Aperture Data Studio in Retail Banking
Reduce risk and unlock the true value of your data with well-governed, accurate and trusted data.
Profile, standardise, cleanse, govern, report and transform your data
Aperture Data Studio empowers organisations to deliver trusted data. Our intuitive, quick-to-deploy, and transparent data intelligence platform includes robust data quality and governance capabilities to help you improve, control, and govern your data.
It enables everyday users to quickly and easily develop sophisticated workflows that incorporates machine learning algorithms for automatic data tagging and enriches the data using globally-curated data sets from Experian. This ensures data quality and helps govern adherence to data standards.
Align Reporting Metrics
Use the data catalogue and dictionary to define and standardise key metrics—such as CAR, Tier 1 Capital Ratio, and EAD—ensuring consistency across Risk, Finance, and Regulatory teams.
Ensure Compliance with Regulatory Standards
Support compliance with Basel III, BCBS 239, APRA guidelines, and Australian Privacy laws through robust data governance and traceable data quality practices.
Detect and Remediate Data Issues in Regulatory Reporting
Identify and correct data quality issues that impact Capital Adequacy reporting (e.g. EAD, LGD). Reduce manual overheads and ensure auditability of data used in regulatory submissions.
Improve Debt Collection Outcomes
Pinpoint customers who fail early reminder contact due to outdated or inaccurate contact data. Remediate data issues to improve contactability and enhance collections performance.
Empower Automation in Credit Risk Decisioning
Ingest and validate critical customer domain data used in credit limit setting, risk forecasting, and exposure calculations. Automate data quality rules to ensure decisions are based on clean, reliable data.
Support Risk Modelling with High-Quality Data
Ensure customer and product domain data is free of duplicates, standardised, and enriched with metadata. Enable accurate model development, and deployment for credit risk forecasting and provisioning.
How your dashboard could look, with an example of three levels of Data Quality reporting
- Ingest credit risk decisioning data along with critical customer domain data to automate data quality rules and continuously validate data for credit risk decisions.
- Identify customers failing the early reminder contact for debt collection, enabling detection and remediation of data quality issues.
- Support Risk Management teams with capital measures such as EAD, LGD, to assess the amount of capital to cover losses from various risks, (credit, market, operational) based on internal models.
- Detect data quality issues and remediate as inputs into Capital Adequacy calculations and reporting obligations.
- Identify any critical data elements in consumer domain data that have been unintentionally left out of core risk systems.
- Build out a consistent set of definitions for key reporting metrics that’s supported by the credit risk data model using a data catalogue and data dictionary. For example, regulators are interested in Capital Adequacy Ratio (CAR), Tier 1 Capital Ratio and Total Capital Ratio.
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