Credit Scoring
May 13, 2025
How Alternative and Traditional Data Work Better Together
Be ready to explore how combining traditional and alternative data leads to smarter, more inclusive and more predictive credit scoring.
MD Americas, Chief Strategy Officer
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Lenders have relied on traditional credit data, such as credit scores, loan histories, and credit card repayments, to assess risk for decades. Yet, these backwards-looking metrics exclude millions of people, not just the unbanked and underbanked individuals.
Enter alternative data, the dynamic and predictive approach to risk assessments that is shaking up banking and fintech. Financial institutions are rewriting traditional creditworthiness rules by analysing a variety of alternative data, including device and behavioural data, utility bills, open banking and rent data.
Traditional data, though retrospective, has been the backbone of credit decisions for decades. Some examples used to assess credit risk include:
- Credit bureau scores: TransUnion, Experian, Equifax
- Loan repayment histories: Mortgages, car loans
- Financial statements: SEC filings, income reports for businesses and credit card repayment for consumers
Alternative data has become a dynamic and predictive approach that leverages non-traditional sources. It refers to information on behaviours, habits, interests, and transactions carried out by a person and obtained from non-traditional sources. Some examples include:
- Transactional data: E-commerce purchases, utility bill payments, telco top-up payment histories
- Device metadata: App ownership and installation patterns, device preference, contacts saving patterns, calendar and reminders creation patterns
- Behavioural metadata: Keystroke dynamics app interactions
While traditional data offers stability, alternative data brings innovation, the future of credit scoring lies in their synergy, a hybrid combination of both types of data sources. But how do traditional and alternative data perform in credit scoring?
This blog explores how these data sets compare as data sources and credit scoring models. We also delve into why combining the two offers the perfect solution for unlocking smarter, more accurate, and more predictive credit scoring.
Traditional vs Alternative Data and Credit Scoring: A Head-to-Head Comparison
In summary, traditional data's exclusionary and retrospective nature leaves millions underserved from limited opportunities and blind spots in risk visibility. Lenders fail to capture real-time financial resilience by relying solely on traditional data. This is where alternative data offers a dynamic and more predictive lens in assessing creditworthiness.
A brief perspective on the benefits and challenges
The advantages of using alternative data are clear. Leveraging alternative data enables banks and financial institutions to bridge the data gap securely, reduce credit default risk, and enhance risk algorithms. It also includes lenders anticipating movements and obtaining more accurate patterns of an applicant's willingness to pay.
However, using alternative data is not an easy solution. One of the biggest challenges posed by alternative data is data interpretation. With a wide variety of sources comes an increased difficulty in assessing data source quality to avoid high correlations and incur unnecessary costs. Poor data quality can lead to flawed decision-making processes and even harm the company.
For instance, raw device metadata, such as app usage frequency, can be misinterpreted without proper context. The challenge lies in distinguishing signal from noise. Not all data is equal, and poor-quality inputs can lead to flawed decisions.
The Approval Rate Difference
Using alternative data can more than double approval rates for credit-invisible individuals. In simulations, approval rates for credit-invisibles jump from roughly 15% under traditional scoring to between 31% and 47% when alternative data is used, according to a recent study.
For those with an existing credit history, approval rates remain largely unchanged at about 87%, as noted in the same study. These numbers show clearly that alternative data unlocks approvals for people who would otherwise be rejected.
With just traditional data, many qualified borrowers stay invisible, but alternative data impact on approval rates enables lenders to confidently approve a much larger share of applications.
How Does Alternative Data Complement Traditional Data in Credit Scoring?
1. Builds Credit Histories for the Credit Invisible
Integrating alternative data sources in credit scoring and underwriting processes helps credit invisibles build formal financial identities with credit bureaus. These alternative data sources already tap into people’s regular financial commitments, such as:
- Bills for utilities, telecommunications, and rent
- Alternative lending payments (e.g., microloans, peer-to-peer platforms)
- Demand deposit account information (e.g., recurring payroll deposits, savings patterns)
With this integration, alternative credit scoring transforms credit invisibles into scorable applicants by increasing approval rates and generating new and more complete client profiles.
Another advantage of alternative data impact on approval rates is that it turns “credit invisible” customers into approved customers by scoring previously unscorable applicants for loans. A study by Equifax in partnership with FICO, and referenced in The Federal Reserve Bank of Kansas City, found that alternative data made most unscorable applicants scorable, with up to half reaching scores of at least 620, a common approval threshold.
Another study by Policy and Economic Research Council (PERC), a non-profit research organisation, adds that 35–54 million Americans are credit invisible or thin-file.
Another study by PERC (The Brookings Institution Urban Markets Initiative) adds that utility data drops unscoreables from about 12% to 2%, while minimising risk and increasing credit limits by ~$2,500 and $ 1,100 on average. https://www.brookings.edu/wp-content/uploads/2016/06/20061218_givecredit.pdf
Lenders thus expand approvals safely using telecom, utility, rent, and bank data, alongside bureau data.
2. Impacts the 5Cs Framework
In our previous blog, Modernising Risk Part 2: How To Enhance Credit Scoring with Machine Learning, we highlighted the synergy between these data types and credit scoring processes. Specifically, this table shows how traditional credit scoring and alternative credit scoring boost predictive power by influencing Character and Capacity from the 5Cs of Credit framework:
By combining traditional methods with modern innovation, Credolab helps lenders fairly assess every applicant, even those who traditional underwriting models might reject. Credolab’s models specifically tackle Character and Capacity through behavioural indicators (BIs) and statistical indicators (SIs) derived from device and behavioural metadata.
BIs include financial responsibility, conscientiousness, risk tolerance, integrity, and honesty. SIs refer to features engineered from about 80,000 data points (containing raw metadata) collected by Credolab with the user’s consent and transformed into nearly 11 million features through a proprietary feature engine. BIs and SI combined compensate for the lack of traditional credit data, enhance credit model robustness and improve overall risk segmentation.
Hybrid models increase approval rates by supplementing any missing bureau data with BIs and SIs. These indicators prevent applications from failing due to thin credit files.
Instead of being declined for lack of history, more applicants now meet approval checks through verified behavioural and statistical signals. This reduces auto-rejections solely caused by data gaps.
As a result, a higher share of previously unscorable applicants move directly into the approved category through stronger Character and Capacity scoring.
3. Creates Synergy in Hybrid Credit Scoring Models
Reliability meets agility in hybrid models, creating the best of both worlds: a balanced approach to risk assessment. By combining the historical structure from traditional data with the dynamic and predictive insights of alternative data, lenders can achieve the following:
- Richer Risk Profiles: Merging credit scores with rent payment consistency
- Financial Inclusion: Serving thin-file customers via hybrid frameworks like the 5Cs of Credit
Credolab’s research underscores how combining data types allows companies to anticipate user and technology changes and impact predictive power. The table below shows hybrid risk model examples from Modernising Risk Part 2: How to Enhance Credit Scoring with Machine Learning:
Synergy in Action: How Hybrid Models Improve Approval Rates
Hybrid credit models improve approval rates by joining traditional bureau data with alternative data signals. This combined view helps lenders assess more applicants who would otherwise fall outside standard scoring rules.
When income flows, payment behaviour, and cash patterns are added to credit files, more applications meet approval checks. This synergy allows lenders to approve more borrowers with confidence while keeping decisions fast and accurate.
How Is Combining Data a Proven Credit Scoring Method?
Alternative data has become a proven method, allowing financial institutions to predict clients’ behaviours that build more effective growth projections, optimise decision-making and reduce the cost of risk.
This section highlights success stories across industries, such as Ride-Hailing companies, Banking and Consumer Finance companies, Digital Lenders, BNPL, and NeoBanks and Challenger Banks.
- Banks: Reduce defaults using device and behavioural metadata
- BNPL Providers: Increase approval rates for thin-file millennials
- Neobanks: Leverage geolocation data to verify income stability for freelancers
- Ride-Hailing Companies (e.g. Grab, Gojek and InDrive): Leverage alternative credit scoring so companies can rate a driver's creditworthiness and help them acquire a car. In this way, companies set up a virtuous circle where they ensure driver loyalty and improve their service, which further leads to happier customers.
For more clarity, our ebook, The Complete Guide to Alternative Credit Scoring, delves deeper into how alternative credit scoring, by leveraging alternative data, has become an ally for many industries. Here’s how:
Alternative data has expanded borrower coverage for stronger risk portfolio quality in Banks and Consumer Finance companies, increased risk visibility to cover untapped markets for Neobanks and Challenger banks, enhanced predictive accuracy through behavioural insights in BNPL and elevated credit rating accuracy in Digital lending. Furthermore, partnering with experts like Credolab helps to filter noise and ensure the ethical and accurate use of data is key.
For example, Credolab uses App Profile Categories to identify financial habits and potential risk indicators. This qualitative risk segmentation helps lenders to detect responsible spending patterns vs risk behaviours.
Some indicators of positive behaviours include installing finance management apps and productivity tools. In contrast, some indicators of negative behaviours include the presence of gambling and payday loan apps.
Key Metrics Proving Hybrid Models Work
- Approval Rate Transformation
Hybrid scoring increases approval rates by combining traditional bureau data with alternative signals, especially for thin-file and new-to-credit applicants.
These applicants who traditional models would have previously rejected now receive fairer assessment with real-time insights into spending and behavioural patterns.
Now, lenders can reach previously untapped segments that would otherwise be difficult to score, supporting more consistent approval volumes over time.
- Risk Remains Controlled
Hybrid models stabilise or improve default rates as lenders gain a more complete view of credit history and current repayment capacity.
This approach filters high-risk cases while advancing strong applicants with more balanced risk profiles.
With improved risk monitoring, lenders’ portfolio quality metrics stay within target thresholds while improving risk decision quality over time.
- Market Reach Expands
Hybrid scoring models expand market reach to include previously underserved and credit invisible individuals, like gig workers and new-to-credit consumers.
This creates broader portfolio diversity across demographics, regions, and income levels.
By reducing reliance on manual reviews, lenders can scale risk decisions and support stronger customer lifetime value through better-fit approvals.
The Future of Credit Scoring is Hybrid
The divide between traditional and alternative data is narrowing, and with it, credit scoring models have become fairer, smarter, more inclusive and more predictive. For forward-thinking lenders, the winning strategy is to embrace hybrid models that balance the stability of traditional data with the innovation of alternative data.
The result? Stronger risk assessments, modernised credit scoring, higher approval rates, increased predictive power and a competitive edge. In today’s financial landscape, hybrid models are no longer an option. They are the new standard.
Ready to transform your strategy? Explore Credolab’s risk solutions here.
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