Data & Analytics

Benefits of digital verification on the lending journey

Experian recently acquired a minority stake in Finicity, a leading financial data aggregator enabling innovation in the FinTech industry through its modern RESTful API and Finicity Aggregation Platform. Steve Smith—chairman, CEO and co-founder of Finicity—has a passion and experience in developing innovative and disruptive technology, products and services that leads to efficiency for markets and, ultimately, improvements for consumers. Here he shares his thoughts about disruptive technology in the lending space and its benefits to lenders and consumers. Q: Finicity has said its objective is to take a loan application approval from weeks to minutes using its technology. That sounds pretty great, but how is that possible? How does this play out behind the scenes? A: Well, we’re living in a world where we, as consumers, expect very user-friendly experiences and we expect things to happen at digital speeds. The loan process is no exception. To deliver the experience consumers are expecting requires us to leverage the technology trends of digitization, mobility and big data. Finicity plays a foundational role by leveraging thousands of digital connections across financial institutions to aggregate consumer-permissioned account data. Once we have this data, we’re able to deliver real-time insights into an individual's financial health. This financial health assessment includes income and assets, two critical components to the loan approval process. All that’s required is the borrower to permission use of the data. Once that’s done, we’re able to gather all appropriate data across multiple accounts, rapidly analyze it and send a verification report to the lender. No papers. No multiple requests. No questions on the validity of the data. All done in minutes, not weeks. Q: This is very disruptive technology. What are the benefits for lenders? Consumers? A: Well, as we discussed, one of the major benefits is the speed to a loan. Furthermore, this reduces cost for the lender by maximizing loan officer’s time, while also freeing up loan capital as they can move through loans more quickly with a higher quality assessment. Another benefit for lenders is reduced fraud. Our information on income and assets is coming from real-time bank validated information. This eliminates the possibility of altered data. For consumers, it’s a dramatically simplified process. No need to chase down multiple documents. There are virtually no second requests for information, which we often see in the process. And they’re always in control of their information. All in all, it’s a dramatically better experience for both the lender and the borrower. Q: What sets this solution apart from others in the market? A: A few things set Finicity apart in delivering the quality of insights required. First, we are an industry leader in the number of financial institutions we connect with, ensuring broader access for more customers. Second, 95 percent of our integrations provide access to formatted data, something that’s critical to credit decisioning solutions. In these cases, we’re not screen scraping. This enhances our ability to collect bank validated transactions; we provide the financial institution transaction ID. This provides assurance of data quality. Finally, is our ability to categorize and analyze the transactions. This allows us to identify income streams and assets. Through this process, we’re also able to flag unusual transactions, like large deposits, that may skew actual assets. Q: The future of financial technology is still evolving. What lies ahead? A: We’re very excited about the future of financial technology and the impact that aggregation will have. Whether it’s financial management, digital payments or credit decisioning, real-time data will improve the experiences and the outcomes. As we’re talking about lending, this is one of the spaces that could see significant disruption. Our ability to generate a richer view of an individual’s or organization’s financial health will more accurately determine their ability to repay a loan. This will be a great benefit for those that have thin file or no credit history. We see a world where suitability for a loan will be driven by their actual financial life independent of their use of credit. One of the largest markets in the US is millennials. However, for consumers under 30, two-thirds have subprime or non-prime credit scores and one-third of millennials don't have any credit history. This is just one group underserved because legacy models don’t leverage the full extent of data available. Q: Is there anything else you can tell us about Finicity and its role changing customer experiences across financial service? A: For us, it all comes down to one thing: enabling individuals and organizations to have the information and insights they need to make smarter financial decisions. The data is there. We’re helping to unlock the potential of that data by working with innovative partners like Experian. To learn more about Experian and Finicity's account aggregation solutions, visit www.experian.com/finicity

Published: March 20, 2017 by Guest Contributor
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Auto loan delinquencies extending beyond subprime consumers

There has been a lot of discussion around the auto loan market regarding delinquency rates in the past year. It is a topic Experian is asked about frequently from clients in regard to what particular economic market behaviors mean for the overall consumer lending. To understand this issue more clearly, I ran a deeper dive on the data from our Q3 Experian-Oliver Wyman Market Intelligence report. There are some interesting, and perhaps concerning, trends in the data for automotive loans and leases. Want Insights on the latest consumer credit trends? Register for our 2016 year-end review webinar. Register now Auto loan delinquency rates are at their highest mark since 2008 The findings indicate that the performance of the most recent loans opened from Q4 2015 are now performing as poorly as the loans from the credit crisis back in 2008. In fact, you have to go back to 2008, and in some cases, 2007, to see loan default rates as poorly as the Q4 2015 auto loans originated in the last year. Below we have the auto loan vintage performance for loans originated in Q4 of the last 8 years — going back to 2008. The lines on the chart each represent 60 days late or more (60+) delinquency rates over specific time period grades. For these charts, I analyzed the first three, six, and nine months from the loan origination date. As you can see, the rates of delinquency have steadily increased in recent years, with the increase in the Q4 2015 loans opened equaling or even surpassing 2008 levels. The above chart reflects all credit grades, so one might think that this change is a result of the change in the credit origination mix. By digging a little deeper into the data, we can control for the VantageScore® credit score at the loan opening, or origination date, and review performance by looking at two different score segments separately. Is there concern for Superprime and Prime consumers auto loans? In the chart immediately below, the same analysis as above has been conducted, but only for trades originated by Superprime and Prime consumers at the time of origination. You can see that although the trend is not as pronounced as when all grades are considered, even these tiers of consumers are showing significant increases in their 60+ days past due (DPD) rates in recent vintages. Separately, looking at the Subprime and Deep Subprime segments, you can really see the dramatic changes that have occurred in the performance of recent auto vintages. Holding score segments constant, the data indicates a rate of credit deterioration in the Subprime and Deep Subprime segments that we have not observed since at least 2008 — back to when we started tracking this data. What’s concerning here is not only the absolute values of the vintage delinquencies but also the trend, which is moving upward for all three time periods. Where does the risk fall? Now that we see the evidence of the deterioration of credit performance across the credit spectrum, one might ask – who is bearing the risk in these recent vintages? Taking a closer look at the chart below, you can see the significant increase in the volumes of loans across lender type, but particularly interesting to me is the increase in 2016 for the Captive Auto lenders and Credit Unions, who are hitting highs in their lending volumes in recent quarters. If the above trend holds and the trajectory continues, this suggests exposure issues for those lenders with higher volumes in recent months. What does this mean for your business? Speak to Experian's global consulting practice to learn more. Learn more Just to be thorough, let's continue and look at the relative amounts of loans going to the different score segments by each of the lender types. Comparing the lender type and the score segments (below) reveals that finance lenders have a greater than average exposure to the Subprime and Deep Subprime segments. To summarize, although auto lending has recently been viewed as a segment where loan performance is good, relative to historical levels, I believe, the above data signals a striking change in that perspective. Recent loan performance has weakened to a point where comparing the 2008 vintage with 2015 vintage, one might not be able to distinguish between the two. // <![CDATA[ var elems={'winWidth':window.innerWidth,'winTol':600,'rotTol':800,'hgtTol':1500}, updRes=function(){var xAxislabelSize=function(){if(elems.winWidth<elems.winTol){return'12px'}else{return'14px'}},xAxislabelRotation=function(){if(elems.winWidth<elems.rotTol){return-90}else{return 0}},seriesLabelSize=function(){if(elems.winWidth<elems.winTol){return'12px'}else{return'16px'}},legenLabelSize=function(){if(elems.winWidth<elems.winTol){return'12px'}else{return'16px'}},chartHeight=function(){if(elems.winWidth<elems.rotTol){return 600}else{return 400}},labelInside=function(){if(elems.winWidth<elems.rotTol){return false}else{return true}},chartStack=function(){if(elems.winWidth<elems.rotTol){return null}else{return'normal'}};this.sourceRef=function(){return['Source: Experian.com']};this.seriesColor=function(){return['#982881','#0d6eb6','#26478D','#d72b80','#575756','#b02383']};this.chartFontFamily=function(){return'"Roboto",Helvetica,Arial,sans-serif'};this.xAxislabelSize=function(){return xAxislabelSize()};this.xAxislabelOverflow=function(){return'none'};this.xAxislabelRotation=function(){return xAxislabelRotation()};this.seriesLabelSize=function(){return seriesLabelSize()};this.legenLabelSize=function(){return legenLabelSize()};this.chartHeight=function(){return chartHeight()};this.labelInside=function(){return labelInside()};this.chartStack=function(){return chartStack()}}(), updY=function(chart){var points=chart.series[0].points;for(var i=0;i elems.rotTol){if(thisWidth<20){var y=points[i].dataLabel.y;y-=10;points[i].dataLabel.css({color:'#575756'}).attr({y:y-thisWidth})}}}},updX=function(chart){var points=chart.series[0].points;for(var i=0;i elems.rotTol){if(thisWidth

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