Redefining Rating Engines for Enterprise Pricing Management

By Mavenblue

From Rules-Based to Model-Actuated Rating Engines

The Problem

Rating engines are a core component of Policy Administration Systems (PAS). Traditionally, a PAS includes a rules-based rating engine that allows insurers to build pricing rules derived from actuarial and commercial pricing models. This process typically involves translating the models into a structure the rating engine is able to consume. In many cases this means a translation into thousands of individual calculation rules. This re implementation of the models in the rating engine, introduces simplifications and potential errors, while extending implementation timelines.

This translation process can make it difficult for insurers to update pricing frequently. As a result, these updates often occur annually or less, and pricing models may be simplified to fit system constraints. This potentially leads to misaligned or outdated pricing in the market.

What Changes

A model-actuated rating engine offers a different approach. Instead of re-implementing the models as calculation rules, it integrates the actuarial and commercial pricing models directly into the PAS. This allows the PAS to execute these exact models, running real-time calculations based on the latest version of the underlying pricing logic.

In practice, the models become an embedded, executable component within the PAS or other, connected systems (such as front-end applications or offline sales tools). Pricing updates can then be implemented by deploying updated models rather than re-implementing and re-testing calculation rules manually.

What It Enables 

Embedding the pricing models directly within the operating environment allows insurers to:

Update pricing more frequently and with fewer implementation steps. 

• Retain the full complexity of the actuarial and commercial pricing models, including advanced  techniques such as machine learning, rate optimisation, and AI. 

• Shorten the pricing feedback loop. Testing, updating, and recalibrating models in shorter cycles. This  also allows for experimenting and ‘trying things out’. 

• Offer stronger alignment between actuarial intent and market execution. 

This approach has the potential to move insurers toward much shorter pricing cycles: from months down to  weeks or even days. Supporting more dynamic market responsiveness.

Practical Implications

Pricing Processes

Pricing and product teams can design and operate with greater flexibility, using as complex models and as many factors as needed without being constrained by system architecture. Automated links between the actuarial modelling environment and the live rating engine reduce reliance on IT reconfigurations and testing cycles. Teams can focus more on analytical evaluation rather than technical implementation.

Product Design

A model-actuated rating engine supports new product structures such as needed for – for instance – usage based insurance (UBI). Instead of relying on self-reported metrics at the end of a policy term, insurers can use continuous data inputs to update premiums in near real time, providing a more accurate reflection of risk and usage.

Claims Handling

For claims processes, recalculating premiums through the underlying models enables insurers to show customers how a claim may affect their policy. This transparency can improve understanding and decision making for both parties.

Operational Model 

Adopting a model-actuated rating engine approach can change how insurers manage distribution and pricing governance. Pricing can be differentiated by distribution channel and product bundle, and APIs or hand-overs of embeddable model-actuated rating engines, can replace static rate tables shared with external partners. Greatly reducing administrative overhead and errors.

Over time, this shift may allow insurers to react more quickly to market changes, refine pricing accuracy, and redirect analytical resources toward model development, insight generation, and strategic product innovation, instead of IT implementation work and testing.