In late January, Madison Advisors published the results of its study of the “capture service” industry, which industry encompasses inbound document processing, which Exela provides through its Digital Mailroom and related solutions.
And guess what? Exela is named as one of the top four leaders in the capture service industry.
Madison Advisors’ study was designed to cull information on industry trends, including the ways in which the industry is making use of, and planning to make use of, RPA (robotic process automation) and cognitive automation. Here are some of the highlights of Madison Advisor’s report on Exela:
Exela’s solutions reflect insight gained from decades in the business
Exela’s expertise in inbound document processing has been culled over three decades of experience in the business through its predecessor companies which came together to form Exela in 2017. Please take a moment to get to know Exela’s fascinating three-decades history here. One thing that differentiates Exela from its competitors is that it has been in the business during every step of its evolution to where it is today. In other words, we understand the back-story because we helped drive it.
Exela’s solutions feature novel, proprietary document classification processes
Exela’s robust inbound digital mailroom processes are augmented by highly configurable tools that automate the routing of images to various workflow tools based on their document classification, which eliminates manual presorting and separators for increased speed and accuracy. In addition, the process generates customized user access reports for audit and accountability purposes and encrypts data and images as needed for added security.
Exela’s processes reflect a keen understanding of best AI practices
Exela’s approach to exception management (which leverages both AI and human ingenuity by routing exceptions to industry-specific subject matter experts on Exela’s team for review and clearance) reflects Exela’s extensive experience in both labor arbitrage and digital transformation. “Our rules and bots are more effective because of our understanding of our customers’ business processes, which can only come from our extensive experience,” explains Saleem Ahmed, Exela’s Vice President Business Strategy.
Exela’s solutions feature next-generation content analysis and data-mining
Exela’s solutions deal with unstructured data with next-generation tools for natural language processing (NLP), including:
- JET
JET, which was developed by a dedicated team of data scientists and subject matter experts, utilizes machine learning, natural language processing techniques and a set of statistical rules to automate extraction and conversion of metadata elements from any kind of content.
- BoxOffice
After JET’s processing is completed, the extracted metadata and corresponding document are routed to Exela’s Enterprise Information Management solution, BoxOffice. BoxOffice is a next-generation workflow platform, going beyond traditional document capture and extraction to include enhanced content analysis and text mining.
Unprecedented synergies
Exela is uniquely positioned to provide synergies across business operations through global delivery that streamlines operations and enhances quality, experience and cost structure.
Extraordinary information management
Through its reporting tool, Athena, Exela’s Digital Mailroom offers a virtual window into processing operations by providing configurable dashboards with dynamic drill-down capability, delivering real-time visibility and alerts with regard to mission-critical data. Dashboard reporting includes, but is not limited to, daily reports identifying status and age of inventory for all identified work items and volume of work received and processed each day.
“As the capture service market continues to evolve,” notes Madison Advisors in its conclusion, “we expect to see new offerings that will impact inbound mail processing positively, offering advanced cognitive technologies that have the capability to extract data from viewed images via security cameras using machine-learning techniques currently applied to document processing.”
