| blog

Ernelene Jacobs

January 21, 2026

Less Repetition , More Reasoning

Building actuarial pricing structures means working through familiar steps repeatedly. Each new product, portfolio change, or model update requires adapting existing rating frameworks: defining how risks are grouped, how perils are combined, which adjustments apply, and in what sequence. Depending on the nature and extent of the changes, this typically takes anywhere from several days to two to three weeks per structure.

Once built, these structures must be validated by tracing how each variable flows through the calculation, checking that dependencies between different variables, models, and lookup tables connect correctly, and confirming the logic executes as intended. When errors surface late in the process, additional hours go to debugging and correction.

After validation comes deployment (packaging the rating logic into production-ready code), then simulation testing against real portfolios, then translating the statistical outputs into clear summaries. This final step means explaining what the model reveals about risk separation, pricing adequacy, and segment performance in language that is defensible to stakeholders and regulators.

Each of these stages requires care and expertise. But much of the effort is structural rather than analytical: following established patterns, checking known dependencies, documenting standard metrics. The work is necessary, but it is not where actuarial judgement adds the most value.

The result is that significant capacity is absorbed by process, leaving less time for the interpretive work that shapes better pricing decisions. This includes identifying emerging risks, evaluating trade-offs between segments, or determining where model assumptions need refinement.

Introducing Gauss

Gauss is our context-aware AI agent built to support teams by reducing the repetitive effort that surrounds pricing work.

Embedded directly within our platform, Gauss operates with full awareness of your working context. It understands the rating structures you have defined, the calculations linking them together, the dependencies between variables and models, and the internal structure of your lookup tables. Unlike general AI assistants that require repeated explanation or manual data transfer, Gauss already knows what you are working with. When you ask it to build a rating structure or validate a deployment package, it works from the same foundation you do: your data, your logic, your constraints.

This shared context changes how quickly and confidently work can progress. Iteration becomes faster because Gauss does not need re-explaining. Analysis becomes clearer because Gauss can reference specific components by name and trace their relationships. Outcomes become more reliable because Gauss works within your established framework rather than making assumptions from incomplete information.

Rating Structures and Deployment Packages

Rating structures define how premiums are calculated: grouping risks, applying constants, separating and combining perils, and arriving at a final price. Deployment packages translate these structures into production-ready instructions used to calculate premiums for individual risks.

Building these from existing work requires translating pricing logic into executable, production-ready code. Gauss generates a structured initial framework that can be reviewed and refined. This can be initiated using predefined prompts aligned with our pricing framework or through direct, iterative interaction.

For example:

You describe a home insurance structure with fire, flood, and storm perils, base rates, and adjustments for construction type and building age. Gauss generates the structure and explains how perils are separated and how adjustments are applied.

You add a 15% loading for properties over £1 million and specify a £500 minimum premium. Gauss updates the model and explains exactly where these elements sit within the calculation sequence.

You decide to combine wind and hail into a single storm peril. Gauss restructures the model whilst preserving all existing logic.

At each step, Gauss explains its reasoning so that alignment can be validated before moving forward. What previously required days of manual coding can now be drafted in minutes, with teams reporting that rating structure build times that once took weeks are now completed in hours.

Validation and Code Review

Gauss reviews existing code and full pricing pipelines in the same way an actuary would. It identifies implementation issues such as missing variables, broken dependencies, or incorrect data flows, and returns precise, node-level findings with clear technical explanations.

Examples include:

"The flood loading references construction_class, but this variable is not passed from the previous step."

"This conditional will always evaluate to false given your data ranges."

Where uncertainty exists, Gauss flags it explicitly rather than making assumptions:

"This may be correct, but the variable naming suggests a possible mismatch. Please verify."

This validation process helps catch errors early, reducing the debugging cycles that typically follow late-stage discovery of structural issues.

Simulation Analysis

Simulation is where actuaries explore data, test models, compare rating structures, and run what-if scenarios on real portfolios. Gauss supports this process by interpreting performance metrics such as lift charts, double lift charts, loss ratios, and Gini coefficients, translating technical diagnostics into clear, actionable conclusions about model accuracy, stability, and risk separation.

For example, when reviewing simulation results for a motor portfolio:

You request analysis of loss ratio movements between the current and proposed structures. Gauss highlights that whilst overall loss ratios remain stable at 68%, there is emerging deterioration in the high-value vehicle segment (loss ratios increasing from 62% to 71%), warranting investigation before deployment.

You ask for a summary suitable for stakeholder review. Gauss produces a clear narrative explaining that the proposed structure improves risk differentiation in most segments but requires attention to high-value vehicles, with specific recommendations for further analysis.

Analysis that once took several hours can often be completed in 20 to 30 minutes, with Gauss surfacing the specific patterns and anomalies that require actuarial attention.

Amplifying Actuarial Expertise

By managing structural complexity and surfacing the most relevant patterns in data, Gauss enables actuaries to focus more time on interpretation, strategy, and decision-making. It is not designed to automate judgement, but to strengthen it.

In practice, this means faster iteration without loss of control, clearer insight with less manual effort, and greater confidence that pricing logic behaves as intended from design through deployment. Gauss acts as an embedded teammate, aligned with actuarial standards and focused on the repetitive work that often limits capacity.