Underwriting automation

I. Introduction to underwriting automation

Underwriting automation is the combination of traditional methods for assessing an applicant’s creditworthiness paired with modern software, data integrations, and predictive models. The aim of automated underwriting is not only to underwrite accounts more efficiently, but also to provide faster access to credit and more predictive measurements of borrower behavior.

II. How automated underwriting works

Automated underwriting fits into the broader origination process, using information provided on an application (like the potential borrower’s name, income, or SSN) to find other relevant data and plug it into predictive models and algorithms for credit assessment. The output from the process is a decision, either a simple yes or no, or a more nuanced set of approved terms or products.

Some credit providers can fully automate their underwriting process, allowing a borrower to go from application to approval and funding without any manual effort from agents. Others automate only a portion of the process, using technology to gather data and streamline calculations, but reserving human judgement for edge cases or final approvals.

Key processes and technologies for underwriting automation

Automated underwriting is made up of several steps, each of which may involve specialized technologies. Often, these tools come bundled together as part of a loan origination system (LOS) or loan management system (LMS), but some credit providers may use individual vendors or software tools and connect them together into a cohesive process.

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Here’s a breakdown of each stage of the process and the automating tools involved:

• Front-end applications. As a borrower fills out information on a webpage or smartphone app, that data can instantly be used for identify verification, credit checks, and other underwriting tasks. As each page of an application is completed, the application system communicates with third-party data vendors and the credit provider’s decision engine. By the time the applicant clicks submit, most of their data has already been processed.
• Verification tools. When it comes to financial services, it’s best to follow the old proverb trust but verify. Credit providers can leverage data from third-party vendors to verify a borrower’s identity, preventing identity theft or money laundering and satisfying due diligence requirements.
• Credit history tools.  as well as their financial information. Credit scores are the industry’s baseline, but tens of millions of adults in the U.S. aren’t on record with any of the three major credit bureaus. 
• Decision engines. Once a borrower’s data has been compiled, a decision engine plugs it into one or more algorithms and predictive models to assess whether the applicant is likely to repay their debt on time and in full. More advanced decision engines can deliver nuanced outputs, like recommended terms or best-fit products.

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III. Benefits for borrowers and providers

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Automating the underwriting process offers several major benefits, not only to the credit provider, but also the applicants themselves. Neither party has a vested interest in waiting for repetitive manual processes, and costly inefficiencies inevitably get passed down to consumers. Whether the credit provider aims to improve their customer experience or streamline their own internal processes, the net effect is often the same.

• Speed. Where a fully manual underwriting process might take days or even weeks, automations can run even as the borrower fills out their application, meaning they’re approved almost as soon as they click submit. Borrowers get faster access to capital, and consequently have a much smaller likelihood of abandoning an application.
• Cost efficiency. Cutting out tedious manual processes improves the margins on each account. With greater savings, the credit provider is able to reinvest that money in other improvements or offer more competitive interest rates.
• Greater opportunities. Manual underwriting is expensive, and it may not be financially viable to take a second look at any applicants who don’t immediately qualify. But with inexpensive automations, credit providers can reassess those applicants, either pulling alternate data sources to vet their creditworthiness or running multiple calculations for less risky credit products. Applicants get more financing options, and creditors don’t miss out on their business.
• Scalability. The benefits of speed, efficiency, and credit opportunities all result in greater scalability for the credit provider. The benefit to the provider is obvious, but repeat customers also benefit from a reliable and continually improving financial services provider.

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Each of these benefits can improve a credit organization’s outlook, and should factor into the ROI calculations for any origination system.

IV. Challenges in underwriting and opportunities in automation 

Many of the difficulties in underwriting emerge from limited resources and manpower. But modern technology and automation tools can enhance an underwriting team’s accuracy, efficiency, and scalability, helping overcome many of the major obstacles they face.

Thin files and integrated data

Tens of millions of Americans have no credit history on record with any of the three major credit bureaus, and an even greater number have thin files that only reflect a fraction of their financial history. Traditional underwriting systems lack the tools to look deeper, so they simply deem these applicants as too risky and reject them outright. That leaves applicants with fewer financing options, and providers with less business.

Integrating third-party data sources into underwriting and origination flow can help paint a more comprehensive picture of applicant’s financial behavior and credit history. Borrowers with a robust history might be approved on the spot, and then additional underwriting with alternative data sources sheds a brighter light on borrowers who might not qualify at first glance. Even with a thin file or low score, some applicants might demonstrate ability to repay through strong income or other metrics.

Wasted effort and knock-out rules

Poorly implemented automation undercuts the potential savings when it makes unnecessary use of data and underwriting tools. If an applicant lives in an area that a provider doesn’t service, there’s no point in paying for credit scores, identity verification, or other data.

Knock-out rules consider an applicant’s eligibility at each step of the underwriting process. If their self-reported data like age, location, or income don’t align with the products they’re applying for, then the system can stop them before they even finish an application, perhaps referring them to a partner who’s a better fit.  Similar logic runs before each new data pull or analysis, making sure that the data already gathered doesn’t rule them out.

Missed opportunities and product matchmaking

Binary credit decisions leave applicants with fewer options and credit providers with fewer customers, even if they only fell a few points outside the acceptable range.

Advanced decision engines can use product matchmaking to deliver more personalized results. Credit providers can mitigate risk and keep the applicant’s business by extending an offer with a slightly lower credit limit or higher interest rate, or recommending a credit builder as a stepping-stone toward the products they originally applied for.

V. Automating your underwriting operation

Automated underwriting poses major benefits and cost savings to credit providers, but properly implementing it isn’t always easy.

If you’re looking to upgrade your underwriting, reach out to us. LoanPro’s origination suite comes equipped with tools to streamline underwriting and decisioning, whether through our native toolkit or through integrating your own preferred tools. We’d love to discuss your process and see if LoanPro is the right fit for you.