Friday, June 21, 2024

AI, Big Data and the Insurance Industry

Every time you read a trade journal, an article on LinkedIn or attend a conference you can bet there’ll be something about AI and Big Data (it’s always capital B and capital D too). It’s also probable that many businesses will be able to get along fine without either.

However, anyone wanting to profit from these innovations will be finding out exactly how they can assist them.

On the one hand, AI will undoubtedly help in processes, transactions and compliance. Machine learning will reduce time, cost and complexity from many arduous jobs within companies, businesses and firms. Error rates will drop too and there’ll be great confidence in the results and accuracy of AI-driven events.

All these, welcome as they are, are introspective, internal and are only helping match clients desires and requests to reduce costs and waiting time before their results are delivered to them.

The smart application of Big Data in a business is to use it to mine, analyse and interpret the vast reservoirs of information every business has stored within its archives and, more importantly, in the world outside the business. Few have started to explore the gold seams of data within their business and very few are doing that in the outside environment.

Some of these latter group have focussed their analytic effort on the data held inside litigation court cases throughout the world. If it’s a centre of litigation activity, the case outcomes are on-line and are available there are those who have scraped every piece of information from them. All this irrespective of the language or alphabet the data is in.

This can lead to simple comparisons of which law firms, attorneys/barristers have the best win/loss ratio in which courts. Strangely, the lawyers don’t spend much time promoting or advertising this quantitative, objective and observable information on their court performances. They’d much rather rely on qualitative and subjective snippets of praise from their clients on their websites or in trade directories.

In jurisdictions where the data is rich, the correlations can go much deeper. In the US, where there are 44000 litigation cases each day, this can go as far as matching those attorneys who always perform well in certain courts or in front of certain judges; and vice versa. There is, of course, no inference of any conscious bias in these results. It’s just that, statistically, in such large populations of data there will be discernible, repeatable and predictable patterns. Remember the word “predictable”.

Those who regular contemplate or engage in litigation solutions to their disputes profit from using data like this to help guide them to the best choices of lawyer or, knowing the opposition through its performance and results, settle within the reserves allocated to such events.

That’s just scratching the surface of what these analytics can offer.

Diligent application of industry knowledge to specific problems or anticipated problems provides the questions to ask those who mine the data.

Inside the court, and associated, data will be information about auto accidents, claims for injury through malpractice, claims for exposure to chemicals or other substances hazardous to health and so on. Many insurers and brokers are taking advantage of this already. Additionally, those with an eye to future trends in claims can collect data on, currently, unimportant or unobserved events.

In short, those who invest in the data will enjoy better claims handling due to knowing the likely problem claims/cases. There are also those who offer real time risk pricing using analytics.

Al Gore (Vice-President of the United States, 1993-2001) memorably said, “All predictions are dangerous, particularly those about the future”. It’s probable that, as with many of the things he said, the irony of this remark was lost on him.

However, data analytics have an element of predictive capacity about them. Given enough numbers and the application of statistical analysis to them the probabilities of specific outcomes can be calculated. Any aspect of gambling is prone to this and, while nothing is certain, the odds on any outcome can be derived from sufficient study.

Similarly, with court cases. Academic studies in the UK and the USA show high degrees of correlation between their predictive research and, actual, historic cases. There are a couple of early start-ups applying analytics to real-time court cases but, of course, they’ll have to build a big bank of data showing success before many invest them in real-time.

More usefully, there is already data to show the effects of litigation activity on share prices in US businesses. It’s common sense, really. If you’re suing/being sued a lot, and, whatever the result, your share price is liable to be affected. Now, though, the comparative share price of these serial litigators can be predicted against those of their competitors; by using Big Data.

The insurance industry is well-know for using as much information, qualitative and quantitative – but particularly the latter – to manage risk and reduce exposure as it can get. AI and Big Data moves the solution to the requirement for information to another dimension.

Read about how big data is shaping supply chains of tomorrow

About the author

Ian Dodd,  Director United Kingdom, Premonition

Ian Dodd has worked with the Bar in England & Wales for almost 15 years. As a CEO of barrister’s chambers and, latterly, as consultant to chambers and law firms he has helped the profession through the challenges of LASPO and the rapidly changing landscape of the legal profession.