How Enhanced Scoring Models Drive Financial Growth

Why is improving scoring models the right decision?

Enriching scoring models with alternative data is key to transforming modern lending institutions.

With the emergence of alternative credit scoring models, lenders can increase financial inclusivity, improve risk management, and more effectively prevent fraud.

How does this contribute to the financial growth of lending institutions?

Read on to discover how adopting an enhanced scoring model can help a lender gain significant economic benefits.

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What is an enhanced scoring model?

An enhanced scoring model goes beyond traditional creditworthiness assessment methods, such as FICO scores.

These models utilize a broader range of data, providing a comprehensive picture of an individual's financial capabilities and the risks involved.

Key differences between enhanced scoring models and traditional scoring systems:

Use of alternative data sources, such as digital footprints and behavioral data.

• Integration of AI and machine learning for more dynamic and personalized assessments.

In contrast, traditional scoring systems rely on historical data that may no longer be relevant by the time a borrower applies for a loan.

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Economic impact of an improved credit scoring model

“On the Rise of FinTechs – Credit Scoring using Digital Footprints” research shows that adopting an enhanced credit risk scoring model increases credit organizations' profitability.

This fact is illustrated by the graph below:

The graph shows the dynamics of default levels (on the left Y-axis) and the number of weekly observations (on the right Y-axis) over time.

Key takeaways from the graph:

• Enhanced risk prediction. After incorporating the digital footprint into the scoring model (marked by a vertical red line), the default rate significantly decreases. This indicates more accurate risk prediction and a reduction in unreliable borrowers.

• Stability confirmed. The graph also shows the 95% confidence interval for the default rate (the gray-shaded area), which demonstrates the stability of the results after the initial decline.

As a result, this graph demonstrates that credit score modeling improvement has reduced default risks, leading to economic benefits for model users.

This trend is also observed in RiskSeal. After adding digital footprints to customer scoring models, defaults drop by 17-25% within the first three months.

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How digital footprints benefit unscorable customers

The same study conducted a credit scoring model assessment, which revealed that the use of enhanced models has the most positive impact on low-score and unscorable customers.

This is visible on the slide below:

The table provides the following insights:

• Reduction in default rates after adding digital footprints. Pay attention to columns 2, 3, and 4. The coefficients for the variables "Post x DF Added" and "Post x ScoreAndDFAdded" are negative and statistically significant.

This indicates that adding digital footprints reduces default rates, particularly for low-score and unscorable customers.

• Maximum impact on low-score and unscorable customers. After incorporating digital footprints, the "Low score" and "Unscorable" groups experience the most significant reduction in default rates.

This highlights the unique value of digital footprints in assessing the risk of customers who are challenging to evaluate using traditional credit parameters.

• Result stability. Various model specifications (controlling for time trends, fixed effects, and other factors) consistently support the conclusion that default rates decrease.

All these data prove that enhanced scoring model predictive power is especially high for customers with low credit scores and those without a score.

At RiskSeal, we observe similar results. After integrating our data, clients double their approval rates.

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Digital footprint forecasts future credit score changes

Another valuable observation by researchers is that the digital footprint can serve as an indicator of future changes in credit scores.

Take a look at this table:

The data presented in the table allow for the following conclusions:

• A positive digital footprint improves credit scores. This means that individuals with strong digital footprints are likely to see their credit scores increase in the future. A significant positive correlation supports this, even when accounting for regression to the mean.

• Maximum impact is observed for low-score individuals. The study emphasizes that positive changes in the digital footprint have the greatest impact on individuals with low current credit scores. This is confirmed by the results in the right-hand section of the table.

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Enhance your scoring models with RiskSeal

At RiskSeal, we stay ahead of key alternative credit scoring trends, offering cutting-edge solutions for evaluating potential borrowers.

What can you achieve with our scoring system?

• Increased credit approval rates. Our clients report that enhanced scoring models perform better, enabling them to double their credit approval rates. 
• Reduced risk of default. By incorporating the digital footprint into credit scoring, we help reduce default rates by up to 25%.
• Effective fraud prevention. We leverage advanced technologies to detect fraud, with a special focus on combating identity theft through tools like face recognition, name matching, and location analysis.
• Improved customer experience. All our checks are performed in real-time, ensuring the entire process — from loan application to decision — takes no more than five seconds.
• Operational efficiency and cost savings. We filter out up to 70% of fraudulent applications or clear defaulters at the initial application stage, significantly reducing KYC costs.