Data analytics and automation: Making lenders more competitive, lowering risk

© Can Stock Photo / DamienGeso

Auto-decisioning is the ability to configure business rules and policies through a reliable and streamlined technology process to automatically approve or reject a car loan application. This is the whole basis of a loan origination system (LOS), which should support this capability through a flexible, intuitive platform.

But not all LOS technologies are alike. Lenders should look for an origination solution that uses artificial intelligence (AI) to generate data analytics to both accelerate and bolster the auto-decisioning process. By mining alternative data points — such as utilities, rent and other credit histories — an LOS can better score an applicant’s current financial status and the factors that might affect his or her ability to repay a loan. This use of data analytics has been proven to increase accuracy, making it as reliable as traditional applicant screening techniques — if not more so — and it can deliver extraordinary insights to lenders.

Using data analytics to build credit scorecards

Lenders traditionally create credit scorecards to assess the risk level of a loan applicant based upon a statistical model. The applicant’s personal details and historical finances are analyzed to predict future behavior. Examples of traditional data include credit bureau reporting, past payment performance, history of loans and leases and pay stubs.

Importantly, lenders should have valid data coming from integrated, third-party decision engines incorporating external sources. Data and an LOS system alone aren’t the total answer in creating a robust scorecard for auto-decisioning. Data, effective LOS and input from decision engines in combination can create a significant differentiation, to provide the overall “smart experience” the market demands.

Whether a lender builds their own scorecard or integrates with a specialized provider, a sophisticated LOS should empower users to update their decisioning matrices with alternate data quickly, easily and without the need for IT or developer support. Examples of alternative data that can be ascertained via automation and machine learning include transactional histories, social scores relative to online purchasing behaviors, employment status, income verification and more.

Powerful AI tools can search a variety of online sources for know-your-customer (KYC) data, which provide immediate and dependable background on each applicant. Underwriters should not only be able to push their own data into their decision scorecard but also leverage additional resources from alternative sources or machine learning models to better represent overlooked subprime or credit-invisible borrowers.

Some of the more advanced systems on the market offer lenders the ability to implement new approval parameters on the fly, such as adjusting for regional economic challenges, or even a pandemic. This capability gives lenders greater agility, allowing them to quickly react to market changes and adopt new business lines and workflows as needed.

By leveraging multiple data sources gathered through intelligent automation, deals can be approved more quickly for a wider range of borrowers with less risk. This allows lenders to compete based on speed while better insuring the viability of the loan.

Automated closed-loop analytics

A closed-loop data analytics approach can boost scorecard performance and give valuable insight into how to adjust and continuously improve scorecard rules. It functions as a decision tree with key data points.

The more data that passes through the system, the more efficient a lender’s scorecard process — and its performance — will become over time. To leverage alternative data and closed-loop efficiencies, LOS technology should seamlessly integrate new or alternative data points. Sophisticated AI can almost instantaneously identify these alternative datapoints garnered from existing online information and incorporate them into the LOS, enriching the system’s knowledge of the applicant’s financial behaviors.

Intelligent systems that incorporate enriched data without human intervention creates a more expedient lending process. The more the LOS can automate processes, the more it can deliver an effective, seamless experience that delivers a competitive edge to the lender.

Vlad Kovacevic is the founder and chief executive of Inovatec Systems. Inovatec provides modern and innovative LOS, LMS and Direct systems that eliminate friction in the lending process and automate much of the manual work of originating and managing loans.

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