Technological innovation and information explosion has enabled the dynamically evolving financial services industry to solve everyday problems for millions of people across the globe. Also, there has always been the search for new sources of revenue – especially in the lending market.
The biggest challenge for lenders is to assess the creditworthiness of applicants, so as to avoid the risk of bad debt. Traditionally, leading players like Experian, TransUnion and Equifax calculate the credit scores based on a person’s historical financial and repayment data, which is stored by credit bureau across the world. Almost everything, from loans to rental homes, is given based on a person’s credit score.
However, there is one major problem here.
For over 4.5 billion people globally – a majority of them from low and middle income emerging countries – such credit or repayment data is not available. In the US alone, there were 26 million credit invisible consumers in 2015. A major section of society, worldwide, does not have a decent credit score. Apart from people with poor credit history due to irregular financial transactions in the past, a major chunk of the global population does not even have a credit score due to lack of financial history. Migrants, travelers, war veterans and millennial with no significant financial transactions do not have a credit history. Unfortunately, these with no credit histories and those with poor credit are often treated similarly. As a result, these people are excluded from the credit spectrum, and they become handicapped in their ability to access credit and improve their lives.
In a recent report, Richard Cordray, the CFPB Director, emphasized that “A limited credit history can create real barriers for consumers looking to access the credit that is often so essential to meaningful opportunity – to get an education, start a business, or buy a house. Further, some of the most economically vulnerable consumers are more likely to be credit invisible.”
However, there is nothing that technology cannot resolve. Financial inclusion for the underserved, unbanked population has always been one of the biggest challenges for the financial services industry. Lenders are vying for alternative means of assessing an applicant’s creditworthiness, by leveraging big data and machine learning capabilities. Various innovative and non-conventional sources of data, called as Alternative data, are being used for assessing the creditworthiness of a loan applicant. Some of the examples include a person’s digital footprints in form of social media presence; utility bills payment data, psychometric analysis, etc.
We at LTP have recently published a report analyzing this space, titled: “Alternative Credit Scoring: Enabling Financial Inclusion of Underserved.” In this report, we dive deep into how the use of non-traditional data can bring the thin file and no file applicants in the credit mainstream.
- What are the different types of alternative/non-traditional data sources?
- How many people, globally, are beyond the traditional credit spectrum, i.e. the underserved segment?
- What problems does alternative credit scoring solve?
- Who are the major players in alternative credit scoring and how are they analyzing digital footprints to assess creditworthiness of individuals?
- Opportunity and challenges in the space of alternative credit scoring?
- How does alternative credit scoring enable the financial inclusion of the underserved segment of borrowers?
- How can lenders minimize the risk associated with the use of alternative data sources for credit scoring?
You can access the full report here.