Buzzword like financial inclusion has been trending for the last few years. The latest data reveal that the majority of the population globally is not served under formal financial segments. If we take the example of developed economies like the US, 60 million adults are credit-invisible, which means they have little to no credit history. These credit-invisible people can’t prove their creditworthiness as the standard credit scores are not available for them.
Recent developments around Artificial Intelligence and Machine Learning have created a way for credit-invisible to apply for credit now. At present, alternative credit data can help lenders analyze and approve loan applications for more customers. At the same time, alternative credit data is providing a better accuracy with predictive functionality for credit risk modeling.
Alternative credit data is information derived from sources of non-traditional data that helps determine the creditworthiness of a customer. Although “traditional data” refers to a credit report of a person, alternative credit rating data contains non-credit information on insurance payments, service payments, rental payments, public records, and records of the property. These data points will help lenders get a sense of the financial reliability of a person. By substantiating the reliability of a person’s financial condition, even the job history of the customer will aid in credit scoring.
Differences between traditional and alternative data on credit profiling:
Alternate data is supplementary and lenders can not just use is for credit profiling. It can also be used to curate financial products that are best suited for specific customers. Companies are also launching financial assistance help, outlining the risk profile and the best way to meet their long term goals. For those who have inadequate traditional credit data for credit rating, the only way to get a loan could be with alternative data. Today, by using alternative data lenders are able to serve more customers.
Alternative data sources (such as asset records, tax records, current and debit accounts, salary and income data, location, and spending behavior) have shown that more than 90% of applicants would be analyzed correctly instead of getting outrightly rejected.
Sources of alternative data on credit
Alternative credit score data comes from multiple sources; some examples include digital fingerprints, full-file public records, and banking accounts. The source and types of alternative credit data may depend on the assessing entity and its business model.
A strong alternative credit data source, according to research by Oliver Wyman, should have the following characteristics:
- Coverage: There should be broad and clear coverage for an alternative data source. Any variable which is not prominent in the wider population set may not give an accurate result
- Specificity: Details explicitly about the borrower and provide a full picture (e.g., on-time and late payments over a significant time series, or specific asset or income data)
- Accuracy and timeliness: Alternative credit information must be reliable and recent or modified regularly
- Predictive power: For the actions being expected, the information from the source should be appropriate
- Orthogonality: Alternative credit data should supplement conventional data from the credit bureau
How can alternative credit data for lending be collected?
There is a conventional way to collect a lot of alternative credit data: by asking customers. It is possible to ask loan applicants to provide additional details about themselves. Lenders may ask customers to allow them access to accounts, credit cards, savings, and cell phones with account information.
Another way of obtaining alternative credit data is to get assistance from different data aggregators. Partnering with a supplier or using a platform for data aggregation will help a credit bureau gain access to the information needed about its customers. Using aggregation technology may make the loan process move faster and more effectively. With the developments around open banking and the launch of account aggregation platforms are making it easier for traditional lenders to collaborate with modern fintech solution providers.
How do alternative credit data in credit scoring assessment improve accuracy?
Collecting alternate data points is not enough, rather it’s just the starting point. After collection, data then needs to be analyzed. Scanning through the transaction history of an individual is tedious. To gain instant insights from various datasets, modern lenders use AI technologies and machine learning solutions. An AI model can evaluate copious quantities of alternative credit data rapidly, reducing the processing time significantly. More notably, machine learning algorithms can recognize trends in unstructured data that help determine the customer behavior of a loan borrower and predict the ability to repay the loan.
Though, alternative credit data capture more information about the borrower provides a more detailed insight. According to a recent study conducted by FICO, alternative credit data cannot be used alone in calculating the credit score. A more reliable model would be a mix of both conventional and alternative credit data points.
Chart showing how performance is affected by combining traditional and alternative data points
Credit score using alternate data points may impact all borrower segments. It helps to distinguish lendable applicants from risky ones, making it much easier to decrease credit risk with alternative credit data.
Chart representing movement in lendable population after addition of alternative data:
Source: Oliver Wyman
According to Experian, 65 percent of lenders state that they already use certain details to make lending decisions outside the conventional credit study. In order to establish credit scores, many of them use alternative credit data; doing so can have many advantages. In cases where an individual does not have a credit profile or a credit history of at least six months, where there is no historical data, alternative credit data may provide predictive assistance. Lenders may increase the number of their customers with alternative credit data. In addition, for all types of applicants, alternative credit data can help to establish a robust scoring process. In credit risk modeling, the use of alternative data helps to classify risky customers.
Pirimid helps clients implement AI technologies to more effectively and efficiently operate with copious quantities of information gathered from a variety of sources. Using machine learning algorithms we can minimize the time for data evaluation and provide a complete overview of the creditworthiness of a customer. The end solution is not just limited to lending, this can be customized to suit a wide range of business needs for businesses in sharing economy, e-commerce, and digital insurance providers. Drop a comment or write us an email with any feedback about this article or business inquiries firstname.lastname@example.org.