Australian Banking Industry: Building a Fit-for-Purpose Credit Risk Engine

Overview

In the contemporary market of finance and banking, credit risk management is a crucial pillar for financial institutions, helping to assess client creditworthiness, minimise financial losses, and maintain overall economic stability. The quantification of credit risk assigns measurable values to the likelihood that a borrower will not repay a loan, drawing on both borrower-specific and market-wide factors. The concept is rooted in the idea that liabilities can be objectively predicted and valued to help protect lenders against loss.

Modern credit risk platforms are highly sophisticated systems, incorporating multiple inputs and a feedback loop that depends on accurate initial data and precise calibrations to deliver reliable outputs. Achieving this consistently is nearly impossible without a stable, modern, and finely tuned data platform. For one of Australia’s fastest-growing SME lenders, the need for such a platform became evident as existing systems could no longer support their growth.

Challenges

As the business scaled, it quickly outgrew its original solutions. The existing Credit Risk Engine faced significant challenges in speed, accuracy, and reliability of results. Processing times were a major concern, with legacy systems requiring up to 40 minutes per script, meaning daily transaction runs often stretched into the afternoon, long past the expected 9am deadline.

Data quality was another issue. Incorrect loan information and duplicate records undermined confidence in reporting. An attempt to introduce a new risk engine did not resolve these problems, as the system continued to operate on legacy platforms and fell short of the required accuracy. This left analysts with a heavy reconciliation workload and reduced trust in the results.

Compounding these challenges was the lack of accurate documentation. In a fast-paced environment with constantly evolving requirements, records were often outdated, incomplete, or entirely missing, further complicating operations. The result was an environment where inefficiencies were pervasive and risk management decisions were slowed by poor data quality and system delays.

Synogize’s Approach

Synogize’s approach was to establish a new, strategic data integration platform designed to handle complex datasets, enable transparent data cleansing, and expose quality issues in a systematic way. Working closely with the client’s data leadership and engineering teams, Synogize designed and implemented a fit-for-purpose, cloud-based data solution that addressed both the immediate challenges and the longer-term need for scalability.

Snowflake was selected as the core platform following a detailed vendor comparison. The solution introduced metadata-driven data warehousing to accelerate development, a control framework to support consistent and transparent data loads, and a reconciliation framework that automates testing and improve visibility of data quality issues.

In parallel, Synogize developed new credit risk metrics to strengthen the capabilities of the risk engine, including Loan-to-Value Ratio (LVR), Days Past Due (DPD), Expected Credit Loss (ECL), and Loss Given Default (LGD). Industry best practices in data warehouse automation, DevOps, testing, and quality assurance were embedded throughout delivery, ensuring both resilience and sustainability.

Results

The new Snowflake-based platform delivered tangible improvements to credit risk operations. Data processing times were reduced dramatically, enabling daily data loads to complete at the start of the business day rather than the end. This improvement not only supported timelier decision-making but also improved trust in outcomes.

Enhanced data quality and accuracy provided a stronger foundation for credit risk analysis, giving stakeholders confidence in the numbers underpinning strategic decisions. Most importantly, the platform offered a scalable and sustainable solution that aligned with the client’s growth trajectory and positioned them to meet evolving industry demands.

Conclusion

Through this engagement, Synogize transformed the client’s credit risk management operations. By addressing critical pain points in processing speed, accuracy, and scalability, the project delivered a modern, cloud-based data platform that restored confidence in reporting and enabled faster, more strategic decision-making.

This transformation not only resolved existing inefficiencies but also positioned the organisation for long-term growth, equipping them with the tools and practices needed to manage credit risk in a rapidly evolving financial landscape.