Credit Bureaus such as CIBIL are responsible for keeping historical information of credit of individuals and businesses and credit history is gathered from lenders and other financial sources to obtain the credit score.

For individuals and small businesses not having access to regular banking services, standard credit scores from bureaus are not available; and hence they are unable to get approved for loans. Due to the lack of financial inclusion in India, particularly in remote areas, it has become increasingly difficult for certain groups of people to prove their creditworthiness.

As per an article published in Financial Express 2019, 23% of customers were new to credit and hence had no credit history.

A “thin file” refers to the credit report of someone with little or no credit history. Consumers who are just starting out and may never have taken out a loan or had a credit card are said to have thin files. Expedia, a credit bureau, defines a “thick” file customer as one with more than five accounts that have been open for longer periods of time

As of December 2020, 42% of the consumers in India have very low credit scores (near-prime and subprime categories).

While “traditional data” refers to an individual’s credit report, alternative data for credit scoring includes a lot of information that is not typically associated with criteria used to determine credit scores. Alternate data for example could include insurance payments, utility payments such as water and electricity, rental payments, or even public records, and property records. These data points can help lenders get a sense of an individual’s financial reliability which can help gain a clearer picture of the potential risk of lending to a particular individual.

Alternative credit data goes beyond predictable credit bureau data, which usually focuses on long-established credit activities and modern credit-seeking behaviors of the consumers. However, the issue with most alternate sources is that the data might not exist for 100% of the borrowers, hence is limited in its own way.

What happens to borrowers with little or no credit history?

For the section of people & businesses that have trouble getting finances, being able to establish credit is almost impossible. Even though some consumers or small businesses have a thin file doesn’t mean that they are high-risk borrowers.

Credit-challenged and underbanked customers are not automatically risky customers. They actually lack credit history data typically used by lenders to verify their creditworthiness. Among the credit-challenged or underbanked or ‘thin-file’ clients, technology can help distinguish non-risky habits that are characteristic of ‘thick-file’ clients and find common micro-behavioral trends, thereby helping to increase the number of potential borrowers while keeping default risk at the same level. The lack of coverage in different forms of alternative data is a concern too, as data is often limited to people that have certain access to mobile phones, apps, internet access etc.

How does GeoIQ’s data help understand credit risk better?

The creditworthiness of an individual determined using alternate data sources is usually done with the help of data-backed decisions models. While lending businesses collect and verify information about the borrowers and build models to assess their creditworthiness, GeoIQ’s data helps to provide a better understanding of potential borrowers by adding locational characteristics about the borrower.

Credit risk assessment by augmenting location-based data helps to provide a comprehensive view into credit risk — particularly for those consumers who cannot be scored by traditional credit assessments. According to the World Bank Global Findex Report of 2017, about 190 million of India’s population has no bank accounts and hence resorts to borrowing from local money lenders at very high rates of interest. Hence with the help of alternate forms of credit assessment, lenders can bring in an increased number of creditworthy consumers.

The advanced algorithms used help to assess a potential borrower’s creditworthiness by analyzing the area he lives in or the area he conducts business (e.g. rent rates in the area, brands and stores located in that area, affluence level of people who reside in that area i.e. income, etc).

If bureau data is limited or unavailable for consumers, location data augmentation also helps to build better profiles and provides a lift in the accuracy of the existing credit risk assessment models. This in turn can help reduce the bad rate, or increase approval rates of potential borrowers while keeping the bad rate at current levels.

The key benefit of using location data is that it provides 100% coverage of the population whether for existing customers, new customers, or potential customers and therefore, the impact is greater compared to other alternate sources where coverage is limited to data obtained at a device level or via a phone number or an external app. GeoIQ data also provides good results at the top of the funnel, as a starting point, to help filter out at the initial stage.

In conclusion, not only can lenders use insights from location data to make better credit decisions for consumers with low or no credit scores, and it can also help them gain a substantial increase in approval rates while sticking to their current default risk limit. Growth can be achieved while tightly managing risk across the credit spectrum.

At GeoIQ, we specialize in providing geospatial data analytics and location intelligence services by collecting and collating data from various reliable sources to develop a better understanding of locations and make them available for businesses to use.

Visit our website to know more about us.