Analytics on your side.
PikaGuard is a risk analytics company. We measure what a risk costs you, so the decision to insure it, or keep it, is made on numbers rather than habit.
Insurance is bought on feeling. It should be bought on arithmetic.
Cover is often chosen on habit, a headline price, or an assumption nobody went back to check. That is a shortage of information rather than of judgement. Without the data and the modelling to price a risk yourself, the only number in front of you is the one the insurer put there.
Overpaying for the risk you carry
Premiums are set against the insurer’s default assumptions about a business like yours. There is strong precedent for renegotiating them once you can credibly show your risk is lower than that default.
Insuring losses you could absorb
An insurer charges more than a pound for every pound of risk it takes on. Where you can take the hit, that loading is simply a cost, and the capital behind it is better held, or invested, by you.
Carrying exposures nobody priced
Deductibles set too low, liability limits set too low, and whole risks left off the schedule. The events that end a business are rarely the ones that were argued over at renewal.
Treating risk as if it stood still
Risk moves as the business moves. A programme priced two years ago describes a company that no longer exists, and the client pays for the gap.
A risk has a price whether or not anyone quotes it.
Take a glass in your kitchen. Replacing it costs £40. If it were certain to break this month, the risk would cost you £40 a month. At a one-in-two chance, £20. At one-in-ten, £4. That figure, the average monthly cost of the risk, is the honest price of carrying it, and it exists whether or not you ever buy a policy.
An insurer will not sell you that risk at £4. It asks for more than a pound for every pound it takes on, because it has costs and shareholders of its own. That loading is the reason to transfer only the risks you genuinely cannot absorb, and the reason to know the real number before you decide.
Scale the glass up to an escape of water, a fire, a liability claim, or a month of stopped operations, and the arithmetic does not change. Only the size of the numbers does, and with it the cost of getting them wrong.
Ordinary actuarial methods. Far better inputs.
There is no secret formula here, and we would not trust one if there were. We use the same established actuarial techniques the industry uses. The difference is how much we know about your particular asset before those techniques are applied, and how hard we work to prove the answer against itself.
Parameterise everything
Uncertainty is expensive, because the instinct when you cannot see clearly is to overestimate. So we ask about the detail others skip: whether the pipes are iron or plastic, whether a porter reaches a leak in ten minutes or ten hours, how the basement is insulated. Every one of those changes the number, and each change is justified against data, a physical model, or a stated conservative assumption where no data exists.
Co-validate, then co-validate again
A figure from one source is a guess. We rebuild each estimate from independent datasets and published research, and we keep going until they agree within tolerance. Where they refuse to agree, that disagreement is reported rather than averaged away.
Answer in ranges, not single numbers
Nothing real is a constant. Fire frequency is not a constant, and neither is the cost when it happens. Every figure we publish is an interval with a stated confidence, because a single decimal point implies a certainty that no honest analysis has.
Treat mitigation as a real option
Cover moves a risk. Better controls shrink it. If a building has bad locks, the answer may be better locks rather than a bigger policy. Training, maintenance and monitoring belong in the same comparison as the premium.
Where the data comes from
Public records, licensed datasets, published research, and asset and claims history contributed by partners. It is historical data, held at a finer grain than a postcode average, cross-checked against other sources until it converges. Our edge is the granularity and the cross-checking, not a black box. We would rather explain exactly where a number came from than ask you to take it on faith.
An analytics firm.
We provide analysis. We show you what your risks cost, how a programme behaves under the worst outcomes, and what changes to it are worth considering.
Nobody in this chain is paid more when you buy more cover, which is why the numbers can be shown to you plainly.
Total transparency by default. You see the dispersion, not a reassuring single figure. The assumptions behind it and the workings come with it.
Your decision, made comfortably. We give you the analysis and the options. Take the time you need, and ask for a specialist if you want one.
Explained until it is obvious. Probabilities mean little as bare decimals. We give them in terms you can weigh against the other risks you already accept.
Kept under review. We tell you when capital can be released and when an exposure has grown enough to need covering.
Imperial graduates, working quietly.
PikaGuard was founded by graduates of Imperial College London, working alongside qualified actuaries. We are in stealth until our current talks with investors close: the modelling, the database and the early client work are all live, but we are not yet talking publicly about the details.
That is a matter of timing rather than secrecy. The work that makes the analysis worth trusting is slow: the parameterisation, and the convergence between independent sources. It is better done properly than announced early. We would rather be judged on a report you can pick apart than on a press release.
If you would like to see how we pinpoint the risk on a real asset, we are happy to walk you through a worked example.
A fifth of what we make funds education.
Twenty per cent of PikaGuard’s profit goes directly to charities supporting education for underprivileged people. It is written into how the company is run rather than bolted on afterwards.
Understanding your own risk should not require a business large enough to employ an actuarial team.