As per a study by the Consumer Financial Protection Bureau (CFPB), about 26 million Americans are “credit invisible,” which means they have no credit history with a nationwide credit reporting agency, and another 19 million have credit records that are too sparse or outdated to be scored. This highlights a significant limitation of traditional credit scores, as they do not always accurately reflect the true risk, especially for individuals with limited or no credit history.

Here are some reasons why traditional credit scores may not accurately reflect the true risk:

1. Limited or No Credit History:

Traditional credit scores rely heavily on an individual’s credit history. Therefore, individuals with limited or no credit history often have low scores, even if they are financially responsible.

2. Economically Disadvantaged Groups:

Traditional credit scores may disproportionately punish borrowers from economically disadvantaged groups. These individuals often face greater difficulties in obtaining their first line of credit as both account age and length of payment history are major factors in the scores.

3. Inability to Reflect Current Financial Status:

Traditional credit scores persistently penalize borrowers who have experienced derogatory credit events such as delinquencies, even when those events no longer indicate their ability to pay.

How can these issues be addressed?

Financial institutions (FI) could explore using alternative data or more sophisticated statistical techniques in custom credit scoring and underwriting.

Let’s understand what alternative data is.

Alternative data refers to any data not traditionally used in credit scoring, typically sourced from non-financial personal information. This data can help assess a customer’s creditworthiness, especially those with limited or no credit history.

Here are some ways in which alternative data can be used:

By incorporating these types of alternative data, a more holistic view of a borrower’s financial situation is gained by the lender, potentially leading to more accurate credit scoring and increased access to credit for those with limited or no credit history.

The other approach could be thorough risk segmentation. This involves grouping borrowers into distinct segments based on risk scores, allowing for more tailored lending strategies.

Risk segmentation is crucial to managing credit risk exposures based on risk profile and behavior. It involves grouping borrowers into distinct segments based on their risk scores and behaviors. This allows for more tailored lending strategies, as lenders can develop specific products and services for each risk segment. For example, borrowers in the low-risk segment might be offered lower interest rates, while those in the high-risk segment might be offered secured loans or loans with higher interest rates. This approach allows for more nuanced and fair lending practices. Risk segmentation allows FIs to assess and manage credit risk more effectively.

Here are some ways FIs can do risk segmentation of prospective borrowers and facilitate borrowings without adequate credit history:

1. Understanding Risk Profiles:

Several factors are considered when segmenting credit risk. These may include income levels, employment history, debt-to-income ratios, and past credit behavior. By analyzing these factors, lenders can gain a comprehensive understanding of the borrower’s creditworthiness.

2. Types of Credit Risk Segmentation:

• Demographic Segmentation: This approach involves segmenting borrowers’ demographics derived from specific characteristics. It helps lenders identify patterns and trends within specific demographic groups.
• Behavioral Segmentation:  This segmentation focuses on the borrower’s past credit behavior, including payment history, delinquency rates, and credit utilization. By categorizing borrowers based on their behavior, informed lending decisions can be made by lenders to assess the likelihood of defaults.
• Industry Segmentation:  In this approach, credit risk is segmented based on the industry or sector in which the borrower operates. Different industries may have varying levels of credit risk, and lenders need to consider these factors when evaluating creditworthiness.

Advanced machine learning and statistical techniques algorithms can be used to analyze both traditional and alternative data which helps in identifying trends and patterns that may not be apparent through manual analysis. Once the segmentation models are developed, they need to be implemented effectively. This involves integrating the models into the FI’s existing systems and processes, training staff on how to use the models, and continuously monitoring and updating the models to ensure they remain accurate and effective.

By implementing these strategies, FIs can facilitate borrowings for individuals without adequate credit history, thereby promoting financial inclusion. The use of custom credit scores and risk segmentation can significantly improve the accuracy of credit risk assessment, particularly for individuals with limited or no credit history. It can lead to more fair and inclusive lending practices benefiting lenders and borrowers. However, it’s important to note that such solutions should be implemented ethically and responsibly, ensuring the security and privacy of an individual’s data.

Paresh Ashara is a Vice-President at Quinte Financial Technologies, leading the Data Analytics-as-a-Service. He brings 26 years of IT services and product engineering experience in the banking vertical. He is passionate about data management and analytics and takes active interest in discussing business solutions with clients, prospects and sharing knowledge with academia. He can be reached at paresh.ashara@quinteft.com.