Insurers and Casinos have one stark difference. A casino pays you when you win; an Insurer when you lose. Well, two differences if you count the fact that the house always wins.
Casinos deal in high-frequency, low-severity events, like jackpots with controlled payouts and probabilities of occurrences.
Insurers, on the other hand, must accurately assess risks from high-severity, low-frequency events such as hurricanes and earthquakes to stay profitable.
Catastrophe (CAT) models aim to set the odds in favor of the insurer. They do not predict the occurrence of catastrophic events.
Instead, they are stochastic models of historical disasters, simulated for tens of thousands of years to predict the probability of loss given a disaster's parameters.
CAT models assume forward-looking disaster rates are the same as those of the past century or half.
Humanity's (reliable) documentation of a mere century of past disasters is inadequate to fuel a forward-looking model to predict disasters accurately. Insurers must surf the waves of catastrophes induced by climate change to stay afloat.
An increase in the frequency and severity of catastrophes, coupled with an explosion in the population of areas more prone to disasters, contribute to higher insured losses and, thus, reduced profits for insurers.
Analyzing incomplete or inaccurate exposure data to price insurance policies is like using malfunctioning slot machines to run a casino business. Imagine malfunctioning slot machines in casinos all over Vegas. The house might lose in that case. We know exposure data received in Statements of Values (SOVs) is inaccurate and incomplete. So we make up for this inadequacy by looking at the loss histories of each property. But, even here, the norm of five years of loss history is insufficient.
SOVs are still cleaned manually in 2022. The Data Cleansing, CAT Modeling, Underwriting, Actuarial, and Claims teams keep the same data in their silos. Portfolio snapshots are updated monthly or, worse quarterly. Calculating the marginal impact of adding policies to a live portfolio is theoretically possible but practically challenging to implement due to technology constraints. Dealing with endorsements (mid-policy changes to an insurance contract) is a nightmare. A lack of AI & ML to automate day-to-day Insurance functions is impacting the bottom line.
There are several initiatives an Insurance company can take to drive higher profits. They can increase Net Income, lower their Combined Ratio, or increase their Policyholder Surplus. Insuring bad risks at inadequate prices merely to rack up premiums or meet targets is no less than rolling the dice. A myopic vision by leadership, coupled with irrational incentives for small streaks of success, facilitates hard markets in the aftermath of catastrophes. Insurance as an instrument of societal resilience must be a long-term game to be truly effective. One big disaster can instantly negate a streak of good years.
Culture is anything two or more people share. Companies have humans and, thus, culture. If the collective values of a company's employees strengthen the pillars of its success, it will thrive. Insurance companies can win, too, if leadership is adaptive to periodically embrace new ideas to improve data quality rather than being obedient to old technology.
If there's one unifying theme for these profitability levers, it is certainly data.
The more accurate and accessible your information is, the more correct your decisions are.
So if profit is indeed the raison d'être for your insurance business, the question to ask yourself in 2022 is, "Are you paying enough attention to your data?".
I certainly am.