The insurance industry is at an inflection point. Driven by changing driving behaviours brought on by the pandemic, consumers want the fairest and most accurate individualized insurance rate. Over the last couple of years, the conversation about consent-driven credit data has been reinvigorated as new provinces have either expressed an openness or moved toward a risk-based pricing model by leveraging the use of consent-driven credit data in auto insurance rating. While recognizing the power of credit data in insurance ratemaking, there are concerns about possible embedded biases around rating territory/geography, age, immigration status and income.
To help provide an objective view on the fairness of credit scoring and highlight any differences in the distribution of credit scores for risk segments commonly associated with the pricing of insurance policies, TransUnion conducted data-driven, analytical research on 11 million Ontarians. Our findings demonstrate credit score distributions are stable and equitable regardless of age, geography or economic shock brought on by the COVID-19 pandemic. This suggests whether an individual lives in a high or low-income area or one with high or low rates of immigration, or is aged 25 or 55, credit scores remain fair. Furthermore, they can empower consumers to provide insurers evidence of positive behavioural risk for a more accurate rate. As such, consent-driven credit scores can enable insurers to compete in underserved areas; lower competitive barriers by reducing industry costs; and lead to greater access and choice for consumers — all while cultivating long-term consumer loyalty.
A credit score is a behavioural indicator that objectively leverages the information in a credit report to predict the likelihood of a consumer failing to honour their credit obligations. The main factors in the calculation include: 1
Punctuality: Late payments, collections and bankruptcies can negatively affect a credit score
Balance: Totals owed to all creditors, how much is owed on particular types of accounts, and how much available credit has been used will have an impact on the credit score
Tenure: The longer the average duration of all loan accounts, the higher the credit score
Credit shopping habits: Higher volume of credit seeking behaviour is indicative of higher risk
While it’s essential for consumers to know what attributes go into a score, it’s equally important to note what does not go into the calculation of a score. TransUnion does not collect information on gender, marital status, income or racial data, and adheres to all provincial credit reporting legislation. Any data used through this research on income/immigration are based on publicly available census data.
Canadians are becoming more aware of the value of their credit score as there are progressively more opportunities to monitor, manage and leverage it for personalized product and service offerings from lenders, retailers and insurers. Driven by the expansion of consumer access to both credit data and credit education material, consumers have an opportunity to track their scores and promote good credit management practices, ultimately resulting in improved financial literacy and better overall credit scores.
Consent-driven credit data refines the predictive power of traditional rating algorithms used by insurance companies. The addition of this data helps improve portfolio segmentation and overall performance while also adding value for customers. Outside of pricing improvement and expedited customer onboarding, some other use cases of consent-driven credit data in insurance include marketing, fraud, identity authentication and portfolio management (which are not in the scope of this paper).
Territory and geographical bias. Based on our study of over 11 million consumers in Ontario —which analyzed credit scores across FSA and different geographic regions — we found the overall distribution of scores within each region follows the same pattern and is similar to the overall distribution in Ontario, concluding the geographical bias of credit scores is unfounded.
Age and youth bias. Our research showed age is not a factor in credit scoring, and young people can quickly build toward a good credit score after exhibiting positive, credit-servicing behaviour for 2–3 years. The conclusion is the credit score is not a function of age and any key differences in age cohorts can instead be attributed to tenure of credit history.
New to credit bias. While some new-to-credit customers receive lower credit scores due to volatile financial activities (often in the first six months), with credit management education and positive credit risk behaviours, one can quickly build an average credit score rating within 2–3 years.
New to Canada bias. Looking at new to Canada and low-income earners using regional Statistics Canada data, we found no deviations or outliers in credit distributions with respect to the rest of Ontario. Credit score distributions remained consistent across regions that, according to Statistics Canada Data, were high- and low-income areas, as well as high- and low-immigration areas.
Economic volatility and pandemic bias. Despite the perception the pandemic led to radically reduced credit scores, most Canadians (~85%) have seen no significant changes to their scores over the last year and a half.
Across industries, we’re observing a growing willingness by consumers to provide their consent to companies to access to their data when there’s an exchange of value. As evident from the analysis in our white paper, giving consumers the choice to use their credit information when seeking auto insurance can provide benefits to many people in an equitable way — regardless of age or geography. The goal of this paper is to support both the industry and consumers by highlighting the advantages of leveraging consent-driven credit data, and to provide information on how credit data can help ensure fair and affordable access to insurance products across all provinces.
In the modern digitized economy, it’s imperative to develop mutual trust and an understanding of how consumer data is a vital, predictive variable so consumers and the companies they interact with can transact with confidence and drive positive outcomes for all stakeholders.
Download the full white paper for more information on how consent-driven credit data provides behavioural consumer insights for insurance in a fair, equitable and unbiased manner.
1If a consumer disagrees with any of their reported accounts, they can open a dispute with TransUnion, which will investigate the claim and notify the consumer of the outcome.