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					       insight                                         EXPERT THINKING FROM MILLIMAN

A cost-effective approach to casualty claims analytics
27 April 2012 | RAVI KUMAR, ACAS, MAAA

An insurance company’s financial condition and its customer satisfaction levels are both decided in large part by the organization of its claims
operations. Therefore, streamlining those operations can provide a significant business advantage.

This is especially true for small insurers. One of the strengths of a small insurer is its proximity to its customers. Oftentimes, the smaller the
company, the greater the potential for designing its claims operations to meet both customer needs and profitability goals.

Claims analytics can be an effective tool for streamlining claims operations, but
is it also cost-effective? It may seem to be a high-cost, technology-driven activity        Claims analytics does not need
affordable only to large insurers with big budgets—but it doesn’t have to be so.
                                                                                            to be front-loaded with high
Claims analytics needn’t require a large up-front investment; an optimal approach
is designed in such a way that it pays for itself very quickly.
                                                                                            costs. The whole process can
                                                                                            be designed in such a way that
This article discusses a cost-effective, results-oriented approach to claims                the effort pays for itself in a very
analytics that can provide immediate benefits to any organization, and particularly         short time frame.
to small insurers.

From business objectives to implementation
Claims analytics is an objective tool used by insurance companies to identify, correct, control, and monitor performance issues in their claims
operations. Many large insurers have implemented sophisticated claims analytics solutions that seamlessly integrate their day-to-day business
processes with their corporate goals.

Claims analytics uses empirical information available in data collected by the insurance company and helps answer high-value business
questions about claims operations, resource allocation, vendor management, cost containment, litigation management, fraud detection, and
other areas. For example, can we identify a potentially unexpected property, demographic, or customer characteristic consistent throughout a
recent loss trend? Or, are there unusual attributes of properties that show up more often in a certain segment of claims, or in our handling of
certain segments of claims?

                                    Figure 1: Claims analytics as a sequential process

                                          Define            Prepare
                                                                              Model              Implement
                                          objectives        data

A claims analytics project has many phases, each requiring its own specific set of skills.

First, the claims department needs to define high-level objectives, which are derived from the company’s strategic goals, known issues facing
the department, and a detailed understanding of day-to-day operations.

The second phase of the project consists of identifying the most relevant data sources and preparing the data for analysis. This data
preparation phase is usually both people- and computer-intensive.

Then, statistical techniques are used to develop models that provide objective insights on the selected business problems. This phase is
generally done by actuaries or other people with statistical skills.
A cost-effective approach to casualty claims analytics

Finally, the insights from the models need to be translated into business actions and implemented in real business processes. Implementation
is both business- and IT-intensive.

Standard approaches to claims analytics (the old, expensive way)
Early adopters defined claims analytics as a project with a predefined, sequential set of tasks similar to a typical project undertaken within an
insurance organization. As a result, standard approaches often apply best practices from project-based initiatives to claims analytics.

Because advancements in technology have made claims analytics possible, thought leadership has mainly come from technology vendors and
consultants, and all phases of claims analytics initiatives are often technology-driven.

Large-scale data organization is emphasized in the data preparation phase, while, in the modeling phase, technology issues play an important role
in statistical tool selection. Sophisticated models based on machine learning and other techniques rely on advancements in computing power.

In the IT-implementation phase, large resources are spent in areas such as enterprise-wide data warehousing and business rules engine and
business intelligence implementations.

Typically, most projects use a sequential approach. Traditional software development life cycle methodologies have been used for technology selection,
project planning, staffing, and response to business requirement changes. Business value is often delivered all at once at the end of a project.

In this approach, IT is given well-defined data requirements up front. IT prepares the data and hands it off to actuaries, who typically
spend a few months analyzing the data and designing models. Then, the claims people are introduced to the models. Once the
models are acceptable to the claims leadership, the IT implementation phase takes a few more months to integrate the steps into the
claims process.

All these sequential steps would be appropriate if the claims analytics process was a well-defined process as predictable as widget-making.
Unfortunately, claims analytics is more like a discovery process. As with any discovery process, we do not know beforehand which paths to
take and which paths lead to dead ends. Many times we do not even know what we will be discovering.

In a sequential model, a trial-and-error-based discovery approach can be accommodated only to a small extent and at considerable cost. As
a result, projects that are expected to take only a few months often exceed those timelines and run for years. Many projects realize very little
business value compared to the investments made.

Large teams, large IT infrastructure, high costs, and budget overruns are common—but are they inevitable?

An alternative, results-oriented approach
A more effective approach views claim analytics as a business-driven, iterative, continuous activity, rather than as a technology-driven,
sequential, one-time project.

It used to be difficult to find someone who understood claims and also the immense value of analytics, but most business leaders in the
claims area now realize that valuable objective insights can be found in data.

Claims analytics is a bespoke craft used to gain a competitive advantage. Industry standard solutions or best practices borrowed from others
will not provide that core advantage. Claims department leaders should take it upon themselves to develop a solution tailor-made for the
specific culture and specific needs of their claims organization.

A cost-effective approach to casualty claims analytics

The claims department can leverage people from other parts of the organization and/or hire consultants who complement their internal skills,
but it should make sure not to depend on those resources in the long-term. For example, a strong IT team can set up an initial infrastructure,
but the claims department should not be dependent on IT for the day-to-day data needs of an analytical project.

Iterative and incremental
Claims analytics does not need to be front-loaded with high costs. The whole process can be designed in such a way that the effort pays for
itself in a very short time frame.

The diagram in Figure 2 shows the different phases of a claims
analytics life cycle. The goal is to experience all phases of the claims      Figure 2: Iterative approach to claims analytics
analytics process within a reasonably short time frame, all the while
preparing to do it again very soon. Each iteration enables the team to
learn and to improve. There is gradual cross-training across teams.
Over time, the people who fit best tend to stay on the team, and                                    Monitor
the appropriate technologies are put in place. Business leaders are                                                              objectives
directly involved; they will learn in this process why certain decisions
work and others do not.

These low-risk, small-budget iterations allow for the design of a long-
term solution that is tailor-made to the organization’s unique needs.
And, approaching a project in such an agile, iterative way can allow               Implement
the team to potentially see benefits in a matter of weeks rather than                                                                  Prepare
the old-style approach which could take a year or more to begin to                                                                      data
deliver value.

Continuous                                                                                             Analyze and
To be effective, claims analytics should not be a one-time or once-                                    get insights
every-few-years project. Models require constant calibration as claims
adjusters, customers, and vendors react to changes already in place. In
addition, claims analytics must incorporate changing business needs on
an ongoing basis.

A core team should stay close to the business to provide the needed services and continue to play a role in every phase of the claims
analytics process.

Practical and effective claims analytics strategies are vitally important for insurance companies. A business-driven, iterative, and continuous
approach results in a solution that best serves their business needs.

Small insurance companies are uniquely placed to take advantage of this approach. They are already used to deploying small but effective
teams to solve other issues in their organization and do not have a “bigger is better” mindset or the organizational silos that often affect
larger insurance companies.

Small insurance companies can therefore more easily design and implement cost-effective solutions that are tailor-made to their unique
needs—an approach that is long overdue.

Ravi Kumar is a predictive modeling consultant in Milliman’s San Francisco office. Contact him at

A cost-effective approach to casualty claims analytics

1       Cockburn, Alistair (May 2008). Using both incremental and iterative development. Software Technology Support Center (STSC) CrossTalk 21 (5):
        27–30. ISSN d0000089. Retrieved April 10, 2012, from
2       Larman, Craig & Basili, Victor R. (June 2003). Iterative and incremental development: A brief history. IEEE Computer (IEEE Computer Society) 36 (6):
        47–56. doi:10.1109/MC.2003.1204375. ISSN 0018-9162. Retrieved April 10, 2012, from
3       Wikipedia: Iterative and incremental development. Retrieved April 10, 2012, from
4       Wikipedia: Agile software development. Retrieved April 10, 2012, from


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