Insurance
What a Failed Insurance Software Release Really Costs
Rishabh Kumar
Published On
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A failed insurance release costs far more than the fix. The public cases show the real bill arrives as customer redress, regulatory penalties, past business reviews, operational disruption and delayed change, often over months or years. A single pricing implementation error in UK motor and home insurance led to redress estimated at around £30 million.
This page uses only figures from named public sources. Where no credible source exists for a number, we leave the number out and show you how to estimate it from your own data instead.
That is deliberate: much of what circulates about the cost of software defects does not survive a look at its origins, and a CIO taking a business case to a board needs numbers that will.
What Do the Public Cases Show?
Four public cases set the scale. One is an insurer’s pricing implementation error, one a bank’s failed platform migration, one a faulty vendor update that hit insurers’ own customers, and one an economy-wide estimate. Together they show that the cost of a failed release is dominated by what happens after it ships.
Case | What failed | Publicly reported cost | Source |
|---|---|---|---|
UK motor and home insurer, 2023 | An error in implementing the FCA’s equivalent new business price calculation meant some renewing customers paid more than they should have | Redress estimated at around £30 million, plus a voluntary past business review to find every affected customer | FCA statement and the firm’s own disclosure, as reported in the trade press |
UK bank platform migration, 2018 | Migration of customer data and services to a new IT platform caused widespread disruption to branch, telephone, online and mobile banking | £48.65 million in combined FCA and PRA fines (after a 30 per cent settlement discount), £32.7 million in customer redress, and disruption lasting until December 2018 | FCA and PRA final notices, December 2022 |
Faulty security software update, July 2024 | A defective update from a widely used endpoint security vendor crashed Windows devices worldwide | An estimated $5.4 billion in direct losses to affected US Fortune 500 companies (excluding Microsoft), an average of about $44 million per affected company, with insured losses estimated at $540 million to $1.08 billion | |
US economy, 2022 | Poor software quality across operational failures, cyber incidents and technical debt | At least $2.41 trillion a year, with accumulated technical debt of about $1.52 trillion | CISQ, The Cost of Poor Software Quality in the US: A 2022 Report |
The insurer and bank cases are described without naming the firms, in line with our editorial policy. Both are set out in public regulatory statements.
What Makes Up the Cost of a Failed Insurance Release?
The fix itself is usually the smallest line. The cost of a failed release is made up of seven components, and most of them scale with how long the defect stays in production and how hard it is to work out who was affected.
Customer Redress
Refunds of overcharged premiums, top-ups to underpaid claims, interest and compensation for distress or inconvenience.
Regulatory Penalties
Fines where the failure reflects weak systems and controls, as in the 2018 bank migration.
Past Business Review
The work of identifying every affected customer across every affected release: data extraction, recalculation, quality assurance and customer contact. In pricing cases this is often larger than the redress itself.
Remediation Engineering
Diagnosing the defect, fixing it, testing the fix and deploying it under pressure, usually by the same people who should be working on the next release.
Operational Disruption
Call centre volumes, broker queries, claims backlogs, manual workarounds and overtime.
Delayed Change
Releases frozen while the incident is handled, product launches slipped, and vendor upgrades deferred into a bigger, riskier jump.
Trust
Complaints, ombudsman referrals, broker relationships and customer retention. Real, but rarely measured well, so we do not put a number on it here.
Why Does Detection Time Drive the Cost?
Because most cost components scale with the number of affected customers, and that number grows every day a defect is live. A pricing error caught before the first renewal notice costs a test re-run. The same error found after a full renewal cycle costs a past business review across every policy it touched.
Insurance makes this worse than most industries for three reasons. Renewal cycles are annual, so a pricing defect can sit unseen until the following year’s comparison. Many defects do not raise errors; they produce plausible but wrong numbers. And long-lived products mean a calculation defect in life or pensions administration can affect values that customers only see years later.
The practical implication is that the cheapest place to catch a pricing, document or calculation defect is before release, and the second cheapest is the first build after the change, not the first complaint.
Catching that class of defect on every build is what a well-built insurance regression testing pack is for, with pricing checks that assert on the number, not just on the page loading.
What About the Claim That Production Bugs Cost 100 Times More?
We do not use it. The figure is widely repeated and usually attributed to an IBM institute, but researchers who have tried to trace it back to an original study have not been able to find one. The direction is right, since later defects cost more, but the multiplier is not evidence.
The public cases above make the point more persuasively anyway. A board does not need a multiplier when it can see what a single implementation error cost a peer.
How Do You Estimate the Cost of a Failed Release for Your Own Insurer?
Build the estimate journey by journey from your own data. For each critical journey, estimate how many customers a defect could affect before detection, the average financial impact per customer, and the fixed cost of investigating and fixing it. The expected cost of release failure is that impact multiplied by its likelihood.
For a single journey, the exposure for one undetected defect can be sketched as:
where the number of affected customers is the daily volume through the journey multiplied by the days until detection. Then weight by likelihood across the year:
The inputs come from places you already have.
Input | Where to find it |
|---|---|
Daily volume per journey | Policy, quote and claims volumes from MI or the data warehouse |
Days until detection | Your own incident history: when defects were introduced versus when they were found |
Average impact per customer | Average premium, claim or refund size, adjusted for the likely size of error |
Past business review cost | Previous remediation exercises, or internal day rates multiplied by estimated effort |
Remediation engineering cost | Incident tickets and time logged on recent production fixes |
Disruption cost | Call volumes and handling times during past incidents |
Likelihood of failure | Escaped defect rate per release, multiplied by the number of releases per year |
The likelihood input rests on your escaped defect rate, one of the software testing metrics worth tracking release by release if you don't already.
Worked Example: One Renewal Pricing Journey
These figures are illustrative and don't describe a specific firm. Replace every one with your own.
Input | Illustrative Value | Working |
|---|---|---|
Renewals per day through the journey | 4,000 | |
Days until detection | 45 | Found when complaints start to arrive |
Affected customers | 180,000 | 4,000 × 45 |
Average overcharge per customer | £18 | |
Customer redress | £3,240,000 | 180,000 × £18 |
Past business review | £600,000 | Data extraction, recalculation, QA and contact |
Remediation engineering | £80,000 | |
Operational disruption | £120,000 | Extra call volume and broker queries |
Exposure for one defect | £4,040,000 | Sum of the four lines above |
Likelihood in a year | 5% | From your escaped defect history |
Expected annual cost | £202,000 | 5% × £4,040,000 |
Now change one input: days until detection. If the same defect is caught on the first build after the change, before any renewal notice goes out, affected customers drop to zero, and the exposure falls to the cost of a fix and a re-run. That's why detection time is the lever that matters most.
What is the Cost of Releasing Too Slowly?
The other side of the ledger is the cost of caution. Insurers that cannot test quickly release less often, batch changes into larger and riskier releases, defer vendor upgrades and delay products. That cost rarely appears in an incident report, but it shows up in time to market, in upgrade effort and in the risk carried by every large release.
The aim is not fewer releases. It is releases that are proven before they ship, at the pace the business and the vendors set.
How Does Touchstone Change the Cost of a Failed Release?
Touchstone attacks the two drivers that matter most: detection time and the effort of working out what a failure touched. Requirements drawn from your own documents are tested on every build, so defects are caught before customers see them. And when something does go wrong, the evidence chain shows exactly which releases, rules and journeys were involved.
Cost Driver | What Touchstone Changes |
|---|---|
Earlier detection | Price, document and calculation checks run on every build and every configuration deployment, not only at release time |
No silent failures | Journeys assert on outcomes, such as the price used and the value calculated, so plausible but wrong numbers fail the test |
Smaller reviews | When a defect escapes, the evidence chain shows which release introduced it, which requirements and journeys were affected and which builds carried it, so the scope of a past business review can be set from evidence rather than assumption |
Faster, safer fixes | Deterministic re-runs prove the fix against the original build and the corrected one |
Faster releases | Evidence produced during the work removes the assembly step before go-live, which cuts the cost of caution as well as the cost of failure |
How that evidence chain is built, link by link, from the source document to the approver, is explained in release evidence in insurance.
Frequently asked questions
How much does a failed software release cost an insurer?
What is the biggest hidden cost of a failed insurance release?
Do regulators fine insurers for software failures?
Is it true that bugs found in production cost 100 times more?
How can an insurer reduce the cost of release failures?
Written by
Rishabh Kumar
AI Testing & Quality Engineering Contributor
Rishabh leads business development and marketing at Virtuoso QA. He writes about AI-powered test automation, end-to-end testing, self-healing tests, and quality engineering, exploring how these capabilities can improve test coverage, reduce test maintenance, and support reliable software releases.
