Challenges of Alternative Credit Scoring

As traditional data reaches its limits, particularly in emerging markets or thin file populations, CROs and risk teams face a pressing dilemma.

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How to harness alternative data in credit scoring without compromising risk standards?

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While alternative data can capture financial behaviours and offer more predictive insights compared to traditional data, finding a reliable source is fraught with challenges. Without rigorous validation, what appears promising in theory often becomes arduous in practice.

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We examine six challenges CROs and risk teams must overcome to unlock alternative data’s true value, framed by Oliver Wyman’s data quality framework:

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1. Measuring predictive power - Cut through the noise

Alternative data offers tremendous potential for risk assessment; however, its sheer volume presents a fundamental challenge: distinguishing truly predictive signals from meaningless noise.

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The diversity of alternative data sources and the lack of standardised use patterns complicate this task. For example, translating raw behavioural signals into actionable insights requires expertise in artificial intelligence (AI) and machine learning (ML), creating a capability gap for lenders.

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Alternative credit scoring typically operates in a sea of data, yet it often lacks depth of insight.

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Millions of raw data points, including complex user behaviours and digital footprints, may generate low-level features with no proven truth or link to credit risk. Without expert interpretation of these features, extracting reliable patterns and meaningful signals might be a hurdle.

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Three critical pain points for risk teams:

• Overfitted models chasing baseless behavioural patterns in sparse data
• Black box paralysis due to limited transparency from unknown features driving predictions
• Benchmark blindness due to no benchmark

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These challenges do not diminish the value of alternative data; in fact, they highlight the need for robust validation frameworks and expert guidance to unlock its full potential.

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2. Assessing Coverage - Data representativeness gap

While alternative data promises wider financial inclusion, its uneven coverage creates a critical blind spot: the populations most in need of credit are often the least visible.

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Risk models rely on consistent, longitudinal data to assess creditworthiness and identify patterns of fraud. However, most alternative data sources experience inherent limitations that could undermine data reliability.

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Sources such as mobile usage, telco records, and open banking/finance often lack sufficient historical depth and time. A short observation window limits predictive power, market fragmentation creates uneven data quality, and a lack of global standards makes cross-border normalisation challenging.

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Three tangible operational risks for CROs:

• Unstable model training from short historical timelines
• Geographic performance bias, skewing results based on local penetration rates
• High-risk market expansion with no reliable baseline for risk calibration in new regions

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These coverage gaps do not invalidate alternative data—they underscore the need for contextually collected signals captured at the point of application.

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3. Maintaining Specificity - Standardisation deficit

Alternative data offers unparalleled depth for borrower profiling, but its inconsistent formats and interpretations create navigable complexities for risk teams.

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Risk models require consistent variables to build accurate borrower profiles. However, alternative data sources deliver wildly different formats with varying levels of granularity and degrees of interpretability.

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Device and behavioural biometric metadata, call records, and transaction logs each require unique normalisation strategies, ingestion protocols, and data governance reviews. This wide variability might complicate integration and hinder accurate quality evaluation across data providers.

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Three critical operational burdens:

• Slow and costly model validation from manual data reconciliation
• Audit and internal review failures due to a lack of clear variable definitions
• Inconsistent feature stability during testing

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While these specificity challenges demand attention, they are resolvable through standardised frameworks that leverage pre-engineered feature libraries.

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4. Ensuring accuracy and timeliness - Verification gap

Alternative data enables uniquely responsive risk assessment, but its reliability depends on three non-negotiable qualities: verifiable accuracy, a bias-free algorithm and real-time data fresh updates.

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Many alternative data sources are not tied to real-world financial transactions and real-time financial behaviours. Fraud prevention and risk modelling demand real-time, validated data streams to assess borrower reliability. Self-reported income sources lack audit trails, while cash-flow underwriting techniques become useless within days if the customer loses her income. This creates a fundamental mismatch between the dynamic nature of borrower risk and static data snapshots.

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Three critical vulnerabilities for risk and fraud teams:

• False positive surges from unverified or outdated data
• Unback-testable models due to volatile, inconsistent, or missing data
• Gaps in audit trails obscure alternative feature-based decision rationale

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These accuracy hurdles do not invalidate alternative data, they mandate using solutions that allow for live data collection, which ties digital behaviours directly to applications without uncertainty.

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5. Verifying orthogonality - Duplication dilemma

Alternative data achieves its full potential only when each source delivers truly statistically independent insights, a challenging standard to meet.

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Orthogonality requires data sources with no or low correlation with each other, yet many alternative datasets redundantly echo traditional credit information. When two sources overlap, such as open banking data or utility bill payment data, they can create diminishing returns and unnecessary model complexity.

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These overlaps create three operational risks:

• Redundant scoring logic with limited added value
• Signal conflicts in composite or ensemble models
• Duplicated cost of sourcing similar data

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These orthogonality gaps do not diminish the success of alternative data. Instead, they emphasise the importance of carefully evaluating providers and conducting tests to validate the signal’s unique contribution.

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6. Implementing regulatory compliance - Governance gap

Alternative data can unlock transformative fraud and risk insights, but an evolving regulatory landscape demands vigilance in compliance that many providers find challenging to navigate.

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Global regulations, such as the EU’s Digital Operational Resilience Act (DORA) and the proposed FIDA/PSD3, impose stringent requirements on the usage of alternative data, ranging from mandatory third-party oversight to expanded data-sharing rules.

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Most vendors lack documentation frameworks to prove compliance across three critical areas:

• Legal exposure from undocumented feature derivation
• Missing key compliance clauses in vendor contracts
• Cross-jurisdictional data storage or transfer issues

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These hurdles do not negate the value of alternative data. They demonstrate the need for frameworks to assess regulatory adherence and compliance, on top of obtaining the user’s consent.

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These challenges explain why many alternative data initiatives fail to scale. Yet for lenders who address them systematically, the rewards are substantial.

Striking the right balance with alternative credit scoring

Navigating these challenges is the first step toward successfully integrating alternative credit scoring.

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By understanding these factors, lenders can make more informed credit decisions and effectively achieve the best outcome. This includes reduced data asymmetry and enhanced predictive power in credit scoring models.

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Relying solely on traditional credit scoring is no longer the solution. To increase risk visibility into financial behaviour, alternative credit scoring leverages non-traditional, aka alternative, data sources. This includes:

• Gig Economy Insights: Ride-hailing app logs (e.g., Uber and InDrive) can help verify income stability
• Mobile Money Data: Platforms like Pngme can provide real-time cash flow insights
• Behavioural Analytics: Behavioural biometrics data can help analyse data familiarity and data manipulation (e.g., typing patterns, gestures) to help catch applicants who are misrepresenting or embellishing their loan application, often resulting in first payment defaults

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The key takeaway? Alternative data provides a comprehensive view of creditworthiness. It transforms invisible borrowers into bankable customers, increasing predictive power and fostering financial inclusion.

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Implementing alternative credit scoring with confidence

Alternative credit scoring offers promise with navigable challenges, but it requires careful consideration. The reality for CROs, underwriters, and fraud teams is that every new data source introduces risk and operationalising it safely, effectively, and scalably is crucial.

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Ultimately, overcoming these challenges is a necessity for building equitable, future-ready financial ecosystems. By directly addressing them, lenders bridge data gaps, reduce data asymmetry, and redefine risk assessment to create opportunities with pivotal allies where traditional systems fall short.

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Ready to navigate these challenges with confidence? Explore Credolab’s risk solutions here.