Data Solutions

Data Strategy for Mortgage Portfolio Analytics & Stress Testing

Challenge

A major APAC-based universal bank required a comprehensive data strategy and analytical framework to enhance the performance management of its national mortgage business. The bank’s finance division faced challenges in ensuring that stress testing, forecasting, and performance analytics were aligned to modern regulatory expectations and real-time business needs.

The primary objective was to build a driver-based financial model that could evaluate the bank’s mortgage performance, Net Interest Margins (NIM), and financial resilience under multiple macroeconomic scenarios. Additionally, the program needed to uplift the bank’s financial modelling capability, automate stress testing processes, and improve decision-making through actionable data insights.

Key challenges included:

• Manual, spreadsheet-based stress testing models that lacked scalability and automation.

• Fragmented data sources across finance, treasury, and risk systems.

• Difficulty linking macroeconomic variables to business performance outcomes.

• Regulatory pressure to enhance model governance, transparency, and explainability.

Solution

Harmonic Strategy was engaged as Lead Consultant to design a modern data strategy and financial modelling framework that integrated predictive analytics, AI-driven forecasting, and regulatory-aligned stress testing within a single, governed platform.

1. Financial Modelling & Stress Testing Framework

   • Developed a driver-based revenue and balance sheet model to project profitability across mortgage, retail, and business lending portfolios.

   • Designed multi-scenario stress testing frameworks assessing the bank’s financial resilience to macroeconomic shocks such as rate changes, credit stress, and funding disruptions.

   • Integrated stochastic simulations (Monte Carlo methods) to model tail risks and non-linear effects on NIM and profitability.

   • Embedded dynamic feedback loops between stress testing, forecasting, and pricing strategy modules.

2. Advanced Analytics & AI Integration

   • Deployed machine learning-based predictive models to simulate loan book behaviour under varying economic conditions.

   • Implemented AI-driven calibration engines to adjust models in real time as market data changed (interest rates, inflation, unemployment).

   • Enhanced credit loss forecasting using behavioural analytics, improving default probability and provisioning accuracy.

   • Introduced advanced scenario visualisation dashboards to present stress outcomes interactively to executive leadership.

3. Governance & Model Risk Management

   • Designed a Model Risk Management (MRM) framework aligned with APRA and Basel IV standards.

   • Introduced model validation processes, back-testing, and tiered model risk categorisation.

   • Established a centralised model repository with full version control, audit trails, and documentation of assumptions.

   • Aligned financial model governance with the bank’s enterprise risk and compliance architecture.

4. Technology & Infrastructure Modernisation

   • Transitioned all financial models to cloud-native infrastructure (Azure and AWS) for scalability and speed.

   • Introduced API-based data integration between finance, treasury, and risk systems for unified data access.

   • Implemented high-performance computing (HPC) clusters for large-scale risk simulations and NIM optimisation.

   • Enabled automated model validation and AI-driven anomaly detection for continuous quality control.

5. Performance Insights & Decision Enablement

   • Delivered executive dashboards visualising stress test results, NIM forecasts, and key profitability drivers.

   • Introduced scenario-based sensitivity analysis to identify top variables influencing mortgage profitability.

   • Provided actionable recommendations for deposit optimisation, funding mix management, and pricing adjustments.

   • Fostered collaboration between finance, data science, and risk functions through a shared data governance council.

Key Deliverables

• Enterprise Data Strategy for Financial Modelling & Stress Testing

• Driver-Based Revenue & Mortgage Profitability Model

• AI/ML-Powered Predictive Analytics Framework

• Model Risk Management (MRM) and Governance Policy

• Cloud-Native Modelling Infrastructure Design

• Executive Insight & NIM Performance Dashboard

• Scenario Simulation Toolkit (Macroeconomic, Credit, Liquidity)

Client Benefits

• Enhanced forecast accuracy and model transparency across the finance and risk functions.

• Reduced manual effort in stress testing by over 50% through automation and integration.

• Improved understanding of macroeconomic sensitivities on NIM, ROE, and NPAT.

• Strengthened regulatory compliance with APRA-aligned model validation and documentation standards.

• Enabled executive leadership to make faster, data-driven decisions with real-time stress scenario visualisation.

• Cultivated a culture of data literacy and advanced analytics adoption across finance teams.