How a leading UK property and casualty insurer, AEGIS London, with an annual turnover of more than $1 billion, achieved transformative efficiency and underwriting insight by leveraging Fisent BizAI, the industry-leading Applied GenAI Process Automation solution.

The Challenges

Across enterprises, manual, human-dependent tasks are creating significant inefficiencies. These tasks, often involving diverse information, interrupt workflows and divert skilled personnel from core responsibilities and more valuable work. This is a universal problem for information-reliant organizations, where data variations make manual processing cumbersome and prone to error. AI holds a compelling solution to this challenge.

According to Roger Misra, Change Leader at AEGIS London, the rapid advancement of AI prompted the company to seek new capabilities. “We were getting lots of requests from our leadership team to try to add new AI capabilities within our key underlying processes,” he stated.

Like many in the property and casualty insurance industry, AEGIS London faced a critical bottleneck in its policy endorsement process. Processing endorsements, which are mid-year policy changes, previously required significant manual effort from brokers and underwriters. They had to interpret information, determine additional premiums, secure agreement on changes, and manually capture data for downstream systems. The core challenge was the sheer volume, tens of thousands of annual inquiries, which came from insureds and insurance agents, arriving in various formats with inconsistent details, making the workflow laborious and inefficient.

Experienced underwriters must manually analyze inbound inquiries to extract critical information. They then compare these details against existing policies to determine the necessary adjustments, summarizing the required policy amendments. This can involve adding, deleting, modifying, or excluding coverage to address the unique circumstances surrounding each policyholder’s change. This manual workflow was time-consuming, with each endorsement taking an average of five minutes.

AEGIS London correctly expected a GenAI solution should be able to automate a significant portion of endorsement processing. It needed a partner that could not only deliver a solution but also integrate seamlessly with its existing Pega platform and provide a clear path for future efficiency in a highly regulated environment. To address this challenge, the company took on a complex vendor selection process. The company’s comprehensive RFP revealed a wide range of options, from legacy market stalwarts with rigid, pre-trained models to consultants offering generic, platform-based solutions.

Finding Fisent BizAI

After a thorough evaluation, AEGIS London selected Fisent as its partner, viewing the company as the ideal fit. While AEGIS London considered a competitive solution focused solely on the London market, the company believed Fisent would be the better long-term choice due to its demonstrated ability to solve a wide range of complex problems. Roger Misra explained the choice, stating that Fisent is “a highly agile operation with knowledgeable people and proven ability to navigate the corporate governance we require.” The partnership began with an accelerator sprint, a hackathon-like workshop where key AEGIS London stakeholders, including underwriters, developers, and analysts, collaborated to prove the concept.

The team ultimately chose to tackle the policy adjustment endorsement process as the first at-scale manual process to automate. In only six weeks, Fisent BizAI was configured to automate rule application, understand the full rule set underwriters use to categorize endorsements (e.g., early, material, read-only), infer context, and capture the key data points from endorsement documents. The implementation was highly iterative, beginning with just six rules and ultimately expanding to a robust 55 rules. A critical element of the project’s success was a swift feedback loop, which allowed the administrative team to provide direct input on performance, leading to continuous refinement and improvement, and improving accuracy.

Business Outcomes

The implementation of Fisent BizAI yielded significant benefits for AEGIS London, transforming its endorsement process and setting the stage for future innovation.

• Increased Accuracy: Fisent BizAI’s accuracy in categorizing endorsements and extracting data fields improved from around 70% to 98% after just three iterative cycles of refinement.
• Dramatic Time Savings: The time to process a single endorsement was reduced from an average of five minutes to an average of about two minutes. An upcoming API integration is expected to further reduce the average processing time to between 15 and 20 seconds per endorsement. This integration is projected to automate more than 90% of all endorsements.
• Enhanced Underwriter Efficiency: AEGIS London is now able to achieve a quicker and more consistent turnaround time, allowing underwriters to focus on more high-value tasks.
• Improved Transparency: Fisent BizAI transforms unstructured or semi-structured data from documents into a structured format, which provides a better view for reporting and improves market knowledge

Uncovering an Unknown Risk

A critical, unexpected outcome of implementing Fisent BizAI was the discovery that a quarter of all endorsements were going unprocessed. These documents were simply sitting untouched in underwriter mailboxes, creating a lack of transparency and a substantial risk to business continuity.

While the immediate benefit was a reduction in exposure through automated routing, the long-term impact on AEGIS London’s risk posture is even greater. Fisent BizAI is now providing a rationale for all of its analyses. This new, data-driven insight helps the company better understand and manage its exposures, creating a more robust and transparent process than was previously possible. 

More Human-Dependent Tasks to Automate

Fisent’s agentic solution can be applied to a multitude of at-scale manual tasks. Most organizations typically find two to three dozen of these automation gaps within their operations.

For AEGIS London, the success of the policy adjustment endorsement automation effort has led to the implementation of Fisent BizAI throughout its operation, including document ingestion for new insurance quotes and the creation of risk summaries. AEGIS London views its relationship with Fisent as a long-term partnership, with the goal of implementing three to four new use cases per year.

According to Roger Misra, the return on investment for Fisent BizAI was evident, as it not only improved a single process but also provided a robust solution for ongoing digital transformation. He further noted that there were numerous potential applications for Fisent BizAI cases.

Key Takeaways

AEGIS London provides a clear example of how a major insurer, through its adoption of Fisent’s BizAI solution, transformed a cumbersome manual process into a highly efficient and accurate automated workflow. Key takeaways include:

• Fisent BizAI achieved a 98% accuracy rate in data extraction and categorization, ensuring a high level of reliability.
• Endorsement processing time was dramatically reduced, from an average of 5 minutes to about two minutes, with the aim of driving that number down to 15-20 seconds per endorsement.
• Underwriters are freed from repetitive tasks, allowing them to focus on more high-value work.
• The Fisent BizAI implementation identified that 25% of all endorsements were going unprocessed, eliminating a significant risk.

Ultimately, the success of Fisent BizAI proves the power of Applied GenAI Process Automation to drive transformative business outcomes and provides AEGIS London with a scalable solution for future digital transformation.

Orco Group, a bank holding company, leveraged Applied GenAI Process Automation to classify and process a massive influx of physical documents, expediting the integration of a strategic acquisition in their market. Fisent BizAI significantly improved process efficiency, reduced costs, and enhanced customer service while harnessing GenAI to modernize how data was integrated into the growing bank group’s digital systems.

The Challenges

Orco Bank’s acquisition of CIBC FirstCaribbean presented the group’s technology team, based at Aruba Bank, with a considerable challenge: how to efficiently integrate a massive influx of information on physical documents from the newly acquired bank into their core digital systems. The sheer volume of paper-based customer files, loan agreements, and collateral documents demanded a solution beyond traditional manual processing.

Before deploying Fisent’s BizAI in 2024, which was the IT team’s first major foray into AI, the time-consuming tasks of digitizing, renaming, and categorizing documents were already unmanageable.

Adding to the problem, these types of projects have fixed deadlines. Once a deadline is set, with agreements from all parties, including regulatory bodies, there is no flexibility. The team required a solution capable of rapidly processing and ingesting the hundreds of thousands of documents it received. What’s more, on the island-nation of Aruba, physical storage of these documents represented a significant expense, encompassing rent, compliance, and outsourcing costs.

A timely introduction to Fisent BizAI changed everything. A trusted advisor suggested that Fisent’s Applied GenAI Process Automation technology could automate much of the manual work otherwise required of the bank’s staff.

The Fisent BizAI Solution

Fisent utilizes GenAI to automate common, human-dependent tasks. By bridging the enterprise application layer with the evolving LLM ecosystem, Fisent’s Applied GenAI Process Automation solution, BizAI, automates time-consuming business processes like complex contract analysis, new customer onboarding, customer request resolution, and purchase order fulfillment. BizAI enables businesses to automate content interpretation, make informed decisions, and streamline execution by processing diverse data types, including unstructured, multi-language, and multimodal content.

In its first meeting with Fisent, the bank’s team reviewed its existing document processing procedures, noting their reliance on increasing staff to manage volume on deadline. Fisent provided a demonstration of BizAI, showcasing its potential to automate the bank’s manual processes for this specific use case.

After seeing the BizAI demo, it became clear that this technology could be, as Nigel Wix, Aruba Bank’s IT Innovation and Technology Manager, put it, “the ultimate answer” to address their document processing dilemmas. This realization sealed the decision to proceed with the project.

The implementation, led by Fisent, proceeded smoothly. “No major overhauls were needed to accommodate the BizAI solution. It integrated well with our existing infrastructure with minimal disruption,” reports Wix. Fisent rapidly equipped the team with the knowledge needed to utilize BizAI, demonstrating how the platform leverages GenAI and integrates seamlessly with the banks’ existing enterprise systems. This focused training enabled the team to become proficient in the specific use case within just a few hours.

Business Outcomes

Error Reduction, Cost Savings, and Reduced Processing Times

The adoption of Fisent BizAI resulted in a 90% decrease in errors compared to previous manual processes. This positive impact was further complemented by a significant increase in processing speed, reducing document processing time by more than 70%. This speed was demonstrated by BizAI’s ability to process as many as 10,000 unstructured documents per day. Faster processing by BizAI meant ingesting information more quickly, which in turn curtailed physical storage costs.

Improved Customer Experience

With troves of digitized and structured data now integrated into its system, the bank can now avoid asking customers for documents it already possesses, streamlining processes like file reviews and loan applications. This is enabling proactive customer service, such as notifying customers of upcoming loan expirations with pre-approved offers.

Addressing Legacy Data

The success of BizAI prompted the IT team to consider applying BizAI to the substantial legacy archive of paper files across the Orco Group, leading to as many as six new project requests from different departments. BizAI addressed not just the immediate post-acquisition document challenges, but also a long-standing issue of managing existing physical legacy data.

While Orco Group was anxious to tackle its paper documentation challenge, there are dozens of hidden manual processes that financial institutions are leveraging Applied GenAI Process Automation to address, including complex contract analysis, new customer onboarding, customer request resolution, and bank statement processing.

Key Takeaways

Adopting Fisent BizAI marked a significant shift from a heavily manual approach to a sustainable, automated solution. It demonstrated the potential for ongoing use in daily operations and helped overcome internal wariness related to moving away from physical documents, facilitating the transition to a fully digital environment. BizAI is now considered crucial for future acquisitions by Orco Group and its member banks, streamlining the integration of acquired banks’ document management systems and supporting growth aspirations.

Wix says his leadership team embraced AI adoption responsibly and carefully. “This initial use case was well-controlled, focusing on back-office operations without direct customer interaction, allowing us to learn what works best,” Wix adds. Ultimately, the Fisent approach built confidence in well-applied GenAI technology among Orco Group leaders, managers, and front-line teams.

Looking ahead, Orco Group plans to expand BizAI’s use to process legacy documents, recognizing the need to integrate hundreds of years of accumulated data. Orco Group also envisions using BizAI for additional use cases such as improved customer onboarding and other customer-focused processes.