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Alternative Credit Scoring – Financial Salvation For Those With Low or No Credit Score

credit scores

Reports estimate that over one-third of the American population has no record in any of the credit bureaus and, therefore, have no credit history. Millions of Americans do not have access to financial services, and this is an even more common scenario in developing markets. Over 80% of the African population do not use lending or banking services because they have no fixed income. The situation creates a question mark on the relevance of traditional credit scoring agencies and their impact over the larger population. Thankfully, fintech innovators are heading towards new, alternative data sources like rent payments, cell phone data, and even social media usage to evaluate credit risk. The aim is to replace traditional credit models with a more complete assessment of a prospective borrower. The focus is to create a win-win situation for both lenders and loan seekers by providing a new foundation to lenders for credit underwriting and providing millions of borrowers a chance to step up on the credit ladder.

The Current Credit Scenario

People with a low or no credit score find it impossible to prove their eligibility for loans. The most disenfranchised are minorities and women. This then creates a vicious circle as they can’t get a loan due to no score, and they can’t improve their score because no one is ready to give them credit.

Currently, the credit score of a person is calculated depending on the information in his credit reports. This information consists of the person’s name, phone number, social security number, employment information, account information, loan repayment details, and credit card accounts.

Alternative Credit Scoring Models

The following alternative credit scoring models have a different approach, leveraging their own data sources, proprietary algorithms, and technology to disrupt existing industry systems. The idea is to reach new audiences and onboard creditworthy borrowers who are lost due to the current model’s shortcomings.

FICO Score XD

FICO (Fair Isaac Corporation) is best positioned to bring the change. Its FICO is synonymous with credit scoring and is usually the most important element in deciding if a person can qualify for a loan.

FICO’s new alternative model, FICO Score XD, developed in partnership with LexisNexis Risk Solutions and Equifax, considers alternative data sources like internet and phone bills to help users who do not have credit data attain a score.

The new scoring system has had a major impact:

  • FICO Score XD 2 was able to score over 26.5 million previously unscorable consumer files
  • 11.8 million were without any credit file and unscorable via any traditional bureau
  • The new system has increased coverage from 91% of applicants to almost 98% of applicants

CreditVision Link

Another scoring model has been developed by credit reporting giant TransUnion in order to integrate alternative data and provide a solution to those who do not have any credit score. It concentrates on “trended data” as compared to only considering historical data in silos. So it not only evaluates the current situation of the borrower but also the credit details for last two years for a more comprehensive credit-scoring model. It has been able to score over 60 million borrowers who were previously unscorable.

Cignifi

Cignifi, based in Cambridge, Massachusetts, is an interesting alternative credit scoring startup. Backed by the Omidyar Network, it provides a platform for providing credit and marketing scores for consumers (especially in the developing world) via mobile phone behavior data. Its big data engine allows the mobile network operators and insurance partners to discover eligible customers for credit cards, loans, insurance, savings, and other banking services.

Cignifi’s main focus is on “financial inclusion,” which uses risk scoring technology to serve the unbanked population who do not have credit scores. It leverages mobile and texting patterns, routine of being at workplace or home, and contact with reputable borrowers.

CreditVidya

This India-based fintech startup offers alternate data-based credit scores for underwriting first-time borrowers using machine learning and big data analytics. It has raised $7 million in funding and uses over 10,000 digital footprints via in-house designed APIs. It has been able to help lenders attract first-time borrowers as well as reduce the processing time from days to 30 minutes.

India alone has 800 million unscored individuals, thus the market size is huge and lenders desperately need a service which can help them tap such a massive segment of the population.

Advantages of Alternative Credit Scoring Models

  • Access to a wider customer base: It enables the widening of the prospective borrower base. This is extremely vital for fintech lenders as it is difficult for them to compete with the big banks on pricing. But their ability to utilize new credit models and give borrowers with “thin” credit files a chance will lead to the expansion of the entire market.
  • Customer experience: Alternative credit scoring also automates the process of credit decisioning and allows for a more hassle-free digitally-enhanced experience. This enhances the utility to millions of borrowers who were otherwise supposed to visit their nearest branches for processing of their loan applications.
  • Improved underwriting process: All the alternative data adds a layer of analytics to the existing data, as well. This gives deep insight to the underwriters and helps in developing an enhanced credit-scoring model.

Conclusion

These alternative credit-scoring models aim to bring banking to the unbanked. This empowers lenders to reach out to individuals who were rejected for the reason that they had no/thin credit flies. The new models will shake up the industry, and lenders incorporating them into their credit underwriting process will see better traction and stronger customer loyalty, especially from those who were earlier denied credit on a faulty premise.

Author:

Written by Heena Dhir.

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