Step 3: Assess impact

A practical framework for fair algorithmic pricing

Who gains and loses?

Quantify firm profit and consumer welfare from pricing outputs (premium, approval, loss ratio, markups). Prediction-level fairness alone is insufficient (Huang, Shimao, and Khern-am-nuai 2026).

Cost modelling and welfare

Huang, Shimao, and Khern-am-nuai (2026) connects fairness constraints on cost models to pricing, demand, participation, and welfare:

• Interventions can alter pricing even with modest accuracy changes
  
• Standard fairness metrics may reduce welfare for protected groups after selection effects
  
• Distinct firm vs consumer trade-offs by intervention type

Full pricing process

Huang and Shimao (2026) models the complete pipeline:

Stage	Role
Cost modelling	Step 2 technical premium
Demand modelling	Willingness to pay
Price optimisation	Market premium
Policy constraints	Fairness and accountability

Pricing rules

Rule	Description
P0	Unconstrained
PA	Accountable (decomposable base rates + relativities)
POB	Price optimisation ban
PDP	Demographic parity on premiums
PAF	Actuarial group fairness

Key findings

1. Price fairness vs markup fairness: tension; no rule achieves both.
   
2. PDP closes price gaps but can widen markup disparities.
   
3. PA reduces markup gaps with profit losses.
   
4. POB effects depend on market structure (voluntary/compulsory, competition).

Measures

Measure	Definition
Consumer welfare	Willingness to pay minus price paid
Firm profit	Price charged minus expected cost

Report by protected group: premium changes, approval rates, markup gaps.

Checklist

• Welfare memo: firm profit and consumer welfare by group.
  
• Price vs markup fairness distinguished.
  
• Market structure documented.
  
• Trade-offs linked to Step 4 monitoring plan.
  

References

Huang, Shimao, and Khern-am-nuai (2026)

Huang and Shimao (2026)

References

Huang, Fei, and Hajime Shimao. 2026. “Welfare Implications of Fair and Accountable Insurance Pricing.” Journal of Risk and Insurance. https://doi.org/10.1111/jori.70051.

Huang, Fei, Hajime Shimao, and Warut Khern-am-nuai. 2026. “Do Fair Algorithms Improve Welfare? Evidence from the Insurance Market.” UNSW Business School Research Paper Forthcoming. https://doi.org/10.2139/ssrn.5112616.