Alternative credit scoring evaluates a borrower’s creditworthiness using data beyond traditional bureau reports, such as cash-flow records, utility and rent payment history and the digital signals an applicant generates through their email, phone and device. Lenders use it to score applicants that conventional models cannot read.
The Consumer Financial Protection Bureau (CFPB) estimates that 7 million US adults have no credit record at all, and a further 25 million hold files too thin or stale to score. Globally, 1.3 billion adults sit outside the formal financial system, yet around 900 million of them own a mobile phone. The data to assess these borrowers already exists. It just lives outside the bureau file.
For consumer lenders and BNPL providers, this is a growth opportunity with a catch: the same thin-file profile that conceals a creditworthy borrower can also hide a synthetic identity.
What is Alternative Credit Scoring?
Alternative credit scoring is used to examine additional data sources and other relevant factors to assess borrowers with limited credit histories or who are categorized as “unscorable” by today’s conventional models. While effective, traditional models exclude millions of consumers with insufficient credit data – including young adults, immigrants, women in rural areas and those who prefer cash transactions or live in cash-based economies.
For lenders in unbanked and underbanked regions, institutions that want to engage with younger generations with mistrust for big banks or traditional credit lines or individuals who simply don’t have access to the traditional financial system, there are certain obstacles to assessing creditworthiness outside conventional financial services. This is where alternative credit scoring, a modern method representing an evolution in risk scoring for lending institutions and firms, can be a game changer.
Evaluating Additional Alternative Credit Signals
To create a comprehensive assessment of a borrower’s creditworthiness, various signals serve to determine whether an applicant has the means and the intention to repay a loan. For example:
- Utility and Rent Payments – Timely payments for utilities, rent and phone bills can demonstrate financial responsibility.
- Bank Account Information – Analysis of bank account activities, including deposits, withdrawals and savings patterns, provides insight into a borrower’s financial stability.
- Employment and Income Data – Steady employment and consistent income are strong indicators of a borrower’s ability to repay debts. Employment data can also be combined with sector data, allowing the lender to estimate a debtor’s income level to make a suitable loan offer.
- Education Background – Higher education levels can correlate with better financial habits and higher earning potential.
- Social Media Activity – In some cases, social media behavior and connections can offer additional context about a borrower’s lifestyle and reliability. For example, a LinkedIn account and its details can suggest that an applicant is a natural person in a regular employment situation.
- Transaction Data – Detailed transaction histories from digital wallets and payment apps can reveal spending habits and financial discipline.
- Online Data Points – Additional data points can reflect a higher level of ‘lending trust.’ For example, suppose a customer regularly pays their bills to several online accounts, such as Netflix and Amazon Prime. In this case, their behaviors suggest that these customers have the finances to repay small loans.
- Digital Footprint Analysis – Information from an individual’s digital presence and activities, including a broad spectrum of social signals.
- Device Intelligence – Process of looking at unique device identifiers based on attributes and configurations.
The Digital Footprint as an Alternative Signal
Digital footprint analysis, also called digital footprint monitoring or assessment, involves collecting and analyzing data from an individual’s online presence and real-time digital, social and behavioral indicators.It simplifies customer verification on two fronts at once: financial institutions can examine alternative indicators of economic stability while distinguishing legitimate, eligible applicants from those attempting deception.
Digital footprints prove most valuable in markets where traditional bureau data is unavailable, other sources are stale or inaccurate or the target group is simply underbanked. In each of these situations, a data-driven view of an applicant’s digital behavior gives lenders a reliable way to assess risk where traditional inputs say nothing.
How to Collect Alternative Data for Credit Scoring
Beyond profiling a customer with digital footprint analysis and looking at the above alternative credit signals, additional data can be collected to verify a potential lending customer’s integrity and validity and evaluate further creditworthiness through device intelligence.
Device intelligence collects and examines data points from the devices an applicant uses, such as smartphones, tablets and computers. By analyzing hardware and software configurations alongside how the user interacts with the device, lenders gain a clearer view of an applicant’s identity and financial behavior.
Device fingerprinting, which creates unique identifiers from device attributes, is one component of this. Device intelligence builds on it with behavioral patterns and contextual data, surfacing anomalies that point to fraud: unusual login attempts from different locations within a short time frame, or multiple accounts transacting from a single device. Continuous monitoring of these signals helps lenders separate legitimate users from fraudsters and sharpens the accuracy of credit assessments.
Practical Applications of Device Intelligence in Credit Scoring
Device intelligence can be particularly effective in regions with high fraud rates or where traditional data sources are unreliable. Lenders can build a robust profile of a borrower’s credibility by leveraging real-time data points, like geolocation, usage patterns and network information. For instance, a borrower consistently accessing their account from the same device and location is more likely to be trustworthy than one showing erratic device behavior.
Integrating device intelligence with other alternative signals, like bank transaction data and digital footprint analysis, gives a multi-faceted view of a borrower’s financial stability and intentions. This helps lenders prevent fraud, reduce default risk and extend fair access to creditworthy borrowers who fall outside traditional scoring models.
Looking at Digital Lending and Risk: Loans Default Vs. Fraud
Digital lending made it faster for borrowers to access funds, and just as fast for bad actors to exploit the same rails. The ease of access that legitimate borrowers value also opens the door to applicants likely to default and to those taking out loans under fraudulent premises. These two groups create very different problems, and telling them apart is one of the most consequential distinctions in lending risk.
A genuine default is a credit decision that didn’t work out: the borrower intended to repay and couldn’t. Fraud is a vetting failure: the applicant never intended to repay at all. The cost of confusing the two is significant, because treating fraud losses as credit risk means pricing them into rates instead of preventing them. [neutral fraud-loss stat + primary source to slot here]
Current loan fraud tactics include falsified documents and stolen identities (third-party fraud), straw buyer schemes where fraudsters collude with a willing second person to secure approval, and first-party cases where applicants misrepresent their financial situation for better terms.
Even a small number of malicious applicants can distort a portfolio and undermine the models scoring it. Alternative signals close the gap: screening applicants against their digital behavior and device data separates the borrower who can’t repay from the applicant who never planned to, before either reaches the portfolio.
See how to leverage digital footprinting to gain a comprehensive view of financial reliability to enhance decision-making.
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The Benefits of Alternative Credit Scoring
The primary benefit of alternative credit scoring is inclusivity. By considering a broader range of data sources, lenders can extend credit to applicants excluded by traditional models. Non-traditional data like utility and rent payments and employment history lets these borrowers demonstrate financial responsibility and access credit that was previously out of reach.
Accuracy follows from the same breadth. Traditional credit scores rely on a narrow set of financial behaviors, primarily credit card usage and loan repayments. Alternative credit scoring reads a wider set of inputs, capturing habits traditional models overlook: consistent rent and utility payments, steady income and well-managed bank accounts all indicate reliability, even without an extensive credit history.
Personalization is a further advantage. Lending models can be tailored to specific demographics or sectors, so lenders can offer products that fit the circumstances of different borrower groups rather than applying one template to all of them.
AI and machine learning extend all three benefits. Models that learn continuously can analyze volumes of data no analyst could review, identifying patterns as they emerge and improving predictive accuracy over time. The same capability that sharpens credit assessment also strengthens fraud detection, since both depend on reading connected signals across an applicant’s profile.
Lending is All About Trust
Building a solid and efficient online credit scoring model in the digital era is challenging. The good news is that SEON’s digital footprint analysis and device intelligence can lower default ratios without closing the door to valuable customers.
At its core, lending is founded on trust, the confidence that a borrower will commit to repaying the funds they’ve been provided. However, determining trustworthiness is complex, especially when assessing individuals with limited financial histories or those operating outside of traditional banking systems. The key lies in finding the right signals that allow lenders to accurately evaluate a customer’s credibility and intent.
By thoughtfully incorporating alternative credit scoring models, lenders can make more informed decisions, extending credit opportunities to underbanked or unbanked populations that may appear excessively risky based on traditional metrics. By harnessing the wealth of social and digital signals, lenders can promote financial inclusion while effectively managing risk—a win-win scenario for businesses and consumers.
Sources
- Consumer Financial Protection Bureau: “CFPB Explores Impact of Alternative Data on Credit Access for Consumers Who Are Credit Invisible”
- FDIC – How America Banks: Household Use of Banking and Financial Services
- Investopedia: Default RiskL Definition, Types, and Ways to Measure
- American Banker: Business lending fraud still higher than before COVID, survey says
