The Problem
American Financial Network, Inc., is a mortgage banking firm based in Brea, California, which operates a network of community-based branches across the United States. The company is a Direct Lender, with the authority to underwrite, fund, and service various loan products, including Fannie Mae, Freddie Mac, FHA, USDA, VA, and Jumbo loans, all in-house. AFN emphasizes operational efficiency and exceptional customer service, focusing on building strong relationships with clients and ensuring a positive experience during the mortgage process.
The primary challenges faced by AFN included a general lack of visibility on certain key workflows in its business. As with most businesses, AFN did not truly understand how certain workflows were functioning and what was the true resource utilization of these workflows. Some of the questions that AFN wanted to answer included questions about employee productivity across teams. Additionally, AFN wanted to discover areas where there was significant manual data transfer between business applications which they felt was causing numerous variations in the invoice handling process. In addition, AFN wanted to identify the largest process bottlenecks, and see where time was being spent on tasks such as reading and writing emails that could potentially be automated with nextgen AI services.
The Solution
AFN deployed ProcessMaker’s Process Intelligence and during the first phase worked with the team at ProcessMaker to undergo a detailed four-week pilot analysis phase. During this analysis, the ProcessMaker Process Intelligence solution was used to evaluate 23 computers. As part of this initial analysis, our solution discovered and analyzed the usage of 132 applications and portals in order to identify areas for improvement. This comprehensive approach enabled the company to tackle operational roadblocks and optimize resource utilization effectively.
Thanks to Process Intelligence, AFN was able to better understand its workforce and how they could possibly better distribute this workforce to improve time and cost metrics. Additionally, AFN used the Process Intelligence solution to identify and investigate automation opportunities for repetitive tasks using Business Process Automation (BPA), Integration Platform as a Service (iPaaS), and Robotic Process Automation (RPA).
The Value Created
One major outcome of the Process Intelligence initiative is the creation of a new effort to standardize the invoice handling process and streamline operations by finding ways to eliminate less efficient variants of this process. Furthermore, Process Intelligence was able to show AFN areas where the business might be able to eliminate costs by eliminating unused software licenses. Additionally, Process Intelligence revealed valuable workload-balancing techniques that can ensure consistent performance. One of the most interesting discoveries and recommendations from Process Intelligence related to GenAI tools. ProcessMaker’s solution can both discover unsanctioned and potentially dangerous usage of GenAI tools as well as suggest areas where GenAI tools can be deployed to automate tasks like email summarization. Overall, the implementation of Process Intelligence has revealed significant ways for AFN to boost productivity and operational effectiveness.
Discovered KPIs and Recommendations made by Process Intelligence:
- Employee Productivity: Discovered the average daily working time on computers (4.23 hours) and potential productivity improvement (~50% or ~$630,000).
- Unused Licenses: 120 underutilized applications were identified which could represent a potential savings of $70,000 annually.
- Automation Potential: 10,000 manual transactions were identified that could be automated, saving ~3,333 hours annually (~2 FTE).
- Process Standardization: Standardize process variants to Improve throughput time by 16.8 hours, saving 7.3 days of annual work time, and reducing 4,461 annual touches.
- Invoice Processing: Reduce bottlenecks and rework in the “List Unassigned Invoices” step, which has a 48% potential for automation.
- Workload Fluctuation: Monitor weekly invoice volumes and processing times to balance workloads.
- Email Management: Reduce the 500 hours spent monthly on reading emails by automating summarization with GenAI tools.
