June 18, 2026
Credit Scoring
June 16, 2025
Credit Scoring
Discover how alternative credit scoring uses behavioural and device data to redefine creditworthiness by boosting predictive accuracy, reducing bias, and expanding credit access.
Globally, 1.4 billion adults remain excluded from formal credit systems (World Bank). What if their financial potential could finally be unlocked?
Alternative credit scoring has rewritten the rules of financial inclusion and increased predictive power. It has empowered underserved populations and sharpened predictive accuracy in risk assessments.
Traditional vs. Alternative Credit Scoring: Bridging the Data Gap
Traditional Credit Scoring: Limitations and Blind Spots
Traditional credit scoring uses traditional data to assess credit risk. Some examples include credit bureau scores (e.g., TransUnion, Experian, Equifax), loan repayment histories (e.g., Mortgages, car loans), and financial statements (e.g., SEC filings, income reports for businesses, and credit card repayment for consumers). While effective for borrowers with established credit histories, this approach struggles to serve individuals globally who lack formal credit records.
The limitations, however, are twofold:
1. Exclusion of thin-file or no-credit-file customers
These individuals typically make up the unbanked or underbanked population, and the reliance on historical financial data has created blind spots and left lenders unaware of real-time behaviours, such as consistent utility bill payments or responsible mobile money usage.
2. Perpetuation of data asymmetry
Outdated or incomplete information has only widened the data gap between lenders’ findings and a borrower’s actual financial state. This gap is also known as data asymmetry. Traditional scoring methods fail to capture real-time financial behaviour, leading to the misclassification of borrowers due to limited historical data.
In essence, traditional credit scoring systems limit opportunities for individuals, create blind spots in risk visibility and limit access for deserving borrowers.
Alternative Credit Scoring: A Modern Solution
Non-traditional credit scoring, now more commonly referred to as alternative credit scoring, utilises non-traditional data to evaluate creditworthiness.
Some examples include transactional data (e.g., open banking, cashflow data, utility bill payments and telco top-up payment histories), device metadata (e.g., App ownership and device preferences), and behavioural metadata (e.g., keystroke dynamics and app interactions).
Unlike traditional models, this approach bypasses historical credit files and instead analyses real-time financial behaviours to build dynamic borrower profiles. By leveraging real-time data enrichment to create comprehensive profiles, lenders gain a deeper understanding and can make alternative credit decisioning more accurate.
Each alternative data source, with its specific pros and cons, as well as its unique use cases, provides a holistic view of an individual’s financial behaviour, allowing lenders to gain a 360-degree view of applicants’ financial habits.
The result? A more inclusive, accurate and predictive credit scoring system with higher approval rates.
The Predictive Power of Alternative Data
Alternative data has become a valuable asset in credit scoring. It has only demonstrated its importance in unlocking real-time insights, enhancing inclusivity, increasing predictive power, and modernising dynamic risk assessment. By capturing dynamic financial behaviours, alternative credit scoring bridges gaps left by traditional models, making previously credit-invisible borrowers scorable.
But how exactly does this translate into tangible benefits?
How Alternative Credit Scoring Works: Data, Models, and Decisioning
Alternative data refers to information collected from non-traditional sources, which is central to understanding what is alternative credit scoring. In credit scoring, this can include transactional data, device and behavioural metadata, utility bill payment patterns, telco top-up histories, app usage patterns, keystroke dynamics, battery charging habits, contact saving patterns, and calendar or reminder creation patterns.
Unlike traditional data, which often depends on past credit relationships, alternative data captures a wider range of financial and non-financial behaviours. This gives lenders a more dynamic view of creditworthiness, especially for applicants with limited or no formal credit history.
Three key drivers fuel the adoption of alternative data:
- Greater affordability: The cost of collecting, storing and processing alternative data has reduced significantly, making it easier for lenders to use wider data sources without increasing operational complexity.
- Improved technology: Machine learning algorithms can analyse large volumes of alternative data, filter out noise and identify meaningful behavioural patterns that may support more accurate credit risk assessment.
- Higher demand: Lenders need more inclusive and predictive risk models that can assess thin-file, no-credit-file, unbanked and underbanked customers with greater confidence.
Alternative credit scoring has proven to produce benefits, specifically higher approval rates and new and more comprehensive client profiles. In essence, alternative credit scoring enhances opportunities for individuals and improves risk visibility, leading to higher approval rates and more fair and accurate risk assessments. For example, Credolab’s platform leverages device and behavioural metadata to build comprehensive borrower profiles:
Example: Credolab’s Data-Driven Insights
To illustrate, let’s explore how Credolab uses device and behavioural metadata to refine credit risk segmentation.
These tables (device and behavioural insight) highlight the relevance of each data point regarding creditworthiness, explaining how it can be utilised in risk assessment and detailing its importance for lenders. It also provides examples of both positive and negative behaviours, enabling lenders to make more informed and fairer decisions.
Why Credolab’s Approach Works
By combining device and behavioural insights, lenders gain a more comprehensive understanding of risk profiles and categorise them as high or low risk with unprecedented accuracy. With Credolab’s model, lenders can:
- Reduce Bias: Focus on real-time actions over historical data.
- Improve Accuracy: Flag high-risk behaviours while rewarding responsible habits.
- Enhance Inclusivity: Turn thin-file borrowers into scorable candidates by utilising non-traditional signals.
Using Credolab’s insights and scores, lenders can ensure smarter, fairer, more predictive and more accurate credit scoring in their risk assessments for even the most underserved borrowers.
Why Are Lenders Adopting Alternative Credit Scores?
Lenders are adopting alternative credit scores because traditional data alone no longer gives a complete view of creditworthiness. As more financial activity moves online, lenders need scoring models that can assess borrowers quickly, accurately and inclusively.
Alternative credit scoring fintech companies were among the early adopters of this approach. However, the shift is now accelerating across traditional banks, digital lenders and other financial institutions that want to improve risk visibility and expand responsible access to credit.
- Growth of thin-file and no-file borrowers
Many individuals and small businesses still lack enough formal credit history to be assessed through traditional credit scoring. Alternative credit scoring helps lenders evaluate these applicants by using non-traditional data, such as cashflow behaviour, transactional data, utility payments, device metadata and behavioural data.
- Rising competition in digital lending
Digital lenders compete on speed, convenience and customer experience, which makes the question of why are lenders adopting alternative credit scores especially relevant in digital lending. Alternative risk scoring helps lenders streamline decision-making, shorten approval times and offer a smoother application journey without relying only on traditional data.
- Improved model performance
Alternative data can strengthen predictive power by adding behavioural, transactional and device metadata signals to alternative credit scoring models. When analysed through machine learning algorithms, these wider data points can help lenders identify repayment capacity, detect changing risk patterns and improve portfolio decisions.
- Regulatory and financial inclusion pressure
Regulators and policymakers are placing greater emphasis on responsible innovation, fair access and financial inclusion. Alternative credit scoring supports this shift by helping lenders assess underserved borrowers more effectively while encouraging stronger governance, explainability and responsible data use.
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