Despite the insurance industry’s robust tradition of collecting and analysing internal data to assess risk and price policies, the richness of external data sets like credit data often remains underutilised in Australia. The absence of comprehensive insights into the validity, financial stability and performance of business customers hampers decision-making processes spanning pricing strategies, risk assessments, claims management, operational efficiencies and customer acquisitions.


Equifax collaborated with a leading commercial insurer to validate the hypothesis that Equifax credit data offers distinct advantages to insurance operations, risk pricing and portfolio insights. 

As one of Australia’s largest credit bureaus, our data sources are extensive – we have one of the largest databases of its kind in Australia, boasting more than 19.4 million credit-active individuals, 3.6 million businesses and companies and 3.4 million sole traders. Equifax commercial credit data encompasses a spectrum of financial and risk indicators, including operational longevity, default history, credit inquiries, trade payment performance, and directorship details. 

Key focus areas:
  • Insurance operations:

Information insurers collect about their policyholders, including self-declared information, are prone to inaccuracies, gaps and inconsistencies. Even if correct at the point of entry, this information may cease validity midway along the contract period if the policyholder’s details change. Data integrity is critical for due diligence and minimising claims processing delays and policy mispricing risks.

To gauge the accuracy and completeness of the data held by the insurer, we tested and validated a representative sample of their internal customer data against Equifax credit data. This process substantiates key details about the business and the people behind the business, such as the business name, trading address and ABN. 

Two data records might appear dissimilar at first glance, but are the same on closer investigation. The machine learning algorithms used in Equifax data-matching technology are sophisticated enough to work across disparate data sets to compare, identify or merge related entities with an approximate 97% match rate. 

  • Risk pricing

The accuracy and completeness of data are critical for assessing risk and pricing premiums accurately. Credit data is an untapped source of intelligence that can help generate a more complete customer profile to enhance the predictiveness of claims models. To test this predictive uplift, Equifax augments the insurer’s predictive model with the additional attributes of credit data. 

Equifax commercial credit data draws on a broad range of data points, joining the dots between a company’s trading history, its directors, shareholders and associated entities to reveal vital warning signs about financial stability and performance. Credit enquiry data, for example, is a highly predictive attribute in risk decisions, providing a red flag to either fraudulent activity or inability to repay debt.  

  • Portfolio insights

Portfolio management creates the opportunity to improve product competitiveness, customer engagement, segmentation and retention. To identify where credit data may be of specific value, Equifax credit data was integrated into the insurer’s portfolio analytic framework. 

With this enhanced view of the market, insights can be leveraged to benchmark competitors for improved marketing campaigns. For example, by pinpointing which segments of the market to expand into and which are less favourable. Identifying precise areas where data insights can elevate these functions is an important driver of sustained growth and competitiveness. 


Experimental findings substantiate the hypothesis that Equifax credit data confers distinct advantages across insurance operations, risk pricing and portfolio insights. Key findings from a representative sample of policy, risk and claims data revealed specific enhancements in validation, risk modelling and marketing strategy. More data and tests are needed to truly validate the consistency of these estimates, but early results are promising of potential uplift with further exploration and refinement.

  • Validation:

Validation leveraging Equifax credit data identified many thousand  invalid ABNs, along with new ABNs and new ACNs. This is an example of the additional data completeness and quality that validation brings, filling gaps in knowledge and improving clarity for more accurate decision-making.

  • Risk modelling:

Incorporating Equifax credit data into the insurer’s claims model resulted in an upward of 20% uplift in claims predictiveness (at least 10 points Gini uplift) based on best guess estimates from initial experimentation with the data provided. This heightened predictive power allows underwriters to expedite decision-making processes with increased pricing accuracy, while mitigating risks associated with misinterpretation and non-disclosure. Integrating accurate credit data also facilitates streamlined application processes and aids in combating fraudulent claims.

  • Marketing strategy:

Insights derived from Equifax credit data includes the discovery that around 40% of the insurer’s portfolio is concentrated within select ANZSICs, indicating potential over-indexing in certain sectors. The amalgamation of credit data with internal datasets in portfolio management equips insurers with actionable insights to drive customer retention, identify value-added services and foster customer loyalty. 

Dr. Carlos Leung, Principle Data Analytics Designer at Equifax comments “As insurers integrate our credit data with existing datasets, there is real potential to unlock a myriad of benefits across the value chain, positioning credit data as a potent source of competitive advantage.”

Talk to an Equifax Insurance Solutions specialist today about how we can partner with you to turn challenges into improvements across the insurance policy lifecycle. Unique, data-driven Equifax Insurance Solutions can add value to your processes, reduce friction, manage risk, improve fraud detection and improve your customer experience and service levels.

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