Mastering Credit Scoring with Alternative Data - The 2025 Guide

Is your lending organization losing thousands of potential clients due to the limitations of traditional credit scoring?

Using alternative data to assess borrowers can help address this issue. It allows lending to unbanked populations, reduces defaults, and minimizes fraud.

At RiskSeal, we specialize in enriching our clients’ scorecards with insights from alternative data sources. In this guide, we’ve compiled a wealth of knowledge on this topic to help you align with the credit scoring trends expected in 2025.

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An overview of alternative data in lending

Alternative credit data is borrower information collected from non-traditional sources.

This can include digital footprints from online activities, utility payments, rent, mobile phone bills, and other sources beyond standard credit scores.

Traditional information, when supplemented with alternative data, enables the creation of a comprehensive profile of applicants and an objective risk assessment.

FICO research results confirm that the synergy of data from various sources delivers the best results.

We at RiskSeal have seen in practice that the synergy of data from various sources delivers the best results. This is illustrated in the diagram below:

So, what issues of traditional credit scoring can be resolved by turning to alternative credit data providers?

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Issue #1. Low financial inclusion

Globally, many people cannot access loans from traditional financial institutions. 

These 'unbanked' individuals lack bank accounts and credit history. According to the World Economic Forum, their number is around 1.4 billion.

These population categories include:

1. Residents of developing countries where the banking system is not sufficiently mature. For example, in Mexico and Nigeria, over 60% of the population lacks access to banking services:

2. Residents of rural areas where banks do not have physical branches. Even in developed countries, the number of unbanked citizens can be shocking. For instance, in the United States, 5.6 million households do not have a bank account.

3. Young people who have not yet established a credit history. Even with formal employment and sufficient income levels, young individuals without a credit score cannot qualify for loans from traditional banks.

‍4. Immigrants. If a person has recently arrived in a country and lacks a credit history with a local bank, they will also be unable to secure a loan.

Alternative scoring enables the evaluation of applicants without focusing solely on their financial information. This approach allows lending organizations to significantly expand their target audience.

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Issue #2. High levels of fraud

Alternative data providers can supply lending organizations with information that helps effectively combat fraudulent schemes.

One common scheme is synthetic identity fraud, where a fake identity is created to secure loans without any plan to repay. This identity often combines stolen real information with fabricated details.

This form of crime is considered the fastest-growing in the financial industry. According to industry research, U.S. banks lose an astounding $6 billion annually due to synthetic identity fraud.

Another source demonstrates the significant increase in cases of synthetic identity fraud:

Financial organizations also face other types of fraud. Among them:

Identity theft. Fraudsters deceitfully gain access to a consumer's personal information and use it to apply for a loan, naturally with no intention of repaying it.

Bust-out fraud. The criminal builds a positive reputation with the bank, and when the credit limit reaches its maximum, they withdraw the money and disappear.

Loan stacking. The borrower simultaneously applies to several lending organizations, often getting approved by multiple institutions. This happens because traditional credit scoring only considers historical data.

Using alternative credit data, like RiskSeal, offers an innovative way to detect fraud, including during the loan application process.

This approach helps lenders reduce non-performing loans (NPLs) in their portfolios and lower costs tied to expensive Know Your Customer (KYC) checks.

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Using digital footprints for alternative credit scoring

Alternative data companies provide lenders with information from various sources. However, the most valuable data is considered to be that obtained through digital footprint analysis.

A digital footprint refers to all the information left online as a result of a consumer's interaction with online resources and digital devices.

The value of digital footprints lies in the broad coverage of the global population by Internet service providers. This allows for the collection of information on a vast number of potential borrowers.

According to statistics, more than 67% of the world's inhabitants use the Internet – that’s 5.52 billion people.

By interacting with various platforms and applications, users leave various information online that is freely accessible. This information is used by financial organizations to optimize credit scoring.

Here is the information that becomes available to creditors as a result of digital footprint analysis:

1. The consumer's financial status. This includes details like the type of device used, paid or free accounts, and subscriptions.

It has been proven that certain digital footprints or their combinations indicate a high risk of default. For example, “Android + Yahoo” or “Android + Hotmail” are considered high-risk, while accessing the internet from an Apple device is a positive sign.

2. The applicant's personal traits. Certain character traits may indicate a tendency toward default.

For example, nighttime purchases or registration on gambling platforms suggest excessive impulsiveness and risk-taking behavior, which may negatively impact the applicant’s digital credit score.

3. The accuracy of the provided data. Alternative credit scoring allows for a high degree of accuracy in detecting potential fraud. Digital footprints are almost impossible to falsify.

For instance, a high-risk indicator would be the absence of social media profiles, the use of disposable phone numbers, VPNs, etc.

Digital footprint analysis allows for the enhancement of credit score modeling, improving the accuracy of predicting the likelihood of a borrower fulfilling their financial obligations.

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Top informative alternative data sources

Let’s explore the most insightful alternative data sources commonly used by lending organizations.

Email lookup

With the applicant's email address, the lender can determine whether it belongs to a real person or a fraudster.

During a reverse email lookup, the lending organization can verify the address's authenticity and gather additional details, such as the owner's information and associated accounts.

Phone number lookup

The same verification can be done based on the phone number provided in the loan application.

The validity of the number, information about the mobile network provider, and the owner's location are just a few details that can be gathered about the phone number's owner.

IP lookup

An IP address analysis can also reveal much about a potential borrower. Fraudsters often try to conceal it using special tools such as anonymizers (e.g., TOR connection or proxies).

By knowing the real IP address, the lender can obtain geographic, network, and device-related information.

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Alternative data used in credit scoring

From the alternative credit data sources listed above, lending organizations can gather various information about borrowers.

We present to your attention a brief overview of them in the table below:

Legal and ethical aspects of alternative credit data usage

The use of information provided by alternative data vendors​ obligates lenders to consider certain regulatory aspects of this process.

1. Compliance with data usage laws. Confidential user information must be collected and processed following the laws and regulations of the respective country.

For example, if you operate in the United States, you must follow the Fair Credit Reporting Act (FCRA) and the Equal Credit Opportunity Act (ECOA).

In Europe, the General Data Protection Regulation (GDPR) applies, so GDPR compliance is mandatory.

2. Adherence to ethical standards. When using alternative data in credit scoring, it is crucial to eliminate bias and discrimination.

For instance, it is unacceptable to consider a potential borrower’s race in scoring models.

3. Monitoring compliance with standards and regulations. It is important to regularly check the quality of the data used to prevent potential legal and ethical issues.  

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How is alternative data used in lending?

Alternative data helps financial organizations achieve key objectives. Here are the main ones:

Improved accuracy in assessing creditworthiness

Alternative data enhances the objectivity of credit decisions by enriching scoring models with hundreds of data points about the borrower. This allows the system to generate a highly reliable digital credit score for the applicant.

This is backed by the results of one of RiskSeal's clients, a credit organization in Mexico

The lender tracked a clear pattern: individuals with high digital credit scores rarely miss loan payments. On the other hand, a low score suggests a high probability of default:

Risk management optimization

Alternative credit scoring allows lenders to improve the effectiveness of risk management. This becomes possible because the financial institution can:

1. Identify so-called “red flags” – signs of financial instability and a high probability of default – at the loan application stage.

2. Effectively combat fraud by reducing KYC costs and improving the quality of the credit portfolio.

3. Make informed decisions on loan applications, increasing the number of approved loans without raising the default rate.

Development of personalized offers

Alternative credit data helps create a detailed profile of a potential borrower. As a result, the lender can objectively assess which credit product to offer to a specific consumer.

Here's how the assigned risk level can affect the loan terms:

Low-risk level. The applicant can expect a low interest rate, the maximum possible loan term, and no additional requirements.

Medium-risk level. The lender may require the applicant to provide a guarantor/collateral or apply a higher interest rate.

High-risk level. A positive decision on the application for such clients may be possible only if guarantees (guarantor, collateral) are provided, along with a high interest rate and a short loan term.

Reduction of fraud cases

Credit scoring with alternative data is an effective way to combat fraud.

It helps identify:

• Clear signs of criminal intent. These include discrepancies between the actual location and the one provided in the application, inconsistencies during identity verification, and more.

• Suspicious behavioral patterns. These are difficult to detect using traditional applicant assessment methods. For example, an alternative data provider might notice unusual purchasing activity or an atypical geolocation for the consumer.

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The process of enriching scorecards with alternative data

Fintechs gain significant advantages from using alternative credit data in scoring. To succeed, they must follow a strict process to enhance scorecards:

1. Define your key objectives. You need to clearly understand the target audience for your services and what you expect from your scorecard. This may include assessing the creditworthiness of applicants, detecting fraud, segmenting customers, etc.

2. Select an alternative data provider. Check if they have experience operating in your jurisdiction and ensure the quality of the data they provide. 

Many companies offer testing of their services — the so-called proof of concept (PoC). For example, RiskSeal allows clients to launch a trial project for free and evaluate the effectiveness of alternative data.

4. Integrate data into the scorecard. This process differs across financial institutions. However, it typically involves structuring the data and combining it with information from credit bureaus.

5. Evaluate and adjust the scoring model. Ensure that your efforts are worthwhile. Assess whether the alternative credit scoring model is more effective than traditional methods. 

Comparison metrics include:

• The accuracy of the scoring model
• The number of false positives and false negatives
• Model performance
• Compliance with regulatory requirements
• User satisfaction levels

If the results are unsatisfactory, make adjustments to the data set used.

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The future of alternative credit data in lending

Alternative scoring is shaping the future of credit institutions and is already taking form today. What trends can we expect to see in this field soon?

1. Intensive use of progressive technologies. Specifically, artificial intelligence and machine learning. These are applied to detect atypical user behavior, predict risks, verify identities, and more. Read more about this in our article on AI credit scoring.

2. Reliance on alternative data sources​. Lenders will increasingly depend on non-traditional data obtained from various sources to assess potential borrowers.

3. A movement toward financial inclusion. Alternative data is an effective way to solve the global problem of limited credit access for unbanked individuals.

4. Maximizing attention to compliance. The operations of credit institutions cannot proceed without adhering to legislation regarding the use of personal data and ethical standards for implementing these models in credit scoring.

5. Collaboration-based efficiency. The most effective results can be obtained by integrating the efforts of different companies, such as fintech firms, traditional banks, and IT organizations, in advancing credit scoring.

To explore this topic further, read our article on alternative data trends. 

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Boost scoring models with alternative credit data by RiskSeal

Enriching scoring models with alternative credit data is an important step for credit institutions aiming to maintain competitiveness and increase profitability.

At RiskSeal, we specialize in digital footprint analysis and provide our clients with over 300 data points about applicants.

To help you assess the quality of our alternative data, we offer a free proof of concept (PoC). During the PoC, you can evaluate:

• The depth and accuracy of the data. RiskSeal provides a comprehensive analysis of your potential borrowers' digital footprints. We deliver insights such as their financial behavior and digital credit score.
• Ease of integration. Experience how seamlessly our API integrates into your existing systems, ensuring a smooth and efficient onboarding process.
• Real-world outcomes. See tangible results, such as changes in approval rates, KYC costs, and default rates.