Loan Rejection Rates Digging into Revenues? Dig Deep into Data

Posted by Paul Greenwood on May 4, 2018

Paul GTWWe're pleased to present this guest blog by Paul Greenwood, CEO and Co-founder of our strategic partner, GDS Link, a provider of technology solutions, analytical and consulting services for the modern lender.

Banks and credit unions have an opportunity to leverage technology to broaden and deepen their pool of potential borrowers. However, they also face considerable financial strain as they work to incorporate effective risk management and keep up with innovation that is happening at a blistering pace. All of this pressure is happening at a time when loan rejection rates are high.

The straining subprime lending market

A study from TransUnion analyzing big-picture trends across the industry found that loan origination rates are declining in a variety of segments. This is particularly evident in the sub-prime lending segment, where originations have declined consistently, similar to trends taking place shortly after the Great Recession. At that time, lenders were slowing on subprime loans to control delinquency. Now, many lenders are revisiting that strategy.

This trend is resulting in a reduction in originations and, subsequently, increased rejection rates. Banks and credit unions that can identify opportunities to reach underserved borrowers can increase their profits. However, managing risk is paramount. Firms that can use risk analytics to identify safe subprime borrowers can expand their customer base.

GDS Link is positioning organizations to take advantage of new prospective borrowers through a full risk analytics suite. Our partnership with Accelitas brings this to another level, as the AI and machine learning service provider can empower firms to identify rejected applications that may benefit from reconsideration.

Machine learning can take risk analytics to another level.

Machine learning can take risk analytics to another level.

Using analytics to reduce rejections

Typically, a bank or credit union will be forced to depend on credit scorecards or similar decisioning tools to identify if borrowers' credit histories indicate they are viable candidates for a loan. This often leads to a quick rejection for subprime borrowers unless a firm is actively willing to take on a bit of risk.

Big data strategies can turn some of these rejections into accepted applications. Analytics tools will bring together data from a wider range of sources. Information found on social media and similar unstructured data sources can be used to gain a wider perspective on a borrower. However, processing and analyzing this data can be time-consuming and labor-intensive.

At GDS Link, we've introduced automation and decisioning tools into our platform to ease some of these burdens, and our partnership with Accelitas makes it even easier to leverage more data during lending.

Accelitas offers its Lift solution within our DataView360 platform, building AI and machine learning into our analytics systems. This allows for automated analysis in which rejected applications are parsed and revisited. In response, firms can accept loans that would have been rejected without taking on risk.

Want to learn more about this process?

Check out our webinar with Accelitas for an in-depth look at how AI-powered analytics can empower your firm to reduce loan rejections and gain access to a new customer base.

You can also read more about our partnership with Accelitas here, which includes ID verification services for online and mobile banking that have resulted in ROI ratios of 30:1 and higher.

Tags: lending, AI Lift, credit risk management, Artificial Intelligence, GDS Link

 

 

 

 

 

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