The Mechanics of Alternative Data: Precision in Modern Credit Scoring

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First, we need to revisit one important question: What is the core concept of alternative data?

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Alternative data refers to information collected from non-traditional sources. Among others, it consists of transactional data, device and behavioural metadata. Specifics include app usage, keystroke dynamics, battery charging habits, utility bill payments, contact saving patterns, and calendar and reminder creation patterns. Alternative data captures real-time financial and non-financial behaviours, offering lenders a dynamic lens into creditworthiness.

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Three key drivers fuel the adoption of alternative data:

• Greater Affordability: Falling costs of data collection and processing.
• Improved Technology: AI/ML tools to filter noise and identify patterns.
• Higher Demand: Lenders’ need for inclusive, predictive risk models.

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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:

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Example: Credolab’s Data-Driven Insights

To illustrate, let’s explore how Credolab uses device and behavioural metadata to refine credit risk segmentation.

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These tables (device and bhevaiourla 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.

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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.

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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.

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Ready to execute better risk assessments? Schedule a demo to try it for free.