Ethical InsurTech pricing: separate vs joint optimisation

Price optimisation in insurance is increasingly under the spotlight, especially as firms seek to maximise profitability without falling foul of ethical or regulatory standards. At its core, price optimisation involves adjusting premiums across a portfolio to increase profit, constrained by strategic, legal and ethical considerations.

Price optimisation in insurance is increasingly under the spotlight, especially as firms seek to maximise profitability without falling foul of ethical or regulatory standards. At its core, price optimisation involves adjusting premiums across a portfolio to increase profit, constrained by strategic, legal and ethical considerations.

One of the more controversial practices linked to price optimisation is price walking—raising prices for loyal, renewing customers while offering better deals to new ones. While this may increase short-term margins, it damages trust and is banned in several countries, including the UK.

Quantee, an InsurTech pricing platform, is actively exploring ways to optimise prices fairly, ensuring renewals and new business can be managed strategically without resorting to price walking.

Balancing ethics and business outcomes

The key challenge lies in managing demand (conversion and retention probabilities) while ensuring pricing decisions do not rely on whether a customer is new or existing. In ethical optimisation, the model must “forget” this distinction when setting a price, even while tracking sales targets for both segments separately.

Quantee has outlined two approaches to this:

  1. Separate optimisation – Building distinct models and pricing for new and existing business, then combining results through weighted averages using a regression model.

  2. Joint optimisation – Merging data from both segments, training unified models, and applying constraints without revealing customer tenure in the pricing step.

Both approaches avoid price walking, but differ in complexity and precision. Joint optimisation allows for scenario testing and better alignment with strategic goals, though it requires more sophisticated modelling.

Choosing the right approach

Quantee’s analysis shows that separate models perform best when evaluated within their own business segment.

However, joint optimisation better meets overall demand goals and enables more accurate pricing across the board. While complex to set up, it allows insurers to fine-tune their pricing to hit retention and conversion targets without discrimination.

Ultimately, Quantee is working to refine these techniques further, helping insurers adopt fairer pricing strategies that boost profitability and trust, without breaching ethical or regulatory lines.

Read the full blog from Quantee here.

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