{"id":6997,"date":"2022-05-07T17:33:25","date_gmt":"2022-05-07T17:33:25","guid":{"rendered":"https:\/\/prolifics.com\/us\/?p=6997"},"modified":"2025-09-17T21:39:03","modified_gmt":"2025-09-17T16:09:03","slug":"bots-help-mortgage-lender-keep-customers-and-employees-happy","status":"publish","type":"post","link":"https:\/\/prolifics.com\/usa\/resource-center\/case-studies\/bots-help-mortgage-lender-keep-customers-and-employees-happy","title":{"rendered":"BOTs Help Mortgage Lender Keep Customers and Employees Happy"},"content":{"rendered":"
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A Midwest credit union (MWCU) providing a full line of financial service offerings to its members. Prolifics has worked with MWCU since 2014.<\/p>\n

Our Client<\/cite><\/p><\/blockquote>\n

Challenge<\/h4>\n

The pandemic and subsequent shut down in March 2020 sent mortgage rates tumbling. This gave homeowners with mortgages the opportunity to refinance at a lower rate. Financial services companies, including MWCU, were inundated with \u201crefi\u201d applications. This influx exposed and exacerbated weaknesses in MWCU\u2019s loan processes. Many of MWCU\u2019s workflows involved manual processes, which were nearly impossible to scale for volume.<\/p>\n

In many situations a set process didn\u2019t exist for a task, it was accomplished based on an employee\u2019s own knowledge and experience. Data verification, for example, required MWCU personnel to go and log into multiple systems manually. They had to send multiple emails, and they had to scan and fax multiple documents to different agencies. It got to the point that MWCU was not completing mortgages on time. The situation negatively affected employee moral as well as key performance indicators (KPIs).<\/p>\n

Action<\/h4>\n

Prolifics started with a process discovery workshop and process mapping. We engaged MWCU personnel and created a process model for them, focusing on their major pain points and challenges. Based on those findings, we recommended an implementation to robotic process automation (RPA), better known as bots, and produced the \u201cto-be\u201d process model showing what it would look like after RPA. This raised a challenge in itself \u2013 overcoming the concerns of MWCU. The credit union had two main bot issues, which we helped them overcome:<\/p>\n

(1) MWCU was concerned whether the automated bots could actually perform what their employees were doing. The concern was partially in response to many of the tasks not being documented. How could we automate a person\u2019s work without involving the person. Once we implemented our workshop and subsequent process mapping, and they were able to see the bots in operation and the improvements that started, the client was on board.<\/p>\n

2) MWCU was concerned about the reaction of their employees to bots \u2013 would it generate the fear of being completely replaced by automation? We demonstrated that when we automate a business operation or a business process, we are actually augmenting your knowledge worker or your business worker with the automation bots. The automation bots aren\u2019t performing everything in a process from end to end. They\u2019re developed to perform the tasks which are mundane and repetitive in nature, so that now your business workers can focus on much more intelligent work.<\/p>\n

Based on the segmentation of our process mapping, we created seven primary bots (larger applications) and 18 helper bots (smaller applications). These unattended bots go through the application packages in the loan origination system and perform various validating and verification tasks, as well as generating certain documents.<\/p>\n

To help MWCU further, we added hyperautomation (or intelligent automation) on top of the RPA, which means a layer of artificial intelligence \/ machine learning (AI\/ML). AI\/ML techniques look at large amounts of data, make connections, and generate insights better and quicker than a knowledge worker ever could. AI\/ML can analyze the entire historical mortgage portfolio on an ongoing basis, and recognize, for example, that a certain combination of factors led to a much higher risk of default. If a current, otherwise qualified loan application has those factors, the AI-powered bot will flag \u2013 if not outright deny \u2013 the loan application.<\/p>\n

Results<\/h4>\n

When MWCU saw the results, the return on investment (ROI), and what they could now do with their freed-up workers, they were very happy. According to MWCU, the bots:<\/p>\n