Answers · Insurance · 7 questions

Life and term insurance data

Updated 25 Oct 2026 · Reviewed by Mohammad Shakir, Co-founder & CEO

Life insurance competes on products as much as premiums, which changes what's worth tracking. These answers cover term premium benchmarking, product and rider tracking, and the refresh cadence the category actually needs.

How do I benchmark term insurance premiums across insurers?

Collect premiums for standardised profiles - age, sum assured, policy term, smoker status, payout options - across insurer sites and aggregators, and compare the annual premium for identical cover.

Term insurance is the most comparable life product, which makes it the natural benchmarking anchor for the category.

What affects term premium comparisons?

Age, sum assured, policy term, smoker status, premium payment term, payout structure and riders. Small differences in any of these move the premium enough to invalidate a comparison.

As with motor, normalisation is the work: fix the profile, then compare.

How do aggregators keep life premium tables fresh?

By re-collecting premiums for their profile grids on a schedule and on insurer product changes, and by monitoring for new products and rider changes across insurers.

Stale tables are a trust problem - a shopper who gets a different number at the insurer than the aggregator showed stops trusting the aggregator.

How is life insurance data different from motor?

Life premiums change less often, products differ more in structure than price, and more of the decision rides on features, riders and claim settlement than on small premium gaps.

So life benchmarking is as much product-feature tracking as price tracking - a comparison table of riders and terms, refreshed as products change.

What can be tracked beyond premiums?

Product launches and withdrawals, rider availability and pricing, publicly reported claim-settlement ratios, and how insurers position products on their own sites and on aggregators.

Feature and positioning changes often precede price moves, so tracking them gives earlier signals than premiums alone.

How do banks and distributors use life insurance data?

To compare the products they distribute against the market, support advisory conversations with current data, and spot gaps in their shelf - segments where the products they carry aren't competitive.

The need is the same as aggregators': current, matched-profile data across insurers, delivered into their own tools.

How fresh does life insurance data need to be?

Weekly to monthly covers most benchmarking needs, with event-driven refreshes when an insurer launches or reprices a product. Faster than that rarely changes a decision in this category.

Spend the freshness budget where products actually move - term pricing for younger profiles sees more action than legacy products.

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