Large Insurer Utilizes AutoPilot to Ensure Quick Claims Processing
Helps Retain Customers in an Increasingly Competitive Service-Oriented Environment
The insurer profiled in this case study is one the nation’s leaders in healthcare, property and casualty and disability insurance. Its mission is to help people lead healthier lives, make healthcare more affordable and guide its customers to financial security. The insurer’s healthcare division is affiliated with over 150 hospitals and employs over 5,000 associates nationwide.
Today’s economy has had a significant impact on the insurance industry and has exacerbated the ongoing issues of customer retention, lowering costs and improved loss ratio. In addition to a struggling economy, regulatory pressures and the implementation of complex new technologies have been a burden on insurance providers. With all of these challenges, it is essential to focus on customer service and retention of existing customers, as the cost of new customer acquisition is significantly more than improving the satisfaction of one’s existing customers. And it is well known that insurers select insurance companies based on outstanding service, not just on low price. In addition to customer service issues, there are those impacting operations – namely, the need to lower allocated and unallocated loss adjustment expenses, reduce fraud and leakage and maintain an improved loss ratio.
US Insurance firms polled by independent research firm, Forrester Research1 said that their most important business priorities over the next 12 months were (in priority order – highest to lowest: lower operational expenses, grow the business, acquire and retain customers, drive new offerings and comply with government regulations. The leading goal for IT, after lowering costs was to improve business process execution speed2. And of course as it has been in recent years they are still continually asked to do more with less. A prohibitively tall order? Not necessarily.
Insurance processes can be complex. Their core applications are highly interdependent and tightly intertwined. For example: claims processing starts with first notice of loss (FNOL), to assignment, coverage, contact, investigation and evaluation, negotiation and settlement, litigation management and recovery and salvage. Without deep visibility, it becomes a lengthy and expensive process to handle failure when it eventually occurs. Over time, the applications, the middleware interconnecting them and the transactions they invoke in order to deliver these services will invariably develop performance issues, incur failures and exhibit logic errors. This can lead to performance issues that slow the claim adjustment process.
Simply stated, policyholders want their money as quickly as possible. With this level of complexity, a software solution that can auto discover all applications, auto discover and profile the performance of all IT transactions and determine root cause is essential, especially when required to do more with less. While insurance IT groups do utilize monitoring tools, unfortunately, in order to monitor application and transaction performance it could require viewing as many as four consoles and figuring out on your own how they relate to each other. However, individual IT staff members can only focus on one at a time. Furthermore, these tools focus on individual Web, Java, DB or CICS silos, and their individual status may not enable the operator to know if the business is impacted by what they are seeing. These tools fail to provide big-picture transactional visibility.
This insurer’s health plans division has a series of branch locations, a single primary headquarters and over 3 million members. It is in a process of expansion and is adding more branch locations over the next 12-month period. Its current claims processing workflow has parallel processing of payments on claims occurring; however, they noticed that over a one-month period of time, the average time per claim transaction had been increasing by 10-percent, but that slowdown and trend toward SLA breach only occurred for one transaction type. They had tools to analyze OS problems, hardware servers, application servers, databases and the network. However, the existing monitoring tools were over five years old and were not providing the visibility the insurer needed. They were unable to automatically determine the problem causality chain, differentiate the symptoms of the problem from its root cause and resolve it.
This problem became increasingly expensive as it consumed over 90% of the insurer’s tier-three support personnel, taking them away from their daily projects that were associated with business growth and new revenue. Customers were finding more than 65% of application problems before IT support. The help desk was getting buried in tickets as customer dissatisfaction increased. The queue backlog at the help desk only made things worse. This became a critical issue for the insurer, as claims services are a major customer satisfaction criteria. They were concerned about customer attrition as the effectiveness and efficiency of claims processing customer service was a key differentiator for them. As a result of this, senior management became aware of the ongoing problem and requested IT to urgently find a solution.
After analysis of their situation, the following requirements were agreed upon by the IT and management teams:
The first application to be focused on would be claims processing
Real-time monitoring across .NET, Web Sphere AS and MQ, AS400s and CICS Transaction Server on their mainframe.
Ability for application support to easily answer the question from a customer, “Where is my claim?
Currently, Support couldn’t rapidly determine if the claim was stuck in a queue, an integration broker, an application or a database; thus, resulting in a long mean-time-to-repair (MTTR)
Reduce current four management consoles to a single one
Improve performance and meet our SLAs
Proactive problem detection
The insurance firm decided it needed a robust monitoring system with real-time alerting that could monitor transaction processing with high volume and volatility across a multi-state environment, including many platforms, systems and applications. It would have to work proactively to alert staff and resolve problems immediately when they arose.
After testing a handful of solutions, the insurer brought in Nastel AutoPilot® to assure performance, help meet SLAs and immediately diagnose why claims processing transaction performance was steadily decreasing. AutoPilot began to auto discover the applications and transactions in the insurer’s claims process including first notice of loss (FNOL), assignment, coverage, contact, investigation and evaluation, negotiation and settlement, litigation management and recovery and salvage.
Using AutoPilot, the insurer determined that one of its claim types was sitting in a WebSphere MQ queue too long and found there was severe contention on this queue from their various .NET and Java applications. In addition, a Java transaction, key to claims processing, was timing out waiting for this message. As new branches were added, they had saturated their current architecture design and had to reconfigure their middleware to handle the load increase. AutoPilot was able to immediately auto-determine the root cause of the problem and with the deep-dive visibility it provided, it was clear what needed to be done to remediate the situation.
After diagnosing the problem with an overly congested WebSphere AS and MQ deployment, the insurer worked with Nastel to implement a more effective architecture that would scale to handle the additional loads they were incurring.
“AutoPilot helped us get a handle on our claims processing, reduce our support costs and better support our customers,” said the Head of Claims Processing. “It was able to scale as our load increased and has enabled us to be considerably more proactive in our approach to problem management.”
As a result of deploying AutoPilot the following benefits were achieved:
The mean-time-to-repair (MTTR) for software problem decreased by 40%
The amount of time tier three spent on a problem decreased by 60%
The number of tickets at the help desk decreased by 35%
The average time to process a claim improved by over 30%
Additional benefits were derived from that fact that the claims process touches many roles both within and outside of this insurer. They include: customers, agents, adjusters, third party partners, supervisors, executives and attorneys. All of these roles benefited from the improvements in quality of service and reduction in outages.
Future plans at this insurer include leveraging AutoPilot beyond claims to provide visibility for business-critical systems and applications including:
Underwriting and rating
Institutional finance and governance
Service portals for agents
Self-service (including online policy servicing)
Contact center and CRM interfaces
About Nastel AutoPilot
Nastel’s application performance management solution, AutoPilot ensures the availability and performance of critical business applications via auto discovery, business transaction management, real -time monitoring, dynamic dashboards, complex event processing, application performance analysis, root cause analysis, proactive alerting and automated problem resolution. Our customers in the line of business, development and IT utilize AutoPilot to guarantee high application performance, compliance, reduced user impact, fewer incidents, lower costs and greater productivity.
Nastel Technologies is a premier global provider of application performance management and business transaction performance ™ solutions for mission-critical applications. Nastel is a privately held company headquartered in New York, with offices in the U.S., the U.K., Germany and Mexico, and a network of partners throughout Europe, the Middle East, Latin America and Asia. For more information, visit Nastel’s website at www.nastel.com.
1 “The Industry Essential: The US Insurance Market”, September 16th, 2010 Ellen Carney, Forrester Research, Inc.
2 “The State Of Enterprise Software And Emerging Trends: 2010”, February 12th, 2010, Forrester Research, Inc.