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June 18, 2026
Credit Scoring
April 2, 2020
Alternative Data
Why Alternative Data Matters
Traditional data does not always provide lenders with a complete view of a consumer’s ability to meet financial commitments. This can be particularly limiting for people with thin or limited credit histories, as well as borrowers whose financial behaviour is not fully reflected in conventional credit records.
Research from Oliver Wyman’s Alternative Data and the Unbanked report highlights the potential of alternative data to improve credit assessment by strengthening risk separation and making more credit-invisible consumers assessable. By adding relevant non-traditional data to existing decision models, lenders can gain a more complete understanding of applicants and identify creditworthy consumers who may otherwise be overlooked.
Alternative data can provide additional information about financial activity, payment behaviour, assets, and other factors with predictive power. Common sources include:
- Utilities, including gas, water, and electricity
- Telecom services, including television, mobile, and broadband
- Rent payments
- Property and asset records, including the value of owned assets
- Public records beyond the information already included in standard credit reports
- Alternative lending payments, including instalment loans, rent-to-own arrangements, auto loans, and other forms of non-traditional credit
- Demand deposit account (DDA) information, including recurring payroll deposits, payments, account balances, and related activity
However, having access to more data is not the goal in itself. Alternative data creates value only when it delivers genuine incremental benefits beyond traditional bureau data. Those benefits should support both lenders and consumers rather than simply add more variables to a credit score.
For lenders, stronger data can improve risk separation and increase the number of applicants who can be assessed with sufficient confidence. This can help expand the pool of potentially lendable consumers while allowing lenders to operate within their established risk appetite.
Better information can also help lenders increase profitable lending opportunities, streamline processes that might otherwise require manual assessment, and reduce overall credit losses. A more complete view of prospective borrowers can also support more informed pricing decisions, enabling lenders to offer terms that better reflect an applicant’s assessed level of risk.
For consumers, these improvements can support broader and fairer access to credit, particularly for those who are underserved by traditional credit information alone.
Characteristics of a Good Alternative Data Source
The value of alternative data depends heavily on evaluating quality and freshness of alternative data at the source level. Some sources may offer strong predictive power but limited coverage, while others may be widely available yet provide less direct insight into repayment behaviour. Rent payment data, for example, can be useful for assessing consumers with limited credit histories, but fragmented reporting can restrict how consistently that information is available.
A strong alternative data source should therefore perform well across six key characteristics:
- Coverage: A useful alternative data source should provide broad and consistent coverage across the target population. For example, a source that captures information for only a small proportion of applicants may have limited value in a lending model, even if the data itself is highly predictive.
- Specificity: The data should relate clearly to the individual being assessed rather than relying mainly on broad household, geographic, or demographic indicators. Individual-level information can help lenders improve risk assessment by providing a more precise view of the applicant.
- Accuracy and timeliness: Alternative data should be reliable, current, and updated frequently enough to reflect recent behaviour. For example, regularly updated payment or account information may provide a more accurate assessment than records that are several months old.
- Predictive power: The information should have a meaningful relationship with the outcome being predicted, such as repayment performance or credit risk. A data source with strong predictive power can help improve risk separation and support more informed lending decisions.
- Orthogonality: Alternative data should ideally add information that is not already captured by traditional credit bureau data. For example, a behavioural or payment-related signal may improve a model if it contributes distinct insight rather than simply repeating information already available in a credit report.
- Regulatory compliance: Any alternative data source used in credit decisioning should meet applicable financial, privacy, and data protection requirements. Lenders should understand how the data is collected, processed, and used to ensure that it can be incorporated into decision models responsibly and compliantly.
How to Evaluate the Quality and Freshness of an Alternative Data Source
Evaluating the quality and freshness of alternative data requires looking at both its quality and how quickly it reflects changing behaviour. Start by checking coverage, accuracy, consistency, specificity, and predictive power across the target customer base. Freshness should be assessed by comparing how often the source updates with how frequently the underlying behaviour can change.
A monthly update may be sufficient for stable information, while faster-moving behavioural data may require more frequent refreshes. Lenders should also backtest the data against real repayment outcomes to confirm that newer information improves risk separation, complements traditional data, and adds measurable value to existing decision models.
Combining Multiple Alternative Data Sources
Combining alternative data sources can give lenders a more complete view of applicants than relying on a single source. For example, rent, telecom, utilities, transaction data, and behavioural data may each reveal different aspects of financial behaviour and credit risk. When these inputs are combined with traditional data, lenders can create a hybrid model that captures both established credit history and additional risk signals.
This approach can improve coverage, strengthen predictive power, and support better risk separation. Hybrid models often work best because they reduce dependence on any one source while adding complementary information to existing credit decisioning processes.
Looking Ahead: Embracing Alternative Data
According to a TransUnion survey, 34% of lenders already use some types of alternative data to evaluate both prime and nonprime borrowers. The use of alternative data is most prominent in credit card, auto loans, and consumer finance, as well as in the FinTech world but there are also signs of early adoption in the mortgage industry. 66% of lenders surveyed reported that they were able to lend to additional borrowers in their current markets and 56% reported access to new markets by using alternative data.
Major credit bureaus (Experian, Equifax, and TransUnion) are already starting to incorporate alternative data within their databases, through acquisitions and/or partnerships. Being a free market, lenders and data sources will surely work towards more widespread use of alternative data—after all, it opens new profit pools for both parties. However, the social benefits of using alternative data may be significant enough to warrant the use of legislation to accelerate the pace of adoption by mandating the reporting of selected alternative data.
How Credolab Applies These Quality Standards
Credolab applies these quality standards through real-time behavioural risk scoring, built on proprietary device and behavioural interaction metadata, analysed from smartphone and web environments. Its behavioural and device risk intelligence provides broad coverage while generating individual-level insights without processing personal data. By transforming device and behavioural metadata into behavioural features, Credolab supports strong predictive power and adds complementary signals to traditional data.
These insights can strengthen existing decision models, improve risk separation, and help lenders assess more applicants with confidence. The result is a behavioural intelligence layer that supports more informed credit decisions, higher approval potential, and better control of credit risk.
FAQs
How do I evaluate the quality and freshness of alternative data?
Assessing how to evaluate quality and freshness of alternative data involves reviewing coverage, accuracy, specificity, predictive power, update frequency, regulatory compliance, and performance against actual repayment outcomes.
What makes a good alternative data source?
A good alternative data source offers broad coverage, individual specificity, strong accuracy, timely updates, predictive power, regulatory compliance, and useful information beyond traditional bureau data.
What are the main characteristics of alternative data quality?
The main characteristics of alternative data quality are coverage, specificity, accuracy, timeliness, predictive power, orthogonality, and regulatory compliance, which together determine whether alternative data adds reliable overall decisioning value.
How often should alternative data be refreshed for credit decisions?
Refresh frequency should match how quickly the underlying behaviour changes. Fast-moving behavioural data may require frequent updates, while stable information can be refreshed less often.
Can alternative data replace traditional credit bureau data?
Alternative data should complement, not replace, traditional credit bureau data. Combining both can improve coverage, predictive power, and risk separation within existing credit decision models.
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