Tag Archives: claims leakage

How to Cut P&C Claims Leakage

In their search to boost profits and reduce their loss ratio, property and casualty (P&C) insurance carriers often turn to improving a cast of “usual suspects”: sales, pricing, new product development and a host of operational areas from new business through subrogation. But the biggest area to target— the one with the largest, near-term upside potential—is claims processing. Every insurer wants to reduce operating costs, cut claims leakage and reduce claim severity.

But what’s the best approach?

That depends on whom you ask. Technology providers insist that bleeding-edge, massive new systems are the answer. Internal processing teams will push for more human resources—with more relevant experience and better training. Other executivess will tell you to focus on reducing claims fraud.

But if you ask The Lab, we will say that the best approach is to keep asking questions, because the answers will point you toward a massive payback—a windfall. For example, what is the “standard” P&C claims leakage ratio, i.e., the industry average benchmark? And what is the source for this leakage number? Probably, the answer you get on leakage ratio will fall in the 2% to 4% range. Press for the source. The likely answer will be vague and hard to pin down. It’s unlikely that the answer will be: “our routine analysis and measurement of our claims processing operations—at the individual adjuster level.”

Stated differently, the source is actually “conventional industry wisdom.” If so, you’ve stumbled into a diamond field of improvement opportunities. To scoop them up, all you need to do is upgrade your company’s ability to perceive and manage claims processing at an unprecedented level of granular detail.

It’s worth the heavy investment of initially tedious effort. That’s because actual claims leakage is typically several multiples of this conventional-wisdom average of 2% to 4%: The Lab routinely documents 20% to 30%, and even more. That means that the payoff for reducing leakage, even for smaller P&C insurers, can easily reach hundreds of millions of dollars—which drop straight to the bottom line.

No, customer experience isn’t devastated. That’s because other (completely satisfied) policyholders are having their claims paid by adjusters who follow the carrier’s guidelines. The lower-performing adjusters, on the other hand, are simply not following these guidelines, and carriers fail to practice the process-management discipline necessary to ensure that all adjusters adhere to the loss-payment rules and targets.

Now, if you ask The Lab precisely how to reduce your claims leakage and loss ratio, we will point to three underused tools, or improvement approaches, to help P&C insurers surmount this challenge and achieve breakthrough levels of benefits, specifically:

  1. Knowledge work standardization (KWS)
  2. Business intelligence (BI)
  3. Robotic process automation (RPA)

While the second two—BI and RPA—require a nominal amount of technology, the first approach, standardization, not only paves the way for the other two but also requires no new technology whatsoever. Typically, Knowledge Work Standardization, or KWS, alone delivers labor savings in excess of 20%, easily self-funding its own implementation— and readily covering much of the BI and RPA improvement costs. Taken together, these three tools rapidly transform an insurer’s P&C claims-processing operation and upgrade its related management capability. This allows management to significantly reduce loss payments while simultaneously improving operating efficiency. The result is an increase in “operating leverage”: the capability of a business to grow revenue faster than costs.

Interestingly, these three tools, or improvement approaches, also deliver major benefits for customer experience, or CX, aiding in policyholder satisfaction and retention. Here’s how:

  • First, roughly half of the hundreds of operational improvements identified during business process documentation will also deliver a direct benefit to policyholders.
  • Second, the process documentation and data analysis help pinpoint the reasons that policyholders leave. The predictive models that result help reduce customer erosion.
  • Third, these documentation and analytical tasks also identify the most advantageous opportunities for cross-selling and upselling. In this article, we will cover these three tools/improvement approaches broadly, then we’ll drill down to explore their real- world application—and benefits—in P&C claims processing.

1: The Search for Standardization in P&C Insurance Operations

Standardization—the same innovation that gave rise to the modern factory system—is arguably the most overlooked improvement tool in insurance operations today. And it applies to everything: data, processes, work activities, instructions, you name it. In other words, variance is standardization’s costly, inefficient evil twin. Consider:

  • Insurance operations performance is typically reported
    in the form of averages. These numbers are usually calculated for work teams or organizations. And this is also how supervisors approach their management task—by groups. Individuals’ performance is rarely measured, compared, benchmarked or managed.
  • Rules of thumb routinely apply. “Here’s how many claims an adjuster should be able to process in a given day or month.”
  • Industry lore trumps data-driven decision-making: “Claims processing is an art, not a science.” Or, even more dangerously: “Faster adjusters are the costliest ones, because they’ll always pay out too much.” (Spoiler alert: The opposite is true.)
  • Differences in details go unexploited: At one insurer, for example, The Lab discovered that five teams were processing claims—and each team used its own format and guidelines for notes. That single, simple issue confounded everyone downstream, as they struggled to reconcile who meant what.
  • “NIGO” prevails. The sheer opportunity cost of things like forms and fields submitted “not in good order,” or NIGO, can be staggering—often with tens of millions of dollars in unrecouped revenue flying just below executives’ radar.

2: Applying Business Intelligence, or BI, to Insurance Operations

Modern BI applications derive their power from their ability to create a clear picture from crushingly vast quantities of seemingly incompatible data. The best BI dashboards visualize this data as insightful, inarguable business-decision information, updated in real time. They let users zoom out or drill down easily; just think of Google maps. You can click from a state, to a city, to a house, then back up to a continent, using either a graphical map format or 3-D satellite photo.

Then why aren’t insurers routinely harnessing this power? Most already own one or more BI applications, yet they’re not delivering that critical Google-maps-style visualization and navigation capability.

This lack can be traced to two, intertwined obstacles: business data and business processes. Each requires its own, tediously mundane, routinely overlooked and massively valuable, non-technology solution: standardization.

See also: Provocative View on Future of P&C Claims  

Business data is already well defined—but it’s defined almost exclusively in IT terms. Think of the latitude/longitude coordinates on Google maps; do you ever actually use those? These existing IT definitions are difficult, if not impossible, to reliably link to business operations and thus produce useful, navigable business information.

The Lab solves this problem by mapping existing “core systems” data points to products, employees, transactions, cycle times, organizational groups and more. The solution requires standardizing the company organization chart, product names, error definitions and similar non-technology items. This is a tediously mundane task.

Technology can’t do this. But people can, in a few weeks if they have the right templates and experience.

Business processes are also already defined—but with wildly inconsistent scope. For example, the IT definition typically involves a “nano-scale” process—like a currency conversion or invoice reconciliation. Business definitions represent the polar opposite: global scale. Think of “order-to-cash” or “procure-to-pay.” All parties involved—throughout business and IT—thus talk past each other, assuming that everyone is on the same page. Worst of all, almost no business processes are documented. They exist informally as “tribal knowledge.”

The Lab solves this disconnect by mapping business processes, end-to-end at the same “activity” level of detail that manufacturers have perfected over the past century. Each activity is about two minutes in average duration. The range for all activities is wide but easily manageable: from a few seconds to five minutes. Over the past 25 years, The Lab has process-mapped every aspect of P&C insurance operations—and we’ve kept templates of every detail for these highly similar processes. Consequently, we can (and routinely do) map business processes remotely, via web conference… around the world!

Rigorously defining, standardizing, and linking business data and business processes underpins the best BI dashboards, delivering the Google-maps-style navigation that execs crave. This is how it’s possible to build astonishingly insightful BI dashboards that help make claims leakage losses apparent to our clients.

3: Robotic Process Automation, or RPA: A Powerful New Tool for P&C Carriers

Robotic process automation, or RPA, is simply software— offered by companies such as Automation Anywhere, Blue Prism, and UiPath—which can “sit at a computer” and mimic the actions of a human worker, such as clicking on windows, selecting text or data, copying and pasting and switching between applications. If you’ve ever seen an Excel macro at work, then you can appreciate RPA; it simply handles more chores and more systems. And it isn’t limited to a single application, like Excel. It is as free to navigate the IT ecosystem as any employee.

RPA “robots” are thus ideally suited for mundane yet important repetitious tasks that highly paid P&C knowledge workers hate to do. Better yet, robots work far faster than people, without getting tired, taking breaks or making mistakes. This frees up human workers for higher-value activities.

RPA also confers customer experience, or CX, benefits. With faster operations, customers enjoy the Amazon-style responsiveness they’ve come to expect from all businesses. On-hold times are reduced, claims get processed faster and the entire company appears more responsive.

Beyond the dual opportunities of knowledge-worker labor savings and CX lift, RPA holds the power to disrupt entire industries. Deployed creatively in massive waves, it can deliver windfall profits on a scale not even imagined by its purveyors.

Yet, today, most insurance companies’ RPA efforts, if any, are stalled at the very beginning; recent surveys indicate that internal teams hit a 10-bot barrier and struggle to find more opportunities, or “use cases.” That’s because the underlying processes to be automated are never made “robot-friendly” in the first place. So there needs to be scrutiny of the different activities—and the elimination of all of the wasteful ones that hide in plain sight, such as rework, return of NIGO input, and so on.

How to Overcome the 10-Bot Barrier in P&C Claims Processing

First, set expectations to focus on incremental automation with bots. No, you’re not going to replace an entire adjuster with a bot. But, yes, you will be able to quickly use a bot to call a manager’s attention to a high-payback intervention in the P&C claims-adjusting process. Examples:

  • Managers look for inactivity on open claims: If a claim is open with no activity in the last 10 days, that’s a red flag. But many claims are overlooked. A bot can call these out promptly.
  • Full-replacement cost, instead of partial replacement cost, is a major cause of overpayment that is most prevalent in roofing, flooring and cabinetry replacements. Bots can track payments and send management alerts based on line-of-coverage and even more granular detail. Roofing examples include: replacement, whole slope vs. whole roof; and number of roof squares replaced.
  • Audits are conducted on claims to improve quality and consistency—and to reduce overpayment. However, these are done on a very limited sample and only after claims have been paid and closed. Based on learnings from past audits, bots can alert management when certain claims- processing failures happen on a live basis. Managers can intervene… before payment.

Standardization (KWS), BI, & RPA: Focusing on P&C Claims Processing

All three of the above tools, or improvement approaches for P&C carriers—standardization or KWS; business intelligence, or BI; and robotic process automation, or RPA—can be readily applied to claims operations. Indeed, they seem to be custom- made for it.

Standardization

Consider the following story, created from a mashup of different P&C insurance carrier clients of The Lab:

This “insurer” had plenty of claims data to share with The Lab; in fact, theirs was better than most. But that’s not saying too much: While 40% of the data was usable and comprehensible, the other 60% wasn’t. (Remember: This is better than most P&C insurers.)

Data was reported weekly, and sometimes daily, on an organization-wide basis. Here’s what they had data to report on:

  • Overall averages of claims processed, based on total headcount.
  • Average losses paid per claim.

That said, the company never tracked the performance of individual claims processors. They were all effectively “self- managed,” following their own individual procedures. There were no standard, activity-level instructions and guidelines, set by management, for quantifying targets for time, productivity or effectiveness. There were, on the other hand, vague, directional methods, many in the form of undocumented “tribal knowledge” and “rules of thumb.” The claims processors simply managed their own workdays, tasks and goals—similar to Victorian-era artisans, prior to the advent of the factory system.

When pressed, the company defended its choice to not track individual performance. The two reasons it gave would come back to bite the managers:

  • They were confident that individual performance, if measured, would only vary by about 5% to 10%, maybe 15% at most.
  • They were equally confident that imposing time and productivity quotas on processors would increase loss severity. In other words, they were completely sure that faster claims processing equates to overpayment of claims.

However, their very own data contradicted both of these notions—in a huge way:

  • First, the “long tail” of claims processors revealed a 250% variance between the top and bottom quartiles of individual performers—that’s 15 to 50 times higher than what management believed to be the case. In other words, the top three quartiles were out-processing the bottom quartile so much that there was no hope of the bottom quartile catching up—even getting close to the average. Put another way: Reducing this variance alone would yield a 25% capacity gain—an operating expense savings. And it could be accomplished by the top performers’ simply processing just one more claim per day—an increase they’d barely even notice.
  • Second—and just as important—the data revealed that the slower performers actually overpaid each claim by an average of 50%, an amount that totaled in the scores of millions, swamping the amount spent to pay their salaries. This carrier was thus getting the worst of both worlds with its lowest performers: They were slower, and vastly more costly. Not only that, they dragged down the average performance figures (not to mention morale) of the faster, leaner producers.

The impact from these revelations equated to losses measured in hundreds of millions of dollars. Incidentally, the story above is not rare; rather, it’s typical. As we’d mentioned, it’s based on a mashup of several insurance carriers.

Here’s one other standardization eye-opener. The claims process itself was rife with rework, turnaround, pushback and error correction. As a claim made its way through reporting, contact, dispatch, estimating, investigation and finally payment, it bounced and backtracked between the FNOL (first notice of loss) team, the appraiser, the casualty adjuster and so on. When presented with this “subway map” of the as-is process, the insurer’s executive sponsors were aghast:

Fortunately, the “spaghetti mess” can be cleaned up, even without new technology.

Business Intelligence (BI)

The Lab often encounters P&C insurance companies that invest heavily in systems such as Oracle Business Intelligence or Microsoft Power BI yet struggle to get value from these advanced analytics platforms.

Many of the issues stem from failing to “complete the final mile” when it comes to data definitions and hierarchies; that is, companies aren’t reconciling the IT-defined data elements with their own business-defined operations characteristics. This problem can often be traced to a disconnect between business leaders and IT organizations.

An IT person could—and often does—assemble and manage business intelligence for business units. But the person needs to understand the business so well that the person could confidently select which data to use and aggregate so that the final KPI (key performance indicator) in the resulting dashboard represents reality. And even if the person managed to create a BI picture of perfect “reality,” there’s no guarantee that the business would accept it. Let’s be frank: Creating useful BI and related analytics is a towering challenge. It’s overwhelming not only to IT; most businesspeople lack both the documentation and the comprehensive perspective to pull it off. So, the status quo continues: The “business language” experts will talk with the “IT language” experts, and the business executives will still lack the Google-maps insights they seek.

See also: Keeping Businesses Going in a Crisis  

Another BI stumbling block is the “false precision” of too much data and too many categories. Consider the automotive insurer with “claims types gone wild”—such as “Accident: Right front fender,” and “Accident: Left front fender,” and so on. The Lab’s BI dashboards will often reveal to claims executives that 20% of the claims types represent 80% of the volume—another valuable, “long-tail” insight.

Robotic Process Automation (RBA)

As noted earlier, operational issues and customer-experience or CX challenges are typically two sides of the same coin. Often, both can be addressed by robots.

For example, consider the policyholder who calls the FNOL contact center and validates info. Then the person is handed off to another rep, who must re-validate the info. And then another. And another.

That’s not just an operational mess. It’s also creates a clear and present danger of losing that customer, hiding in plain sight.

While robots can speed repetitive chores, they can’t fix the underlying business processes (remember that FNOL spaghetti map, above?). Fortunately, Knowledge Work Standardization can. And once it does, the robotic possibilities are practically limitless: They span everything from sales prospecting to renewal notices to premiums/commissions reconciliation.

You saw how RPA bot deployments augmented the work of claims-processing managers. The next step is to augment the hands-on work of rank-and-file adjusters. Again, don’t try to replace the entire job position. Instead, augment the processor’s activities. In particular, hand off the adjuster’s mind-numbingly repetitive activities to the bot. This will allow the adjuster more time and thought—not to mention accountability—for complying with the policy’s payment guidelines.

For P&C claims, there are numerous opportunities to “park a bot” on top of routine, repetitive, knowledge-worker activity. Think of these as admin-assistant bots for adjusters. Here are two of many examples:

  • The “pre-adjudication assistant” bot. Adjusters spend lots of time sorting out “unstructured” information at the receipt of the FNOL. For example, they read descriptions of damage that arrive in free text data fields, then they standardize it and proceed to adjudication activities such as looking up coverages and setting reserves for the claim, prior to contacting the insured. Most, if not all, of these activities can be performed by RPA bots—but only if the inbound information is standardized. The Lab has used its KWS methods to create drop-down menus for this data and make it RPA-friendly. This standardization can be done incrementally, enabling bots to prep claims for adjusters: They look up coverage limits, set reserves and prep for the adjuster’s call to the insured.
  • The “customer contact assistant” bot. Adjusters, and others in the contact center, spend a great deal of avoidable and inefficient effort communicating with policyholders regarding their claims: advising status, notifying for damage inspections, obtaining corrections to initial NIGO information and more. Simply contacting customers can be a tedious, time-consuming and inefficient process; bots can help. They can be configured to send notifications to customers, preempting calls to the contact center. Bots can also initiate “text-call-text” notifications to customers’ cell phones. Here’s how it works: Bots, at the push of a button by the adjuster, send a text to the customer. The text may notify the customer to expect a call from the adjuster—avoiding call screening. The adjuster calls and gets through. Afterward, the bot sends a confirmation of the issue or next step.

Make the Move Toward Improved Insurance Operations & Reduced Loss Ratio

Claims processing, as we’d mentioned at the outset, is just one area within the P&C carrier organization where the power triumvirate of Knowledge Work Standardization (KWS), business intelligence (BI) and robotic process automation (RPA) rapidly deliver massive windfall value.

Disjointed Reinsurance Systems: A Recipe for Disaster

Insurers’ numerous intricate reinsurance contracts and special pool arrangements, countless policies and arrays of transactions create a massive risk of having unintended exposure. The inability to ensure that each insured risk has the appropriate reinsurance program associated with it is a recipe for disaster.

Having disjointed systems—a combination of policy administration system (PAS) and spreadsheets, for example—or having systems working in silos are sure ways of having risks fall through the cracks. The question is not if it will happen but when and by how much.

Beyond excessive risk exposure, the risks are many: claims leakage, poor management of aging recoverables and lack of business intelligence capabilities. There’s also the likelihood of not being able to track out-of-compliance reinsurance contracts. For instance, if a reinsurer requires certain exclusion in the policies it reinsures and the direct writer issues the policy without the exclusion, then the policy is out of compliance, and the reinsurer may deny liability.

The result is unreliable financial information for trends, profitability analysis and exposure, to name a few.

Having fragmented solutions and manual processes is the worst formula when it comes to audit trails. This is particularly troubling in an age of stringent standards in an increasingly internationally regulated industry. Integrating the right solution will help reduce risks to an absolute minimum.

Consider vendors offering dedicated and comprehensive systems as opposed to policy administration system vendors, which may simply offer “reinsurance modules” as part of all-encompassing systems. Failing to pick the right solution will cost the insurer frustration and delays by attempting to “right” the solution through a series of customizations. This will surely lead to cost overruns, a lengthy implementation and an uncertain outcome. An incomplete system will need to be customized by adding missing functions.

Common system features a carrier should look out for are:
  • Cession treaties and facultative management
  • Claims and events management
  • Policy management
  • Technical accounting (billing)
  • Bordereaux/statements
  • Internal retrocession
  • Assumed and retrocession operations
  • Financial accounting
  • AP/AR
  • Regulatory reporting
  • Statistical reports
  • Business intelligence
Study before implementing

Picking the right solution is just the start. Implementing a new solution still has many pitfalls. Therefore, the first priority is to perform a thorough and meticulous preliminary study.

The study is directed by the vendor, similar to an audit through a series of meetings and interviews with the different stakeholders: IT, business, etc. It typically lasts one to three weeks depending on the complexity of the project. A good approach is to spend a half-day conducting the scheduled meeting(s) and the other half drafting the findings and submitting them for review the following day.

The study should at least contain the following:

  • A detailed report on the company’s current reinsurance management processes.
  • A determination of potential gaps between the carrier reinsurance processes and the target solution.
  • A list of contracts and financial data required for going live.
  • Specifications for the interfaces.
  • Definitions of the data conversion and migration strategy.
  • Reporting requirements and strategy.
  • Detailed project planning and identification of potential risks.
  • Repository requirements.
  • Assessment and revision of overall project costs.
Preliminary study/(gap analysis) sample:

1. Introduction
  • General introduction and description of project objectives and stakeholders
  • What’s in and out of scope
2. Description of current business setting

3. Business requirements

  • Cession requirements
  • Assumed and retrocession requirements
4. Systems Environment Topics
  • Interfaces/hardware and software requirements
5. Implementation requirements
6. System administration
  • Access, security, backups
7. Risks, pending issues and assumptions
8. Project management plan

The preliminary study report must be submitted to each stakeholder for review and validation as well as endorsement by the head of the steering committee of the insurance company before the start of the project. If necessary, the study should be revised until all parts are adequately defined. Ideally, the report should be used as a road map by the carrier and vendor.

All project risks and issues identified at this stage will be incorporated into the project planning. It saves much time and money to discover them before the implementation phase. One of the main reasons why projects fail is poor communication. Key people on different teams need to actively communicate with each other. There should be at  least one person from each invested area—IT, business and upper management must be part of a well-defined steering committee.

A clear-cut escalation process must be in place to tackle any foreseeable issues and address them in a timely manner.

A Successful Implementation Process
Key areas and related guidelines that are essential to successfully carry out a project.

Data cleansing
Before migration, an in-depth data scrubbing or cleansing is recommended. This is the process of amending or removing data derived from the existing applications that is erroneous, incomplete, inadequately formatted or replicated. The discrepancies discovered or deleted may have been originally produced by user-entry errors or by corruption in transmission or storage.

Data cleansing may also include actions such as harmonization of data, which relates to identifying commonalities in data sets and combining them into a single data component, as well as standardization of data, which is a means of changing a reference data set to a new standard—in other words, use of standard codes.

Data migration

Data migration pertains to the moving of data between the existing system (or systems) and the target application as well as all the measures required for migrating and validating the data throughout the entire cycle. The data needs to be converted so that it’s compatible with the reinsurance system before the migration can take place.

It’s a mapping of all the data with business rules and relevant codes attached to it; this step is required before the automatic migration can take place.

An effective and efficient data migration effort involves anticipating potential issues and threats as well as opportunities, such as determining the most suitable data-migration methodology early in the project and taking appropriate measures to mitigate potential risks. Suitable data migration methodology differs from one carrier to another based on its particular business model.

Analyze and understand the business requirements before gathering and working on the actual data. Thereafter, the carrier must delineate what needs to be migrated and how far back. In the case of long-tail business, such as asbestos coverage, all the historical data must be migrated. This is because it may take several years or decades to identify and assess claims.

Conversely, for short-tail lines, such as property fire or physical auto damage, for which losses are usually known and paid shortly after the loss occurs, only the applicable business data is to be singled out for migration.

A detailed mapping of the existing data and system architecture must be drafted to isolate any issues related to the conversion early on. Most likely, workarounds will be required to overcome the specificities or constraints of the new application. As a result, it will be crucial to establish checks and balances or guidelines to validate the quality and accuracy of the data to be loaded.

Identifying subject-matter experts who are thoroughly acquainted with the source data will lessen the risk of missing undocumented data snags and help ensure the success of the project. Therefore, proper planning for accessibility to qualified resources at both the vendor and insurer is critical. You’ll also need experts in the existing systems, the new application and other tools.

Interfaces

Interfaces in a reinsurance context relate to connecting to the data residing in the upstream system, or PAS, to the reinsurance management system, plus integrating the reinsurance data to other applications, such as the general ledger, the claims system and business intelligence tools.

Integration and interfaces are achieved by exchanging data between two different applications but can include tighter mechanisms such as direct function calls. These are synchronous communications used for information retrieval. The synchronous request is made using a direct function call to the target system.

Again, choosing the right partner will be critical. A provider with extensive experience in developing interfaces between primary insurance systems, general ledgers, BI suites and reinsurance solutions most likely has already developed such interfaces for the most popular packages and will have the know-how and best practices to develop new ones if needed. This will ensure that the process will proceed as smoothly as possible.

After the vendor (primarily) and the carrier carry out all essential implementation specifics to consolidate the process automation and integrations required to deliver the system, look to provide a fully deployable and testable solution ready for user acceptance testing in the reinsurance system test environment.

Formal user training must take place beforehand. It needs to include a role-based program and ought not to be a “one-size-fits-all” training course. Each user group needs to have a specific training program that relates to its particular job functions.

The next step is to prepare for a deployment in production. You’ll need to perform a number of parallel runs of the existing reinsurance solutions and the new reinsurance system and be able to replicate each one and reach the same desired outcome before going live.

Now that you’ve installed a modern, comprehensive reinsurance management system, you’ll have straigh-tthrough automated processing with all the checks and balances in place. You will be able to reap the benefits of a well-thought-out strategy paired with an appropriate reinsurance system that will lead to superior controls, reduced risk and better financials. You’ll no longer have any dangerous hidden “cracks” in your reinsurance program.
This article first appeared in Carrier Management magazine.