Key Takeaways
- Alternative credit scores solve different underwriting gaps. Some emphasize specialty finance history, while others incorporate banking activity, utility and telecommunications payments, alternative credit-seeking behavior, or lender-specific portfolio data.
- The best fit depends on what your existing underwriting process is missing. A lender trying to score thin-file applicants may need different information than one trying to improve risk differentiation among already-scorable borrowers.
- Standardized and customized scores require different levels of investment. Off-the-shelf scores can be incorporated into an existing decision process, while lender-specific models generally require more data, validation, governance, and ongoing monitoring.
- More data is not inherently better. The additional information should improve a lender’s ability to assess risk rather than duplicate signals already captured elsewhere in the decision process.
- Provider integration is only one part of implementation. Lenders should also confirm product availability, commercial requirements, compliance responsibilities, and how the score will interact with their existing credit policies and decision rules.
Traditional bureau scores give lenders a useful snapshot of an applicant’s credit history, but that snapshot can be incomplete. Thin-file consumers and borrowers whose financial activity falls outside conventional credit channels may have repayment behavior that traditional credit data does not fully capture.
Alternative credit score companies for lenders can help fill those gaps by drawing on information beyond conventional bureau data. Depending on the provider, that may include specialty finance repayment history, telecommunications and utility payments, bank account activity, alternative credit-seeking behavior, or other signals that provide additional context for assessing credit risk.
This guide compares five of the best alternative credit score companies for lenders in 2026:
- Experian Clarity Services
- TransUnion
- Equifax
- LexisNexis Risk Solutions
- Zest AI
We’ll compare the information and scoring approaches each provider offers and where they may fit within an underwriting process.
Best Alternative Credit Score Companies for Lenders Compared
| Company / Solution | Primary Alternative Data | What the Score Adds | Traditional Credit Required? | Model Type | Potential Underwriting Use | Key Consideration |
| Experian Clarity Services / Clear Credit Risk Score 2.0 | Alternative finance inquiries and repayment data | Additional risk information based on activity outside traditional credit products | No | Standardized score | Assessing applicants with non-prime or alternative-finance histories | Most useful when specialty finance behavior is relevant to the applicant population |
| TransUnion / TruVision Alternative Bank Risk Score | Checking and banking activity | Adds bank-account behavior that may not appear in a conventional credit file | No | Standardized score | Evaluating applicants with limited traditional credit history | Requires access to the banking data used by the score |
| Equifax / OneScore | Specialty finance, telco, utility, and optional traditional credit data | Combines several payment and credit signals into a single risk score | No | Standardized multi-data score | Adding alternative payment behavior to credit-risk assessment | Available data and configuration can affect which signals contribute to the score |
| LexisNexis Risk Solutions / RiskView Optics and RiskView Spectrum | Alternative credit-seeking behavior, non-credit events, and optional tradeline data | Provides additional segmentation when conventional bureau information is limited | No | Standardized score | Scoring thin-file, no-hit, or otherwise difficult-to-segment applicants | Optics and Spectrum use different data configurations, so lenders need to match the product to the use case |
| Zest AI / Client-tailored credit models | Lender performance data, credit attributes, and selected alternative data | Builds risk differentiation around the lender’s own applicant and performance data | Depends on model design | Customized machine learning model | Developing a portfolio-specific underwriting model | Requires substantially more model development, validation, governance, and monitoring than adopting an off-the-shelf score |
The main difference among these options is the information each one brings into the underwriting process. Experian focuses on alternative-finance histories, TransUnion on banking activity, Equifax on multiple payment and credit data sources, and LexisNexis on credit-seeking and non-credit events. Zest AI takes a different approach by building a lender-specific model that can incorporate alternative data when testing supports its use.
Experian Clarity Services: Clear Credit Risk Score 2.0
Experian Clarity Services collects inquiry, account, and payment data from alternative financial services providers, including small-dollar installment, single-payment, auto title, and rent-to-own lenders. Because these accounts may not be reported to traditional credit bureaus, Clarity can capture borrowing and repayment behavior that a traditional credit report may not show.
Clear Credit Risk Score 2.0 uses patterns in this data to estimate the likelihood of future delinquency. Lenders receive a standardized risk score, eliminating the need to assess individual alternative-finance accounts separately and allowing it to be used alongside the credit information and decision criteria already in their underwriting process.
The score’s value depends on the applicant population. A borrower with substantial alternative-finance activity may have a meaningful Clarity history, while another applicant may have little or no relevant information in the database. Lenders should test both coverage and predictive performance within their own applicant population before incorporating the score into credit policy.
Best Fit For
Experian Clarity Services is most relevant to lenders whose applicants use alternative financial products that may not appear fully in traditional credit files. That can include consumers with histories involving small-dollar installment loans, single-payment loans, title loans, or rent-to-own products represented in the Clarity database.
TransUnion: TruVision Alternative Bank Risk Score
TransUnion at a Glance
Data analyzed: TruVision Alternative Bank Risk Score uses checking and banking activity applicable to short-term lending to assess consumer credit risk.
What the score predicts: The model uses banking behavior to predict the likelihood that a consumer will default on a loan or other credit obligation.
Consumers evaluated: TransUnion designed the score to provide additional risk information for thin-file, no-file, and subprime consumers.
Data platform: The banking and short-term lending data used by the score is housed on TransUnion’s OneTru platform.
TruVision Alternative Bank Risk Score derives its risk signal from consumers’ banking activity. The model analyzes patterns in checking and banking data associated with short-term lending and converts them into a score estimating the likelihood of default.
For example, an applicant may have little conventional credit history but enough account activity to provide a record of financial behavior. TruVision can incorporate that activity into its estimate of the applicant’s likelihood of default. Experian Clarity, by comparison, bases its score on borrowing and repayment histories from alternative financial products.
Best Fit For
TruVision Alternative Bank Risk Score may be a good fit for lenders that want to use banking behavior to assess thin-file, no-file, or subprime applicants, particularly when conventional credit information provides limited risk differentiation.
Equifax: OneScore
Equifax OneScore at a Glance
Data combined: OneScore brings together traditional credit data with alternative data that can include telecommunications, utility, specialty finance, and other payment information.
Alternative data coverage: Equifax says OneScore incorporates data on more than 191 million U.S. consumers, including information not typically found in traditional credit files.
What the score predicts: OneScore estimates the likelihood that a consumer will become 90 or more days past due within 24 months.
Score range: OneScore uses a 300 to 850 score range, making its output familiar to lenders accustomed to traditional credit scores.
Model methodology: Equifax developed OneScore using advanced statistical modeling to combine traditional and alternative credit information into a single risk score.
OneScore combines traditional credit information with alternative data that can include telecommunications, utility, and specialty finance payment histories. These inputs feed into a single 300-to-850 risk score rather than producing separate scores for each data type.
For example, an applicant may have a conventional credit file as well as a history of utility, telecommunications, or specialty finance payments. OneScore can incorporate the available information into the same model and estimate the likelihood that the consumer will become 90 or more days past due within the following 24 months.
Best Fit For
Applicants who have conventional credit files plus alternative payment histories that could contribute additional information to the risk assessment. OneScore may suit lenders that want those sources evaluated within a single standardized score instead of adding a separate alternative-data score to their underwriting process.
LexisNexis Risk Solutions: RiskView Optics and RiskView Spectrum
LexisNexis Risk Solutions at a Glance
Two scoring models: RiskView Optics uses alternative credit-seeking behavior and non-credit events, while RiskView Spectrum adds traditional tradeline credit data to those same inputs.
Alternative data analyzed: RiskView draws on credit-seeking activity plus non-credit information such as professional licenses, public records, and asset ownership.
What the scores predict: Both models use consortium modeling to estimate an applicant’s likelihood of default within the next 12 months.
Applicants without traditional scores: LexisNexis reports that RiskView Scores can score more than 90% of applicants who do not have a traditional credit score.
Score format: RiskView Optics and RiskView Spectrum produce three-digit credit risk scores.
RiskView Optics and RiskView Spectrum use different combinations of traditional and alternative credit information. Optics analyzes alternative credit-seeking behavior and non-credit events without traditional tradeline data. Spectrum adds tradeline information to those inputs so both types of data contribute to the score.
LexisNexis draws its alternative signals from information about how consumers seek credit and from non-credit events such as asset ownership, professional licenses, and public-record activity. Its consortium models analyze these signals and produce a three-digit score representing the applicant’s likelihood of default within 12 months.
The two models let lenders match the score to the data already used in underwriting. For example, a lender evaluating an applicant without a traditional credit score could use Optics to assess risk based on alternative information. If traditional tradeline data is available and the lender wants to incorporate it into the same assessment, Spectrum includes it in the model.
Best Fit For
RiskView Optics: Applicants with little or no traditional credit information who can be assessed using alternative credit-seeking and non-credit data.
RiskView Spectrum: Applicants for whom the lender wants alternative signals and traditional tradeline information evaluated within the same score, including near-prime and prime applicants.
Zest AI: Client-Tailored Credit Models
Zest AI at a Glance
Model design: Zest AI builds machine learning underwriting models tailored to an individual lender’s borrowers, products, and risk objectives rather than providing one standardized national credit score.
Data analyzed: Models can use existing bureau data and lender-provided data, with alternative or transactional data incorporated when it adds value to the specific model.
How the data is modeled: Zest AI says its models can begin with hundreds of inputs, engineer them into thousands of potential features, and select the few hundred with the greatest predictive value.
Fair-lending methodology: Zest AI incorporates fair-lending testing and bias-reduction techniques into model development, including less discriminatory alternative searches and adversarial debiasing.
Ongoing model management: Zest AI provides active model monitoring and ongoing support after deployment rather than treating model development as a one-time exercise.
Zest AI builds machine learning credit models for individual lenders rather than offering a standardized alternative credit score. Model development starts with the lender’s borrower population, lending objectives, and available data. Inputs can include bureau information, first-party data, and alternative or transactional data when testing supports their use.
Its methodology can start with hundreds of data inputs and generate thousands of potential model features. For example, instead of considering only a borrower’s current credit utilization, the model can examine how utilization has changed over time or how it relates to other tradeline information. Zest AI then selects the features with the greatest predictive value for the lender’s portfolio.
Alternative data is one potential input, not a requirement of the model. Zest AI recommends testing a new data source to determine whether its predictive contribution warrants the cost, integration work, compliance requirements, and additional model complexity. The resulting model is specific to the lender and may incorporate alternative data when testing shows that the data contributes useful predictive information.
Best Fit For
Zest AI is designed for lenders seeking a customized modeling approach, including those that want to test whether alternative or transactional data contributes predictive information beyond their existing inputs.
Bring Alternative Credit Data Into Your Existing Lending Workflow
The best alternative credit score companies for lenders can provide additional ways to assess risk when traditional credit information leaves important gaps. The stronger fit depends on whether the provider’s data and scoring approach add useful information to the lender’s existing underwriting process.
That integration does not have to require a separate underwriting workflow. defi SOLUTIONS supports integrations with Experian, TransUnion, Equifax, LexisNexis Risk Solutions, Zest AI, and other credit and alternative-data providers, allowing lenders to bring third-party scores and data into their existing origination and decisioning processes.
With the right integrations, lenders can determine when to request an alternative score, how it should interact with other credit information, and how the resulting data should inform automated or manual underwriting decisions.
Book a demo with defi to learn how to incorporate alternative credit data and scoring into your existing origination workflow.
Frequently Asked Questions
Can a lender use more than one alternative credit score?
Yes. A lender may use different scores or data sources for different borrower populations or underwriting stages. For example, one alternative score might be triggered when an applicant has insufficient traditional bureau information, while another source could be used when additional verification is required.
However, using multiple scores does not automatically produce a better decision. Lenders should determine whether the additional score adds genuinely new information, whether its variables overlap with existing models, and how it will handle conflicting risk signals.
How should lenders test an alternative credit score before using it in production?
Historical testing can show whether a new score improves decisions relative to the lender’s existing approach. Useful measures may include approval rates, delinquency and loss performance, risk separation across score bands, and the amount of incremental predictive information the new score contributes.
Testing should also reflect the populations for which the score will actually be used. Strong overall performance can conceal weaker results within particular borrower segments, loan products, or risk tiers.
Do alternative credit scores change adverse action requirements?
No. Using alternative data or a complex scoring model does not remove a lender’s obligation to provide the specific principal reasons for an adverse action when required under ECOA and Regulation B. The CFPB has stated that creditors cannot rely on an algorithm’s complexity as a reason they cannot explain a credit decision.
That makes explainability an important consideration when lenders evaluate alternative scoring models, particularly those using machine learning.
What should lenders monitor after implementing an alternative credit score?
Implementation should not be treated as the end of the evaluation. Lenders can continue tracking whether the score separates higher- and lower-risk borrowers as expected and whether performance changes as economic conditions, borrower populations, or lending strategies change.
Useful monitoring areas include approval and decline patterns, delinquency and loss rates by score band, overrides, changes in score distribution, adverse action reasons, and the incremental value the alternative score provides relative to existing decision inputs.
What should lenders ask about the data behind an alternative credit score?
The score itself is only the output. Lenders should also understand where the underlying data comes from, how frequently it is updated, which consumers it covers, whether consumer permission is required, and how inaccurate information can be disputed or corrected.
Those questions are especially important when comparing scores built from very different sources. Two products may both produce a three-digit risk score while relying on fundamentally different views of a consumer’s financial behavior.
