Alternative data refers to non-traditional data sources that can be used to assess creditworthiness or make financial decisions. These data sources can include information such as rent payment history, social media activity, or utility bills, which are not typically considered in traditional credit assessments. 

Alternative data has been introduced into the financial service and led to a significant shift in credit access for unbanked and underbanked consumers. Alternative data is the future of financial inclusion and enabling lenders to extend credit to unbanked consumers using various data sources to boost both traditional and alternative credit models.

By using alternative data, financial institutions can gain a more comprehensive understanding of an individual's creditworthiness, which can help reduce inequality in banking and fintech in several ways:

• Increase access to credit: Alternative data can help expand credit access to people who may not have a traditional credit history or have limited credit history. This can include people who are new to the country, students, or those who rely on cash transactions. By including alternative data in credit assessments, lenders can better understand an individual's financial behaviour and risk, and may be more willing to extend credit to those who would otherwise be denied.
• Reduce bias: Traditional credit assessments can be biased against certain groups, such as low-income individuals, people of colour, or immigrants. Alternative data can help reduce these biases by providing a more complete picture of an individual's financial behaviour and risk, rather than relying solely on traditional credit history. By reducing bias, financial institutions can create a more inclusive financial system and help reduce inequality in banking and fintech.
• Lower costs: For people who have limited or no credit history, obtaining credit can be expensive due to higher interest rates or fees. By using alternative data to assess creditworthiness, lenders may be able to offer lower interest rates and fees, which can make credit more affordable and accessible.

How Alternate Data Reduces The Inequality in Lending?

One of the key ways that alternative data can reduce inequality in lending is by providing access to credit to those who would otherwise be deemed too risky by traditional lending models. This includes individuals who are underbanked or have limited credit histories. For example, someone who has never taken out a loan or had a credit card may not have a traditional credit score. However, by analyzing their financial behaviour through alternative data sources, lenders can gain a more complete picture of their creditworthiness and make lending decisions accordingly.

Moreover, alternative data can also help reduce bias in lending decisions. Traditional lending models are often biased towards individuals with higher incomes, more stable employment histories, and more established credit histories. By using alternative data, lenders can make lending decisions based on a wider range of factors, reducing the impact of these biases.

Evolution Of The Credit Ecosystem

A few credit bureaus are already participating in helping underbanked consumers join the mainstream. They have acquired or partnered with companies specializing in alternative data. For example, KreditBee has partnered with Digitap in strengthening their underwriting process by a report.

Still, some alternative data is not easily available to lenders. 

The emerging credit building programs are providing reports with limited data. With Digitap alternative data suite lenders are able to include more granular information and understand consumer financial behaviour and enable lenders to extend more appropriate loans.

Use Cases of Alternative Data

Financial institutions are now extending access to the alternative data that incumbents have found difficult to get access.

As new data aggregators have empowered the credit ecosystem, lenders have access to a rich set of data to better assess a consumer’s risk. Financial institutions have become more careful about privacy and protection while accessing confidential data. In earlier practices, a number of credit bureaus and fintech players were asking consumers to proactively share their alternative data. Lenders allow consumers to submit their bank account and utility payment data when applying for credit.It increases consumers’ chance to obtain credit and also helps lenders to increase their customer bases. Alternative credit providers are also growing at the expense of traditional players by incorporating alternative data into their lending processes. 

Challenges With Alternative Data

Adoption rate of alternative data sets for lending is growing rapidly, but limited to only lenders and consumers.

Most traditional lenders are not yet advanced credit scoring models in their decision making processes. It remains to be seen how that will change as millennials and Gen Z, counted as an increasing percentage of potential borrowers, will need new products and services that will require the inclusion of alternative data.

The Future Of Alternative Data

Alternative data has taken an important place for credit access. It has become the great equalizer in multiple ways.

Bringing unbanked adults and businesses into the traditional banking sector and thus promoting financial inclusion could generate a good figure in revenues for banks across the globe. Financial inclusion has opened a plethora of opportunities to consumers and has contributed to the growth of entire economies. 

In summary, alternative data can help reduce inequality in banking and fintech by expanding access to credit, reducing bias, and lowering costs. However, it's important to ensure that alternative data is used ethically and transparently, and that individuals have control over their personal data.

Digitap’s alternate data suite for digital lenders will help your fintech business to be more efficient in customer onboarding and credit underwriting.

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