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What is Alternative Credit Scoring and How Does It Differ From the Traditional

Discover how alternative credit scoring differs from traditional and aids in identifying reliable clients without credit histories or traditional employment paths.

Vadim Ilyasov
CTO @RiskSeal
Table of contents

Traditional credit scoring based on credit history and ratings may not always be applicable. 

Imagine the guy in his early thirties, leading a successful creative agency. He wants to grow, so he needs a loan for new talent and tech. However, banks are hesitant because he doesn't have a long credit history or a typical job path.

This shows a problem with traditional credit scoring methods: they miss out on people like this guy. 

But there's a new solution: alternative credit scoring.

We'll explain how these new methods differ from traditional ones and how they can help you identify those valuable customers. 

Let's dive in.

Why financial inclusion needs alternative data beyond the traditional

The Federal Reserve Board reports that 6% of Americans are unbanked, and another 13% are underbanked. Due to outdated credit models, banks will not be able to lend to 19% of the US population.

The issue is that the situation with unbanked people worldwide is much worse than locally in the US. 

Global Finance Magazine reports that the top five world's most unbanked countries are Morocco, Vietnam, Egypt, the Philippines, and Mexico:

This suggests that millions of people cannot rely on obtaining credit from a lending organization using traditional credit scoring.

Another standard requirement for borrowers is formal employment. 

However, recent data indicates that there are over 76 million freelancers in the US. Like credit-invisible people, they may not be able to obtain funds from a bank regardless of their creditworthiness.

To serve these demographics, credit institutions require credit risk digital transformation. They need to supplement their credit scoring algorithms with non-traditional data.

Understanding traditional credit scoring models

These scoring models use traditional data to evaluate potential borrowers:

  • Payment history. Records of payments related to financial obligations. It includes information on the date, amount, and timeliness of payments.
  • Outstanding debt. The amount the borrower owes to one or more creditors.
  • Length of credit history. The period during which a person has been using credit. Typically measured from the opening of the first credit account.
  • Types of credit in use. Forms of credit the borrower has utilized, including installment loans, credit cards, mortgages, etc.
  • New credit. Information including recent credit inquiries or new credit accounts opened by the borrower.
  • Amounts owed or credit utilization. The ratio of total debt to total credit limit across all credit accounts.

Based on this data, traditional credit scoring generates a credit score representing the client's creditworthiness. 

For example, FICO scores range from 300 to 850:

Score Rating Score Range Score Rating Description
poor 300-579 Significantly below average, lenders view this borrower as high-risk.
fair 580-669 Slightly below average, certain lenders may still sanction loans for this score.
good 670-739 Around the average, the majority of lenders deem this score as satisfactory.
very good 740-799 Higher than average, lenders regard this borrower as highly reliable.
exceptional 800-850 Far above average, lenders view this borrower as outstanding.

The borrower's chances of getting credit and receiving favorable lending terms increase as their credit score improves.

The impact of alternative credit scoring on determining creditworthiness

Traditional scoring models fail to objectively assess borrowers and significantly limit the target audience of lending organizations. 

Another problem is that traditional credit assessment does not consider the constantly changing behavior of users.

People of different generations have entirely different attitudes towards lending. For example, there are more freelancers among Gen Z and Millennials, and they are more technologically savvy.

Their lifestyle radically differs from older generations like Baby Boomers and Gen X, so traditional methods are insufficient to assess their creditworthiness. 

Learn the key findings of the U.S. Gen Z by TransUnion in the diagram below.

Gen Z credit behavior

By incorporating non-traditional data, lenders can consider borrowers' behavioral patterns, particularly those that vary based on their age group. 

This type of credit assessment enhances the effectiveness of scoring models and aids in making well-informed decisions regarding credit applications.

Alternative data sources to transform credit scoring

To expand the coverage of consumers with their services, modern credit organizations use different credit scoring models, each of which utilizes specific types of alternative data.

Non-traditional data sources

Information from social media profiles accounts on other platforms, online purchase history, subscription services, etc.

It is used to assess creditworthiness. This data enables more accurate prediction of potential risks and borrower behavior.

ML algorithms

AI-based technology can analyze large volumes of data on borrower behavior—past loans, debts, missed payments, etc. 

This analysis helps identify trends and patterns that may indicate a high level of risk.

Payment data from third-party systems

This includes data on utility and rental payments of potential borrowers. 

Information showing timely and full payments and a high credit rating can confirm their creditworthiness.

Personalized models for specific population groups

There are segments of the population that traditional banks prefer not to lend to, such as freelancers, former defaulters, elderly, or very young people. 

Non-traditional credit scoring considers these factors when building scoring models, resulting in more accurate assessments.

The 10 advantages of alternative credit scoring

Alternative credit scoring enhances the competitiveness of lending organizations by offering a range of benefits.

  1. Expansion of customer base. Alternative credit decisioning allows extending credit to individuals without a credit history or with a low credit score.
  1. Personalized borrower assessment. Lenders can incorporate various analyzed parameters beyond traditional credit history into their scoring models. For instance, the borrower's occupation, age, marital status, etc.
  1. Adaptability. Machine Learning algorithms learn from new data and adapt to it. This ensures that non-traditional credit scoring models remain relevant in changing market conditions and consumer behavior.
  1. Broader access to credit. Allows more borrowers, especially those without traditional credit histories or with low scores, to access loans.
  1. Customized scoring models. Enables the development of unique scoring models tailored to a specific business’s needs, enhancing decision-making accuracy.
  1. Personal approach to each customer. Alternative credit scoring methods provide more detailed information on borrower behavior than traditional credit assessment. This enables offering personalized banking services to customers, such as tailored loan interest rates.
  1. Reduced loan origination costs. Automation and efficiency gains help lower costs associated with loan processing, potentially leading to more favorable interest rates for customers.
  1. Bias and error reduction. Automated systems minimize the biases and manual errors that can occur in traditional credit scoring processes.
  1. Flexibility of software. Digital products for alternative credit scoring support the customization of threshold values and age requirements, allowing compliance with lender conditions. They can also expand the pool of potential borrowers.
  1. High level of data confidentiality. The use of non-traditional data implies obtaining consumer consent. Many borrowers are willing to share personal data, indicating a high level of trust in alternative scoring systems.

In addition to the obvious benefits, lending organizations that use alternative credit scoring models may face certain challenges. You can learn more about this from our guide to enhancing scorecards with alternative data.

Comparing traditional and alternative data in credit scoring

You should use both traditional and alternative data to create effective scoring models. These data significantly differ but complement each other to achieve maximum efficiency.

Aspect Traditional data Alternative data
Data sources
  • Credit reports from bureaus (e.g., Equifax, Experian, TransUnion)
  • Bank statements
  • Employment history
  • Social media activity
  • Online behavior (shopping, browsing)
  • Utility payments
  • Rent payments
  • Pros
  • Long-established and widely accepted
  • Stable and well-understood metrics
  • Regulatory compliance is well-established
  • Inclusion of individuals with limited credit history
  • Real-time and dynamic data
  • Potentially more predictive for specific populations
  • Cons
  • Limited view of an individual's financial behavior
  • May not capture recent changes or emerging trends
  • Less relevant for those with limited credit history
  • Privacy concerns and ethical considerations
  • Lack of regulatory standards and oversight
  • Limited historical data for validation and benchmarking
  • Predictive accuracy
  • Established models and metrics based on historical data
  • Generally good for assessing creditworthiness based on historical behavior
  • Potential for better prediction, especially for thin-file and no-file applicants
  • Can capture current financial behaviors and trends better
  • Costs
  • Relatively lower costs due to standardized processes and established systems
  • Initial costs for data acquisition and integration can be higher
  • Implementation time
  • Faster implementation due to standardized processes and widely available data
  • Longer implementation time due to data integration challenges and potential need for custom models
  • Alternative data performance at RiskSeal

    RiskSeal enriches scoring models with alternative data. We analyze applicants’ digital footprints and provide over 300 digital signals.

    Our latest project has shown that the effectiveness of credit scoring is maximized when both alternative and traditional data are used.

    The effectiveness of credit scoring model

    However, when comparing traditional and alternative data, the latter performs better.

    Improve your credit scoring accuracy

    With Data Enrichment


    How does RiskSeal's approach to alternative credit scoring differ from traditional methods?

    RiskSeal uses non-traditional data in credit scoring, which provides much more information about the borrower than credit history or scores alone. 

    We analyze consumer behavior on social media and other online platforms, check for subscriptions, and provide over 300 digital signals. 

    This makes decision-making on credit applications more efficient and expands the client base of lending organizations.

    What is traditional credit scoring, and how does it work?

    Traditional credit scoring assesses borrowers based solely on their credit history. Depending on factors such as the amount of debt, timeliness of payments, and others, borrowers are assigned a score – a credit rating. 

    A higher score increases the likelihood of a positive response to a credit application and more favorable loan terms.

    What kinds of data are used in traditional credit scoring models?

    Traditional credit scoring models use the following information to assess borrowers: payment history, outstanding debt, length of credit history, types of credit in use, new credit, and credit utilization.

    How do alternative credit scoring methods differ from traditional ones?

    Unlike traditional credit scoring methods, alternative ones use not only traditional data but also information from alternative sources to assess borrowers. This helps to expand the pool of lending services to people with poor or no credit history.

    What types of non-traditional data can be used in alternative credit scoring?

    Alternative credit scoring can include information from borrowers' profiles on social media and other platforms, their online purchasing history, data on subscriptions, utility and rental payments, mobile phone usage, and more.

    What are the main advantages of using alternative credit scoring?

    Alternative credit scoring has several advantages, including expanding the customer base, personalizing borrower assessment, and reducing the costs of issuing credit. 

    Scoring models are adaptable and flexible. Additionally, this approach to credit scoring helps reduce the costs of issuing credit and decreases bias and the number of errors.

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