The simple term "algorithm" covers a wide range of techniques leading to the varying, and often misleading, labels given to various insurtech initiatives.
The linked article below provides a useful reference framework to be more precise and help ensure a meeting of minds.
It also shows the wide range of processes and information flows that various insurtech solutions address with disruptive technologies. It is vital that the C-Suite across all sectors of the Insurance market decide where to focus insurtech.
As in all other sectors, 80% of the investment and time applied will result in only 20% tangible returns. That implies waste, decline and falling into the laggard group. I fear many projects are falling into that trap beguiled by the siren voices of insurtech.
It is too easy to be carried away by digital transformation. Without a clear idea why any specific project should be a priority only tears and gnashing of teeth will be the end result.
One simple example- it is tempting to access and analyse the contextual, streaming data from telematics and dashcam. But if you cannot even access and analyse the unstructured data already stored internally why collect such large volumes of new data much of which will add little value.
And should you need more facts to back up this warning that most analytics and big data projects lead to disappointment just read my article "Damning analysis of Analytics".
Attend to the basics first before tackling the external data. That time will come and then you will need to apply machine learning and AI to filter out the wheat from the chaff.
“Algorithms” cover a wide range of techniques from simple statistical reasoning, to machine learning and artificial intelligence. So when people try to sell you their “big data”, “data analytics” or “artificial intelligence” solution, it is worth digging to understand what is behind the words.