Recent ACCC fines have taught the industry a lesson about Product Reference Data

By Stuart Low, Founder and CEO at Biza.io

Under the Consumer Data Right (CDR), banks and non-bank lenders are obligated to share accurate Product Reference Data (PRD) using APIs and data schemas, defined by the Data Standards Body, and to maintain that data as it changes. This includes information such as interest rates, fees and charges, and eligibility criteria for banking products like credit cards and mortgages.

For most organisations, this isn’t an easy process to do manually. A change to interest rates of a certain product can mean several days of painstaking updates in large, unwieldy spreadsheets, costing time and money. Many of the problems with CDR data in the banking industry to date have been due to inaccurate or stale PRD.

With the threat of regulatory action if mistakes are made, as data holders under the CDR, banks and non-bank lenders must be getting PRD management right.

Why PRD is so important to the CDR

Aside from the threat of fines, mismanaging PRD undermines the success of the CDR as an ecosystem. This is because PRD is available via public APIs, unlike customer transaction data, which is only available to Accredited Data Recipients (ADRs). For example, PRD is widely accessed and used by comparison sites to enable consumers to compare banking products. Inaccurate PRD is an obvious issue that brings into question data quality in the CDR in general.

Findings from an ACCC consultation process on CDR data quality compliance found that “shortcomings in product reference data are making it difficult to use this data as a basis for a consumer-facing product or service.” According to the report, “one individual reported they had signed up to a savings account based on a rate disclosed through CDR, only to find it was incorrect.” This undermines the entire purpose of the CDR, which is to enable consumers to make more informed decisions by giving them control over their own data.

Common roadblocks to PRD accuracy and how to overcome them

For several reasons, maintaining up-to-date PRD has been seen as time-consuming and costly — and that’s before potentially facing fines for non-compliance. However, getting PRD right doesn’t have to be this complicated. Product management solutions can streamline PRD updates whilst reducing human errors and overall costs.

There are a number of common roadblocks preventing PRD accuracy that product management solutions can help to overcome:

• Many data holders are still making PRD changes manually, one by one. Aside from taking up valuable resources, this results in high error rates and is a likely source of recent fines from the ACCC. A product management solution allows for a consistent data maintenance process and allows organisations to retire complex spreadsheets.
• The inability to schedule product updates makes it hard for teams to catch errors before they go live. A product management solution allows you to schedule updates that can be reviewed and approved with less time pressure.
• Fees and charges are often replicated across multiple products. By using fee tables in a product management solution, if a fee changes, it will be automatically modified across all relevant products, eliminating manual errors.
• Changes made in spreadsheets are difficult to audit at a later date. A product management solution provides a comprehensive audit trail that can be reviewed and reported on by the business at a later date.
• It can be laborious to manage PRD for multiple brands at one time and there is no structured way to collate data from different sources.  A centralised product management solution can manage updates to multiple brands from the same. interface and can be integrated with source systems via well-defined APIs.

With increased scrutiny by the ACCC over the accuracy of Product Reference Data, data holders need to shift from archaic manual processes towards modern, automated and easy-to-use platforms that drastically reduce human error and mitigate risk. If not, the CDR will continue to face data quality issues that undermine the success of the ecosystem and hold back the positive gains we’ve made towards putting data back into the hands of consumers.