Risk assessment with Credolab: The predictive power of alternative data

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Using alternative data in credit scoring can identify more nuanced behavioural patterns that predict an applicant’s willingness to repay a loan. A risk score built on alternative data, such as Credolab's behavioural risk score, can help improve the assessment of the first two questions in the Five Cs of Credit framework:

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1. Character

• Key question:
  ‍Can Credolab’s behavioural insights and scores provide a proxy for financial responsibility, financial discipline and creditworthiness?
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• Problem:
  Without traditional data, lenders find it difficult to measure traits linked to a borrower’s willingness to repay loans, such as responsibility, trustworthiness, and honest financial habits.

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2. Capacity‍

• Key question:
  ‍Can Credolab’s behavioural insights and scores help lenders assess the ability to manage new credit beyond traditional income verification?
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• Problem:
  ‍Without credit bureau or income data, lenders find it difficult to measure borrowers' ability to repay loans, such as financial habits, risk-taking behaviour, and debt management.

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Behavioural Indicators: A deeper qualitative look

The table below illustrates how behavioural indicators (BIs) from alternative data can more effectively assess Character and Capacity. It also examines how BIs affect traditional methods compared to modern tools, providing a deeper and more holistic understanding of borrower risk.

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Statistical Indicators: The quantitative proof

In addition to BIs, Credolab uses Statistical indicators (SIs), 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. These features provide quantitative proof that these behavioural patterns statistically predict defaults.

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Furthermore, Credolab's data modelling pipeline filters 11 million features to identify the top 30 to 50 with the highest predictive power for defaults. Net Logistic Regression finalises the analysis by ranking features by Information Value (IV), correlation with each other, and stability over time.

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This approach ensures that only the best features, consistently predictive of repayment behaviour across populations, are included in the final alternative score. Meanwhile, metrics like the Gini Coefficient, Kolmogorov-Smirnov (KS) statistic, and AUC/ROC confirm the model’s ability to distinguish “Good” vs “Bad” borrowers.

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The combined power of behavioural and statistical indicators

Individually, BIs and SIs are powerful tools that can impact the assessment of a borrower’s Character and Capacity and the resulting credit decision. However, their combined power takes things a step further. BIs explain the intuitive “why” behind credit risk (e.g. capturing traits like reliability, integrity, and financial habits), and SIs prove the “how”, demonstrating that these behaviours have measurable, predictive value for defaults.

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Together, they:

• Compensate for the lack of traditional credit data by offering qualitative behavioural insights and quantitative statistical validation.
• Enhance model robustness, ensuring that what seems logical from a behavioural standpoint also holds up from a statistical one.
• Improve risk segmentation, as models built on this hybrid approach achieve higher Gini/KS scores, meaning better separation between "good" and "bad" borrowers.

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Even in the absence of credit history, Credolab’s behavioural data (device and behavioural metadata) can assess an applicant's financial responsibility and capacity to take on new debt, tackling Character and Capacity in the Five Cs framework, respectively. In doing so, Credolab helps lenders fairly assess every applicant, even those that traditional underwriting models would have rejected.

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The ideal approach is a hybrid risk model that combines the best of both worlds and leverages Credolab’s alternative scores as input into a lender's general risk model.

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Credolab’s edge: Reducing risk with advanced credit scoring models

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How Credolab works

Credolab’s unique methodology leverages proprietary SDKs embedded in the lender’s mobile app and online application form to collect privacy-consented, depersonalised and anonymised device and behavioural metadata. Transformed into features first and scores second, Credolab’s risk solutions supercharge traditional risk assessment with a 100% hit rate. By effectively scoring all applicants, including thin-files and new-to-credit individuals, Credolab can:

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• Increase Predictive Power:
  Credolab's unique alternative data layer has a low correlation with socio-demographic, credit bureau, and transactional data, providing a fresh perspective on creditworthiness.

• Increase Approval Rate:
  Expand access to credit for applicants that traditional credit scoring models often struggle to assess due to limited information, leading to missed opportunities and a high rejection rate.

• Decrease Delinquency:
  Analyse subtle behavioural patterns and device interactions to reduce the chances of mistakenly approving high-risk applicants (false positives) or rejecting low-risk applicants (false negatives).

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Leveraging the best possible credit scoring model for success

The ideal credit scoring model combines automation, diverse data sources, and adaptability to deliver accurate and inclusive risk assessment. It should also leverage machine learning to continuously learn, identify new patterns, and refine decision-making. However, the accuracy and reliability of an ML-driven model depend on the quality of the data used to train it.

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This is where alternative data, such as Credolab’s device and behavioural data, plays a critical role. By complementing traditional data sources, alternative data enables deeper, more nuanced insights into borrower behaviour.

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As Andre Ripla, PgCert, explains:

“By leveraging ML, natural language processing, and other AI technologies, organisations can process vast amounts of data, identify complex patterns, and make predictive analyses that were previously impossible or impractical using traditional methods.”

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Credolab’s ML-driven credit scoring models are designed to help lenders succeed in today’s data-driven world. This proven approach ensures lenders can access scorecards tailored to each specific loan product and origination channel: Android app, iOS app, mobile web, and web.

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Benefits of Credolab’s ML-Driven Solutions

ML applied to alternative data offers a novel approach to credit risk assessment that translates into tangible benefits for lenders.

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With Credolab, lenders can identify hidden behavioural patterns and improve their accuracy in assessing risk for every borrower, not just thin-files. Here are case studies to prove it:

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1. Neobank in The Philippines

• Gini 0.29 standalone, Gini 0.35 with Credolab, Gini 0.42 joint model
• Increased predictive power by 45%
• Decreased default rates by 50%

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2. BNPL in the United Kingdom

• Gini 0.25 standalone. Lift of Gini 0.15 in integrated model > 0.40
• Decreased default rates by 35%

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3. Short-term loans in Mexico

• Developed a model with Gini score of 0.40
• Improve performance of a short-term loan with low AR (<10%) and high NPL (>30%)
• Embedded Android and iOS SDK into their consumer app
• Decrease delinquency by 34

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4. Short-term loans in Brazil

• Gini score 0.42
• First Payment Default (FPD) is when a borrower fails to make their first scheduled payment on a loan or credit obligation.
• FPD dropped by 63% in only 7 months
• Increased conversion from approved customers to repeated customers by 40%

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5. Consumer loans in Colombia

• Gini score 21.2
• Increased predictive power by 20%
• Improved false positive detection by rejecting 30%, including risk applicants
• Decreased bad rates by 6.67%

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Applications Beyond Lending

While Credolab’s solutions are often associated with risk assessment, their applications extend far beyond. Here are a few ways organisations can leverage Credolab’s products and solutions:

• Fraud Prevention:
  ‍Identify high-risk individuals during customer onboarding.

• Portfolio Management:
  Assess risk across customers’ or clients’ portfolios

• Customer Segmentation:
  Analyse behavioural insights to tailor financial products and services.

A case study example would be how a telecom company could use Credolab’s risk scores to identify customers likely to default on their bills, enabling proactive interventions to reduce losses. Similarly, an e-commerce platform could use these scores to offer tailored payment options, improving customer satisfaction and retention.

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A new era of risk management and credit scoring

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The future of risk management lies in data-driven decision-making. As traditional credit scoring methods show their limitations, organisations must embrace innovative solutions on top of existing models to stay competitive in a rapidly changing financial landscape.

Credolab’s role in shaping the future

Staying ahead in risk management demands smarter tools and better data sources in an era of rapid financial transformation. Using alternative data powered by Credolab’s proven technology offers a path to more inclusive, accurate, and efficient credit scoring.

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Credolab, as a leading charge in this transformation, provides tools to minimise risks, reduce losses and costs and unlock opportunities in every market, paving the way for a more equitable financial future. By leveraging alternative data and ML, Credolab is redefining credit scoring and shaping the future of risk management.

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Ready to modernise your risk management processes? Explore Credolab’s risk solutions today and see how alternative data and ML can help you excel in a rapidly changing financial landscape.