Risk
Mar 11, 2025
Modernising Risk Part 1: How To Improve Credit Scoring with Alternative Data
How does traditional credit scoring fall short in today’s dynamic financial landscape? Find out more in how alternative data helps modernise risk assessment for greater predictive power and lower cost of risk.
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In today’s rapidly evolving financial landscape, traditional scoring methods struggle to keep pace with the increasing complexities of modern risk assessment. Relying solely on traditional data sources is no longer sufficient for accurately assessing risk or capturing the financial realities of diverse borrower segments. These methods, which rely heavily on credit history data, systematically exclude millions of potential borrowers, particularly millennials, Gen Z, gig economy workers, small business owners, and those in emerging markets.
Traditional systems fail to accurately assess credit risk for financially responsible individuals with limited or no credit history, often misclassifying them as unscorable. The gap between the information available to lenders and a borrower’s true risk profile is often referred to as data asymmetry. Without access to relevant and predictive data sources, lenders face blind spots in risk assessments. This creates difficulty differentiating between genuinely low-risk borrowers and those with higher credit risk.
The good news is that advancements in modern risk assessments and the availability of alternative data reduce data asymmetry and improve predictive accuracy. By leveraging alternative data alongside traditional models, lenders improve risk visibility, increase predictive power and reduce reliance on incomplete datasets while expanding coverage. The latest Global Findex survey proves this shift, showing that the number of unbanked adults dropped from 2.5 billion in 2011 to 1.4 billion in 2021. This shift enables lenders to refine decision-making frameworks, minimising misclassifications and improving overall risk portfolio quality. As credit risk models evolve, lenders can refine decision-making frameworks, minimising misclassifications and strengthening overall risk portfolio quality.
However, challenges persist. Almost 30 million Americans are ‘credit invisible,’ which means they do not have a credit record with any of the three major credit bureaus: Experian, TransUnion or Equifax. Approximately 5.6 million adults were in the same situation in Britain. Women also still remain disproportionately unbanked, with only 63% having an account compared to men at 74%. Economic and infrastructural barriers also continue to limit credit access in regions such as Sub-Saharan Africa and South Asia. These gaps present opportunities to leverage structured and reliable data from multiple sources to build more predictive and inclusive credit risk assessment models for data-driven risk assessments.
The shift towards alternative data in risk assessment
As traditional methods fall short, the demand for innovative tools that provide deeper insights into borrower behaviour has never been greater. Lenders have already adopted a more dynamic approach, one that goes beyond traditional credit histories to embrace the changing dynamics of borrowing behaviour.
Over the past decade, two key innovations have reshaped credit risk assessments: alternative data and machine learning (ML). In 2015, fintech startups began experimenting with the possibility of using smartphone metadata and digital behavioural insights as predictive indicators of creditworthiness in risk assessments. This proven approach has since enabled lenders to expand financial access and improve risk modelling without relying solely on traditional credit history data. As a result, alternative data has emerged as a powerful force in credit scoring, offering lenders a more nuanced, predictive, and accurate understanding of borrowers’ risk.
Credolab’s role in transforming risk assessment
Credolab is at the forefront of this transformation. As a global leader in device and behavioural ML-driven credit scoring, Credolab empowers lenders with smarter, faster, and more predictive risk assessments. By supplementing traditional data with behavioural smartphone metadata, lenders can identify risk patterns, improve risk accuracy, increase predictive power and approval rates, and optimise lending decisions in real-time.
This blog explores how alternative data and Credolab’s proven solutions modernise risk assessment and credit scoring, ushering in a new era of data-driven precision, efficiency and accessibility in lending.
Understanding risk and credit scoring in the modern era
What is risk management?
Risk management lies at the heart of responsible lending. Credit risk management focuses on mitigating financial loss by identifying, assessing, and minimising risk factors throughout the credit lifecycle. A key component of this process is credit risk assessment, which evaluates a borrower’s repayment likelihood using all available data sources during decision-making.
For decades, credit scoring models relied on five key data points to assess borrower risk:
- Payment history - Record of past payments, including frequency, timeliness, and delinquencies.
- Credit utilisation - The amount of borrower debt and credit availability on loans and credit cards.
- Length of credit history - The duration of a borrower’s payment history (longer histories typically indicate lower risk).
- A combination of existing credits and account types - The number of credit accounts a borrower holds, including instalment loans, home loans, and credit and retail cards.
- Recent credit activity - The recency and frequency of new credit applicants and account openings (multiple new accounts may signal higher financial risk).
While effective for borrowers with established credit histories, this traditional method often excludes new-to-credit or thin-file individuals, presenting a risk visibility gap when evaluating those lacking traditional financial data. It fails to assess non-traditional financial behaviours, leaving many borrowers misclassified as high risk due to insufficient credit history data.
As lending environments become more complex, traditional scoring alone is insufficient. More adaptive risk assessment models are needed to reduce data asymmetry, improve risk accuracy, and account for borrower behaviours beyond conventional credit history.
Introducing the Five Cs of Credit: A Traditional Framework
One established approach to evaluating borrower creditworthiness is using the Five Cs of Credit framework. This foundational approach considers multiple financial dimensions beyond standard scoring models. However, it also faces limitations in modern risk assessment, particularly when assessing borrowers without extensive credit histories.
The Five Cs of Credit is a framework used in financial services to assess the creditworthiness of potential borrowers. This approach helps lenders determine the risk associated with lending money. Here is an overview of the 5 Cs:
While the Five Cs framework provides a structured approach to evaluating creditworthiness, it relies heavily on credit bureau data, especially for assessing Character and Capacity. This reliance can lead to negative lending decisions for borrowers who lack traditional credit histories, such as:
- Thin-file borrowers without extensive credit histories (e.g. gig-economy workers)
- No-file borrowers with no credit history (e.g. students who just graduated or small business owners with businesses less than three years old)
The limitations of traditional credit scoring
Traditional credit scoring models have long been the backbone of risk assessment, offering a proven way to assess creditworthiness based on established credit histories. However, these models primarily rely on traditional data and fail to account for millions of potential borrowers who fall outside these criteria.
The challenge is clear: Traditional scoring methods exclude thin-file or no-file borrowers and perpetuate data asymmetry. These methods fail to capture real-time financial behaviour and often misclassify borrowers due to limited historical data. This issue affects young people who are new to credit, have unstable incomes, or simply live in countries without established credit bureau systems.
By focusing on past financial activity, traditional scoring methods limit opportunities for individuals and create blind spots in risk visibility, leading to less accurate risk assessments. Many borrowers who demonstrate financial responsibility through alternative means are excluded from credit opportunities or misclassified as high-risk. This results in two major risks for lenders: false positives, where creditworthy applicants are wrongly rejected, and false negatives, where risky borrowers are approved based on incomplete or outdated credit information.
These limitations restrict access to credit for millions of potential borrowers and expose lenders to unnecessary risk by failing to account for alternative indicators of creditworthiness. To keep pace with evolving borrowing behaviours, modern risk assessment requires a more adaptive approach, one that integrates alternative data sources to reduce data asymmetry, enhance predictive power and improve decision-making.
The role of alternative data in modern credit scoring
Beyond credit scores: How alternative data unlocks hidden insights
Alternative data refers to non-traditional sources of information that can be used to reveal a borrower’s financial habits, stability, and reliability. Unlike traditional credit scores, which rely on historical credit data, alternative data captures real-time behavioural insights that help lenders refine risk assessment.
Some examples of the most widely used source of alternative data include the following:
- Utility bill payments
- Telco top-up payment histories
- Social media activity
- Psychometric data
- Device metadata
- Behavioural metadata
To different extents, and each with its own specific use case, pros and cons, these data sources can provide a holistic view of an individual’s financial behaviour.
Each data source offers unique insights (with its own specific use cases, benefits and limitations), helping lenders build a holistic view of a borrower’s financial behaviour. For example, Credolab’s device and behavioural metadata provide valuable indicators of creditworthiness and insights into a user’s financial habits. By examining factors like the number of finance apps installed, device battery health, or even how often a user copies and pastes information into forms, Credolab’s ML algorithms can identify patterns that correlate with risk.
Alternative data: A paradigm shift in risk management and why it’s important
Once integrated, organisations that combine traditional and alternative data sources have experienced solid improvements in risk assessment for all borrowers, not just the credit-invisible.
By leveraging alternative data alongside traditional methods, organisations can serve previously excluded populations and reduce data asymmetry. This leads to improved credit scoring accuracy, reduced false positives (wrongly rejecting good applicants) and false negatives (approving risky applicants), and increased predictive power. This enhanced predictive power allows financial institutions to make more informed, data-driven decisions while expanding responsible credit access.
In Part 2, we’ll explore how Credolab’s innovative solutions leverage alternative data and ML to redefine risk assessment and credit scoring.
Ready to transform your risk management processes with alternative data? Explore Credolab’s risk solutions today and see how alternative data can help you stay ahead in a rapidly changing financial landscape.
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