Alternative credit data refers to non-traditional data that lenders use to assess a borrower’s creditworthiness, especially when there is limited information from conventional credit sources such as credit bureaus. This type of data includes a variety of information not typically found in traditional credit reports, such as rent payment history and bank account information.

Alternative credit data helps lenders evaluate potential borrowers who might not have a long history with traditional credit systems, often referred to as “thin file” or “no file” customers. This demographic can include young individuals, new immigrants, or those who have avoided using traditional credit. By considering additional data points, lenders can extend credit opportunities to a broader audience and provide more personalised risk assessments.

A 2023 Experian report found that 62% of financial institutions were using alternative data to improve risk profiling and credit decisioning capabilities, highlighting the industry’s shift toward more inclusive credit assessment methods. Below, we’ll discuss how alternative credit data is used, the benefits of using it, and how to implement it into your business’s creditworthiness assessments.

What’s in this article?

• Types of alternative credit data
  
• How is alternative credit data used?
  
• Benefits of using alternative credit data
  
• Key considerations when using alternative credit data
  
• How to implement alternative credit data
  
• Regulations governing alternative credit data
  

Types of alternative credit data

Alternative credit data refers to a wide range of financial information that falls outside the traditional credit reporting system. The exact types of data considered can vary, but alternative credit data generally provides a more holistic view of a person’s financial health and can be a helpful way for those with limited or no credit history to gain access to lines of credit.

Not all lenders use alternative credit data, but lenders are increasingly using it to be financially inclusive and make more informed lending decisions.

Here are some of the key types of alternative credit data.

• Bank account data: Transaction history, account balances, and overdraft patterns can provide insight into financial habits and stability.

• Investment account data: Information on investment portfolios, including types of investments and account balances, can indicate the individual’s level of financial responsibility and long-term planning.

• Payment app data: Transaction history from payment apps can showcase payment behaviour and financial interactions.

• Recurring payment history: A history of timely bill payments can demonstrate financial responsibility and the ability to manage recurring expenses. These bills include rent, utilities (e.g. gas, electricity, water), phone and internet, insurance payments (e.g., auto, health), and even regular payments for subscription services such as streaming platforms, gym memberships, or subscription boxes.

• Public records: Information on property ownership, professional licences, and educational attainment can provide additional context for assessing creditworthiness.

• Gig economy income: Income earned through platforms such as Uber, DoorDash, or freelance work can demonstrate consistent earnings and financial stability.

• Social media data: While the practice is still controversial, some lenders are exploring the use of social media data to assess financial behaviour and risk.

How is alternative credit data used?

Alternative credit data can expand access to credit and improve lending decisions. Here’s how the financial services sector uses alternative credit data.

Assessing creditworthiness

• Holistic view of finances: By considering alternative data such as rent payments or utility bills, lenders can get a more holistic view of a borrower’s financial responsibility and stability.

• Underserved populations: Alternative data can extend credit to individuals who have been traditionally excluded due to a lack of credit history or limited access to traditional banking services, such as young adults or immigrants.

Personalising loan products and terms

• Personalised rates and offers: Alternative data can help lenders more accurately assess risk. They can use this data to determine which loan products a borrower is eligible for and design personalised interest rates based on a borrower’s individual financial situation.
  

Managing risk

• Financial warning signs: Analysing alternative data can help lenders identify early warning signs of potential financial distress, allowing them to proactively manage risk.

• Default rates: By incorporating alternative data into their risk models, lenders can potentially reduce default rates and make more responsible lending decisions.

Detecting fraud

• Suspicious activity: Lenders can use alternative data to detect patterns of fraudulent behaviour such as inconsistent income or unusual spending patterns. By identifying fraud earlier, lenders can protect customers and themselves from financial losses.
  

Benefits of using alternative credit data

Alternative credit data can create the following benefits for businesses.

• Bigger customer base: By incorporating alternative credit data, businesses can offer credit to a broader range of customers that includes individuals who don’t have extensive traditional credit histories. Serving these customers increases economic equality and inclusion by mitigating the biases inherent in traditional credit scoring systems, which often disproportionately impact marginalised groups.

• Better risk assessment: Alternative credit data provides a more comprehensive view of a customer’s financial behaviour and enables businesses to build more precise credit scoring models. This data and these improved models can reveal patterns of financial responsibility and stability that might not be apparent from traditional credit reports, helping businesses make better-informed lending decisions. More accurate credit assessments also means a reduced risk of defaults, creating a more stable portfolio.

• Higher approval rates: With a fuller understanding of an applicant’s financial behaviour, businesses can approve more loans with confidence. This boosts revenue, customer satisfaction, and customer loyalty by extending credit to those who might be denied based on traditional criteria.

• Easier compliance and fair lending: Using a broader set of data points can help companies demonstrate their commitment to fair lending practices. This aligns with regulatory goals of making credit access more equitable and mitigates legal and reputational risks associated with biased lending practices.

• More effective, customised financial products: Alternative data enables lenders to segment their customers with more granularity. For instance, consistent payments on small-scale subscriptions or utility bills might indicate financial stability in customer segments traditionally viewed as higher risk. Lenders can use this detailed segmentation to create custom financial products that meet the specific needs of different customer groups.

• Real-time credit assessments: Traditional credit reports are based on historical financial interactions, but alternative credit data shows a current snapshot of a borrower’s financial health. This can help lenders make real-time lending decisions, especially in fast-paced markets or scenarios where borrowers’ financial circumstances are quickly changing.

• Improved customer loyalty and retention: Businesses that successfully use alternative data to extend credit to new segments often see increases in customer loyalty and retention rates. For customers who might have been denied credit based on traditional metrics, receiving credit approval can create a strong loyalty bond and make them more likely to engage with additional services from the same provider.

• Alignment with growing trends: Customer behaviours are shifting toward digital platforms and away from traditional credit-reliant purchases, creating financial histories that traditional credit scores do not reflect.

Key considerations when using alternative credit data

Businesses should consider the following factors when engaging with alternative credit data.

Data quality and accuracy

• Source reliability: Check that the alternative data provider has a reliable track record and adheres to strict data collection and validation processes. Unlike traditional credit data, which is typically regulated and standardised, alternative data can vary widely in quality and format.

• Data accuracy: Businesses need to establish strong mechanisms to verify the accuracy and completeness of alternative data. Confirm that data is up-to-date to reflect a borrower’s current financial situation.

Regulatory compliance

• Fair Credit Reporting Act (FCRA): In the US, lenders need to comply with FCRA regulations when using alternative data for credit decisions. Compliance involves obtaining customer consent, verifying data accuracy, and giving customers the right to dispute incorrect information.

• Other regulations: Stay informed about and comply with relevant local regulations such as privacy and consumer protection laws.

Ethical considerations

• Transparency: Be transparent with customers about how you are using alternative data and how it affects their creditworthiness assessment. How businesses collect and use alternative credit data can substantially impact customer trust.

• Privacy: Protect customer privacy by implementing strong data security measures and only using data for legitimate purposes.

• Fairness: Ensure that using alternative data does not disproportionately impact certain customer groups or perpetuate existing biases. For example, reliance on certain types of social media data could disadvantage groups less active online. Businesses need to continuously monitor and test their models for fairness and unintended biases.

• Economic implications: The use of alternative data can have broader economic implications. For instance, expanding credit to previously underserved populations can stimulate economic activity, but it also requires careful monitoring to avoid creating new financial bubbles or exacerbating economic disparities.

Technical integration

• Data integration: Integrate alternative data into existing credit scoring models and decisioning systems.

• Scalability: Choose a data provider that can scale with your business needs and accommodate future growth.

• Data security: Implement data security measures to protect sensitive customer information from unauthorised access or breaches.

Cost-benefit analysis

• Cost: Evaluate the cost of acquiring and integrating alternative data, as well as any ongoing maintenance or subscription fees.

• Benefits: Assess the potential benefits of using alternative data such as increased loan volume, reduced default rates, and improved customer experience.

• Return on investment (ROI): Determine whether the potential benefits outweigh the costs and if the investment in alternative data will yield a positive ROI.

How to implement alternative credit data

The following best practices can help you effectively integrate alternative credit data into your lending processes to meet your business’s specific goals.

Identify goals and needs

• Define objectives: Determine why you want to use alternative data. Do you want to expand your customer base, improve risk assessment, or design more personalised loan products?

• Target borrower segments: Identify the specific borrower segments you want to reach with alternative data. This could include individuals with thin or no credit history, underserved populations, or those seeking custom financial solutions.

• Data requirements: Determine the specific types of alternative data that would be most relevant and valuable for your lending decisions.

Choose a data provider

• Provider research: Explore various alternative data providers, and compare their offerings, data sources, data quality, pricing models, and integration capabilities.

• Due diligence: Conduct thorough due diligence on potential providers, confirming that they have a reliable track record, adhere to regulatory compliance, and prioritise data privacy and security.

• Provider selection: Choose a provider that best aligns with your goals, budget, and technical requirements.

Integrate data into systems

• Technical integration: Work with the data provider to integrate their data into your existing credit scoring models, loan origination systems, and decisioning platforms.

• Data validation: Properly format, validate, and cleanse the data for accuracy and consistency.

• Data mapping: Map the alternative data fields to relevant attributes in your existing systems for seamless integration and analysis.

Develop risk models and strategies

• Model development: Incorporate alternative data into your credit risk models to improve predictive accuracy and refine lending decisions.

• Strategy development: Develop lending strategies that use alternative data to target specific borrower segments, offer personalised loan terms, and manage risk.

• Testing and validation: Thoroughly test and validate new models and strategies to ensure they perform as expected and meet regulatory requirements.

Monitor and refine

• Ongoing monitoring: Continuously monitor the performance of alternative data models and strategies, tracking key metrics such as approval rates, default rates, and customer satisfaction.

• Refinement: Regularly refine models and strategies based on performance data and feedback to optimise lending decisions and improve outcomes.

Regulations governing alternative credit data

Regulations governing alternative credit data strike a balance between encouraging innovation in financial services and protecting consumers. Businesses handling any kind of credit data should use the following best practices to comply with relevant regulations.

• Due diligence: Perform thorough due diligence on all data sources to ensure they comply with applicable laws.

• Model validation and documentation: Regularly validate models for accuracy and fairness and maintain detailed documentation of data use, model development, and decision processes.

• Consumer transparency: Provide clear, accessible information to customers about what data is collected, how it is used, and how they can access or contest that information.

• Training and governance: Establish governance policies for data use and conduct regular staff training on regulatory requirements and ethical considerations.

Businesses working with credit data (including alternative credit data) must also comply with the following requirements, depending on their location.

Fair Credit Reporting Act (FCRA)

In the United States, the FCRA regulates the use of credit information and requires that the information used in credit decisions be accurate, relevant, and timely. Businesses using alternative credit data must ensure their data sources and processing methods comply with FCRA standards. Compliance includes providing customers with notices when businesses take adverse actions based on credit reports and allowing individuals the opportunity to dispute and correct credit information.

Equal Credit Opportunity Act (ECOA)

The US’s ECOA prohibits discrimination on the basis of race, colour, religion, national origin, sex, marital status, age, or because a person receives public assistance. Lenders using alternative data must carefully analyse their credit models to ensure that they do not inadvertently discriminate against protected classes, either directly or through disparate impact.

State-level regulations

Various US states have their own laws and regulations that affect the use of credit data. California, for example, has enacted the California Consumer Privacy Act (CCPA), which dictates consumer rights in how their personal information is collected and used. Businesses operating in the US need to comply with regulations at both the federal and state level.

General Data Protection Regulation (GDPR)

For businesses operating in or dealing with data from the European Union, the GDPR imposes strict requirements on data privacy and security. It mandates transparency about how personal data is used, limits data collection to strictly necessary purposes, and gives individuals extensive rights over how their data is used. Businesses using alternative credit data must comply with GDPR considerations such as data minimisation and obtaining explicit consent from individuals.

Global data protection laws

Countries across the globe have their own versions of data protection laws that impact the use of alternative credit data. Laws such as Canada’s Personal Information Protection and Electronic Documents Act (PIPEDA) or Brazil’s General Data Protection Law (LGPD) contain similar principles to the GDPR, requiring businesses to manage data responsibly.