-aligned) and emerging risks (cyber, geopolitics).

• Link stress results to pricing, funding mix, and balance-sheet optimisation—especially NIM at Line-of-Business level.

Solution

Harmonic Strategy designed a regulator-aligned, cloud-native stress testing and NIM analytics framework that fuses macro-financial modelling, stochastic simulation, MLOps, and model-risk governance into one operating model.

1. Regulatory-Aligned Methodology Stack

• Scenario testing: Top-down macro paths (GDP, CPI, unemployment, house prices, policy rate) plus institution-specific overlays (portfolio mix, geographic LVRs, deposit betas, wholesale reliance).

• Sensitivity testing: Single-factor shocks (rate, default, deposit runoff) to isolate key NIM and capital drivers.

• Reverse stress testing: “Breach-seeking” analytics to identify combinations that threaten capital/liquidity thresholds; tied to recovery options.

• Dynamic balance sheet & liquidity testing: LCR/NSFR survival horizons under deposit flight and wholesale market freeze; HQLA optimisation levers.

• IRRBB integration (APS 117): Parallel/steepener/flatteners mapped to NII and EVE; behavioural deposit duration and prepayment elasticity calibrated.

2. Advanced Modelling & AI/ML

• Macro-financial engine: Econometric core with balance-sheet dynamics; overlays for housing/mortgage cycles.

• Monte Carlo simulations: Thousands of rate/credit/liquidity paths to quantify tail risk and capital depletion probabilities.

• ML early warning indicators: Transactional stress signals, delinquency precursors, and roll-rate models to sharpen impairment forecasts.

• Climate scenarios (CPS 190/320-aligned): Physical and transition risk channels (collateral impairment, sectoral transition) wired into PD/LGD and collateral haircuts.

3. NIM Analytics at LoB Level

• Driver-based NIM model: Lending-rate and funding-cost engines (deposit beta ladders, wholesale curves, securitisation windows), asset-liability repricing gaps, and fee overlays.

• Pricing simulators: Elasticity-aware what-ifs for mortgage repricing, deposit campaigns, and hedging strategies; margin vs volume trade-offs.

• Funding-mix optimiser: Target deposit growth, wholesale tenor, and securitisation cadence to stabilise NIM under stress.

4. Model Risk Management & Controls

• MRM framework: SR11-7/APRA-consistent model inventory, tiering, validation, back-testing, challenger models, and periodic recalibration.

• Evidence & auditability: Full lineage, assumption register, versioning, and reproducible pipelines for supervisory review.

5. Cloud & MLOps Enablement

• Cloud-native pipelines: IaC, containerised runtimes, HPC for Monte Carlo, API connectors to finance/treasury/risk data stores.

• Automated validation & drift monitoring: Statistical tests, thresholding, and alerting into risk/finance dashboards.

Key Deliverables

• APRA-aligned Stress Testing Methodology Playbook (scenarios, sensitivities, reverse, liquidity/IRRBB integration)

• Driver-Based NIM Model (LoB granularity) with pricing & funding simulators

• Macro-Financial & Monte Carlo Engines with parameter library and scenario packs

• Climate Risk Stress Modules (physical/transition channels)

• Model Risk Management Policy and controls (validation, back-testing, documentation)

• Cloud/MLOps Architecture (IaC, CI/CD, monitoring)

• Executive Dashboards for capital, liquidity, NIM, and recovery levers

Client Benefits

• Regulatory confidence: Methodologies and evidence aligned to APS 110/117/210 and CPS 190/320 expectations.

• Sharper NIM resilience: Clear levers on pricing, deposit betas, wholesale tenor, and securitisation to protect margins.

• Faster cycles: 50–70% reduction in time to run complex scenario suites via automated, cloud-based pipelines.

• Actionable foresight: Reverse-stress outputs tied to concrete management actions and recovery options.

• Explainable models: Validated, back-tested, and fully auditable with challenger benchmarks.