How Has Alternative Credit Scoring Redefined Creditworthiness

Globally, 1.4 billion adults remain excluded from formal credit systems (World Bank). What if their financial potential could finally be unlocked?

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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.

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Traditional vs. Alternative Credit Scoring: Bridging the Data Gap

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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.

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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.

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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Outdated or incomplete information has only widened the data gap between lenders’ findings and a borrower’s actual financial state.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.

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In essence, traditional credit scoring systems limit opportunities for individuals, create blind spots in risk visibility and limit access for deserving borrowers.

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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.

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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).

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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 tailor risk assessments more accurately.

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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.

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The result? A more inclusive, accurate and predictive credit scoring system with higher approval rates.

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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.

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But how exactly does this translate into tangible benefits?