Predictive Modeling & Small Commercial Insurance Risk Assessment
Predictive modeling is effective as a risk assessment tool in personal insurance, but acceptance in commercial insurance has been relatively slow, even for small commercial. When an insurance carrier hesitates to integrate predictive modeling, it’s usually results from a lack of resources or understanding about how to build an effective model.
Predictive modeling is still a mystery, causing many organizations wondering how to introduce and integrate it into their business operations. Rationale needs to encompass:
– Will our executive leadership understand the basics of predictive modeling and support its use?
– Will the potential benefits justify the investment of company assets and resources?
– Do we have the necessary expertise to build a predictive model, and if not, what options exist?
– Do we have enough data to build a predictive model?
– What data sources should be considered for use in the model?
– How long will it take to design and build the model?
– What will our front line underwriters and, more importantly, our agents think?
– How will we know if our predictive modeling project is a success?
Predictive Modeling & Product Development Lifecycle Process
When implementing predictive modeling the carriers face one of the following dilemmas:
– Lack of expertise or data to build and incorporate their own models into the operations workflow
– Capability to build predictive models, but unable to incorporate them into the operations workflow
– Unable to build their own models but adept at implementing vendor or consultant-built models
– Sophisticated at building and implementing predictive models within their operations
Carriers successful at leveraging the benefits of predictive modeling typically apply a product development lifecycle process to ensure adoption and use. And, they identify the problem they must solve then determine whether or not predictive modeling can help solve it. Before starting this phase, two significant conditions must be met:
– Establish executive sponsorship
– Establish a cross-functional team for the modeling project
Strong executive sponsorship is critical to success through applying the resources and budget needed on the project. It also requires solid team from all impacted functions (including sales, underwriting, product management, claims, IT, actuary and analytics) all engaged and participating to integrate predictive modeling into the workflow. This team is then responsible for:
– Identifying and validating the business problems to be solved
– Generating ideas about how to solve these problems with predictive modeling
– Selecting the best ideas to implement
– Showcasing the benefits of predictive modeling
– Pinpointing what costs will be incurred
– Determining if the costs of solving a problem with predictive modeling justify the cost or going elsewhere
– Exploring how the model works in conjunction with business operations; which benchmarks and measures will be
applied to assess the model’s success
– Establishing buy-in
Design and Development
Many insurance carriers are building different types of predictive models used for risk selection, pricing, claims fraud detection, claims subrogation potential, etc. For small commercial, there is a growing movement to use predictive modeling for risk assessment and pricing via an “insurance score” that ranks order risks in terms of loss propensity. This approach is a three-steps process: Data Exploration, Model Creation & Validation, and Regulatory Review.
Here different groups work together to evaluate which data sources to use and which sources correlate with the target. For small commercial risk assessment, insurance loss is typically the target. The working group responsible for data exploration includes business analysts, statistical modelers, IT resources and a member of the regulatory team― often the carrier’s product manager.
With many third-party data sources to consider when building a model for small commercial risk assessment, commercial credit is a very popular approach because it’s sourced from two commercial credit bureaus. For a micro business, biz owner consumer credit is a commonly used data source as well. Public records on either the business owner or business are another popular source for assessing risk. Many carriers integrate prior loss and/or geospatial data into their predictive models.
Model Creation / Validation
When building a predictive model, carriers require a large sample of data to train their model to predict the target and test and validate that it works. The data must include the variable – the target that is being predicted. This sample data has two distinct sets: Training data and Testing data.
Training data – The training data is used to identify which variables correlate with the target being predicted and what weightings should be applied to those variables within the predictive model. While many variables may correlate with the target, mitigate the use of these correlating variables since predictor variables can create unstable model weights.
Testing data – The data for testing is used to simulate production and validate the model predicts the target or whether further refinement is necessary. When building a model to assess small commercial risk, carriers typically use a large sample of both commercial policy premium and claims data. When organizations do not have sufficient data to build their own model, they leverage a model developed by a vendor or consulting group.
If the scoring system is working correctly, the gains chart measures loss ratio against score deciles. As the score improves, the loss ratio should decrease. Characteristics when assessing a score’s benefit:
– Lift—What is the ratio between the best-performing group and the worst-performing group?
– Smooth segmentation—Is there a smooth trend as the score moves from the worst-performing group to the best-
performing group? As the score improves, does the loss ratio associated with that group decrease?
Properly segmented insurance scores allow a carrier to establish groups in order to automate underwriting to decline, refer or accept business without underwriter intervention. These types of segmenting scores are also easily incorporated into rating or a tier-based algorithm.
Carriers and solution suppliers must also consider the impact of the regulatory landscape on the data sources and attributes used in the models they create for small commercial risk assessment and pricing. When applying a predictive model for commercial insurance underwriting and pricing, most states require the model to be filed. Not all states agree about which data sources and attributes are permissible.
There are several key steps that allow for a successful implementation. While a number of organizations are extremely successful in developing a model that predicts their target, many others struggle when it comes to fully integrating the model into their operations and workflow. These include:
Implementation Considerations – For small commercial risk assessment, carriers must decide how to incorporate the score into their pricing and underwriting. The score can justify discretionary pricing or be incorporated into their rating or writing company selection. They also identify and document how, if at all, underwriting rules and procedures will change.
IT Systems Integration – With rating and underwriting changes determined, the implementation team works closely with the IT team to build the production version of the score, incorporate the score into the application workflow, and store and track score usage.
Dispute Process – Depending upon the data sources used in the model, ensure a that a dispute process from the applicant or insured can be supported. To assess small commercial risk, many carriers use “consumer reports” (as defined by the Fair Credit Reporting Act (FCRA). When using a data source subject to the FCRA, insurance carriers need to be prepared to provide adverse action notice when coverage is denied or priced in a more expensive rating tier.
Stakeholder Training – All internal and external parties need to be trained on the model and how it will be used, its benefits, and how success will be measured. Sales and Marketing must develop a communication plan to explain the score to their marketing representatives and agents. They also must decide how they will roll the score out across their operations: i.e.: will the score be phased or rolled out to production all at once?
Models are great but tracking ongoing performance and tweaking are vital to for the business. There are two components in monitoring for predictive modeling: Tracking usage and Monitoring efficacy:
Tracking usage – Scores should be tracked both when they are used and when they are overridden. When overridden, it is essential to know who overrode the score and why. Allowing for and documenting score overrides provides valuable insight into score limitations and how the score and its implementation should be improved in the future.
Monitoring efficacy – Carriers must also periodically monitor the overall efficacy of the model … does it achieve the desired results? Do performance indicators meet expectations? If not, the underlying causes are required.
A well thought-out, deliberate plan that aligns with a product development lifecycle process is the key to a successful predictive modeling implementation. It enables commercial insurers to realize the full benefits of predictive modeling for small commercial risk assessment and pricing.
Critical elements in this effort are executive sponsorship, a competent and engaged cross-functional project team, and a four stage life-cycle process of identification, design and development, implementation, and monitoring to steer the process.
Carriers who’ve applied these best practices have transformed their businesses. These guidelines provide leverage in ways that help your business grow and prosper.
Contact TelePay Insurance to learn more about Predictive Modeling for your company.
Call Today – 800 977-2976 or email: firstname.lastname@example.org
Source: LexisNexis Solutions