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Use of Third Party Data Sources in Personal Lending
The personal lending market is one of the biggest that holds a wealth of promise for bankers and lenders. Yet, this market seems to be largely unserved and lacks the competitive edge that is usually seen in large business lending. The reason is simple. Banks typically want to lower the risk component and also keep their underwriting costs low to enjoy the benefits of lending, the traditional way. This is why banks prefer lending to larger business with better credit scores and whose underwriting costs are comfortably taken care of, given the size of the loans large businesses apply for. However, the same cannot be said for the personal lending and SMB loan market. Both these markets do not promise extraordinary credit scores, thereby increasing the risk for banks, and most importantly, the underwriting costs for these loans often don’t justify the loan amount that has been requested.
The otherwise overlooked personal lending market, which moved further back in the shadows post the subprime crisis of 2008, has stood neglected for a long time, with little or no promise of showing any surge. But, that was until fintech started moving into the SMB and personal lending market. With their innovative solutions and algorithms that churn numbers in matter of seconds, access and use of big data to conduct predictive analysis, fintech has taken the personal lending industry by storm.
The numbers speak for themselves: Outstanding balances increased about 18% in the first quarter of 2017-18 to a staggering $120 Bn.
Unlike business loans, the reasons people might want to opt for a personal loan are many:
Money for relocation
Paying for a wedding
Pay medical bills
Finance funeral expenses
Buy a car or RV
Pay off credit cards
Sponsor an adoption
And the list goes on. Just like the many needs of an average family, the opportunities in the personal lending market are infinite. What the applicants of these loans need is fast processing of loans and easy repayment facility. Two factors traditional banks are failing to or rather refusing to address given the high underwriting costs and risks attached to it.
How can third party data sources help?
The simple answer to the question of reducing both risk and underwriting costs is having sufficient information about the loan applicant. Most lending companies prefer using FICO scores for their underwriting process. Over the years, companies saw the effectiveness in using third party data sources along with FICO scores to ascertain the risk attached to applications. As per Experian’s 2018 report on the State of Alternative Credit Data, about 80% lenders use FICO scores, as well as, at least one third party data source.
Third party data source not only help lenders understand the liquidity or credibility of the borrowers but also their ability to repay, thereby reducing overall risk attached to extending personal loans to individuals.
For example, millions of personal loan applicants in the US can be profiled as ones with thin credit profiles. This leads to lenders overlooking lending opportunities in a huge market segment that could add to its revenue and margins. Banking solely on FICO scores might not help realize much from this market, as it may show low to nil scores for a majority of these profiles. However, when using third party data sources, lenders have access to more information about the applicant, apart from their FICO scores, like proof of consistent income from a stable employment and dependable payment histories that let lenders take a better decision based on a wider net of information from reliable third-party sources.
Here are some of the information that are available on third-party sources that can help lenders make informed loan origination decisions and reduce underwriting cost:
Records of judgements, liens, or bankruptcy
Utility bills, cable tv and phone bills along with bill payment history
The above-mentioned parameters give underwriters or the algorithms fintech use enough information to decide on whether or not an applicant qualifies for a loan, irrespective of their FICO scores, which may or may not give enough information to help in loan origination.
Using automation to reduce underwriting costs for loan origination
In order to not let the cost of underwriting be a barrier to serving the expanding personal loan market, fintech has embraced automation which makes the process of personal loan origination faster, cheaper and easier and also promises better customer experience throughout the process. Thanks to the advent of big data, companies now have access to incredible amounts of information which can help them make the right and consistent loan origination decisions in a fraction of time that was otherwise impossible.
Algorithms and decision rules help companies make loan approval decisions in less than 24 hours time and sometimes in a matter of just 7 minutes, as opposed to a week’s time taken by traditional methods adopted by underwriters.
In fact, after analyzing the data and passing it through the decision rules, if any application is indeed in need of personal verification by an underwriter, then only are these applications passed on to underwriter’s queue, thereby reducing cost of operations by a substantial amount. For the ones that do not require perusal by an underwriter can be applied with preset funding rules and approved in less than a day’s time.
The benefit of the operational savings from reduced underwriting costs is then often shifted to the end consumer, i.e., the loan applicants in the form of reduced interest which restores faith and ensures better customer experience and loyalty from the applicants. Needless to say, consumer retention adds to the portfolio of the lenders/banks/fintech, thereby helping them expand and retain their market share.
Third party data sources go a long way in helping lenders expand their market share and reduce their operational costs. They also help lenders find out new loan origination opportunities which would otherwise go overlooked. For example, by running the available data, thanks to third party sources, through algorithms and preset rules, lenders can identify loan opportunities for SMBs even before they realize they might need one. In this way, lenders can stay a step ahead and service a previously under-served segment that promises opportunities that can boost revenue lines for the former.