Banks and other financial institutions have been using automated underwriting systems for some time now. They offer an instrumental means of automating the checking process for loan applications. While improvements in speed and accuracy are the most obvious benefits, this is just the tip of the iceberg.
The loan automation system used by many banks today incorporates both Cognitive Process Automation (CPA) and Robotic Process Automation (RPA). The RPA element introduces a structured approach to data analysis, while the CPA introduces artificial intelligence (AI) with a particular focus on machine learning. The result is automated loan processing which has been shown to improve customer experience significantly.
For example, automated mortgage processing has proven highly beneficial to the home buyer, saving time and money by creating an automated mortgage underwriting system. This ensures that each mortgage application can be processed and considered much quicker. Consequently, the historic loan decision process is reduced significantly.
See also: Cognitive solutions in business efficiency and performance
Whether applying for Fannie Mae or FHA loans, mortgage process automation has a significant impact on the industry. On the surface, investment in real estate appears relatively simple, but the loans approval process can be complex. We will now look at the many areas of the lending process in which loan automation software can assist.
Automated underwriting software can assist from start to finish with loan applications. Digital Coworkers are a great example of this kind of underwriting automation. The initial "training" process means that this underwriting software is in tune with the specific systems of individual clients. The use of AI ensures that the software continues to learn going forward, enhancing and improving value for money. Once the application is received, the form is opened, and the loan origination process begins.
When applying for a mortgage loan, the information on the application form is reviewed by the automated underwriting system. Optical Character Recognition (OCR) technology and AI allow the software to extract data from structured and unstructured documents. It can also decipher the most challenging of handwriting!
All of the data required is then transferred to the internal computer system, where the automated loan underwriting process starts in earnest. Automated underwriting software has a 95% straight-through processing rate and is four to eight times quicker than a human worker. At this point, the software will also be able to identify missing documents, such as tax returns and pay stubs, and seek clarity where there is confusion. It will even contact the relevant third party.
Assuming that all of the information is available, the underwriting software will move on to the next part of the process.
The process of pulling and running credit reports is relatively simple but time-consuming when done manually. As part of the automated underwriting system, credit reports will be accessed, and the information analysed. The software will create a report and, using predefined measurements, either flag any issues or approve this stage of the application.
See also: The pros and cons of automated underwriting systems
While traditional fraud detection is part of the best automated underwriting platform, there is an additional Office of Foreign Assets Control (OFAC) check. Fraud detection has been a massive problem with the manual mortgage process, but these automated systems are making huge inroads. They can compare and contrast different financial measurements, client data and review information held on credit files.
Whatever type of finance you are looking for, time is often essential, and a quick decision is appreciated. The automated underwriting approval process is speedy and accurate. Consequently, where there are obvious reasons to decline an application, the client would be informed as soon as possible. This may be via an automated communication, or the recommendation is passed to the underwriter.
There will be occasions where approval of a loan may be borderline, requiring additional input from the underwriter. If this is the case, all of the information available will be presented in an easy to read format for further consideration. In reality, the underwriter will have limited, if any, involvement in the initial loan processing procedure. In an instant, this can create significant additional capacity allowing the financial institution to seek further loan business.
As we have covered above, there are specific benefits when using an automated underwriting system. The more general benefits include:
Automated underwriting software will neither get tired nor lose focus when trawling through mundane figures on a loan application form. This process is also highly efficient using pre-set conditions, algorithms, and an element of artificial intelligence/machine learning.
There is a balance between speed and accuracy, although thankfully, this is not an issue with AI fuelled software bots. As we touched on above, this type of software is anywhere between four times and eight times faster than a human worker.
At best, manual input by a human worker will be up to 90% accurate, though studies show that humans very rarely perform this well. Even when considering the vast reduction in processing times, automated underwriting systems have a 95% straight-through processing rate, and an even higher accuracy rate. This degree of accuracy reduces the need for checking, therefore saves more time, money, and effort.
There is no real comparison when it comes to software and manual processing times. The ability to come to a decision in a fraction of the time taken with the manual process reduces customer stress and improves customer satisfaction. The financial industry is heavily reliant on the trust factor between providers and customers. Therefore, any improvement in customer satisfaction can only be beneficial in the long term.
Automated underwriting has been used for some time, although it is only lately that AI and machine learning have been brought into play. This not only significantly reduces processing times but has also led to greater accuracy and increased automation. This is before we even consider the concept of AI/machine learning and the ability to increase efficiency as we advance.
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