"What's to be done will all that messy data struggling to make it's way from the client to broker to insurer? Or from all the parties joined across insurance ecosystems ? Question raised by two articles: -
Data machine: the insurers using AI to reshape the industry in today's Financial Times
Data sharing models in the insurance industry by Willis Towers Watson
And today in my inbox an answer from Matthew Grant and the Instech London Team who have published a timely and insightful report. It will help in this most fundamental of all success factors- data management and mastery.
"The value of great analytics is significantly undermined if the data is lost, corrupted or hard to extract. And with so much innovation all around us, why does this problem even still exist?
We don't have all the answers, but we have been doing our homework. The latest (sixth) report from InsTech London explores what is happening in the world of data extraction, organisation and ingestion and looks at 40 companies (and more) that are figuring this stuff out.
Available now to download here. Get yours before we start charging for it."
Patrick Kelahan, the Insurance Elephant, vividly describes the impact of everyday data issues across typical data sharing models visualised below.
Illustration from "Data Sharing Models in the Insurance Industry by Willis Towers Watson
"Consider existing data sharing within insurance, say four users/sharers. If the probability of effective handoff of data is 90% (a robust expectation), then over four handoffs the aggregated probability of effectiveness is...66%.
Is that 66% of data are just right, or 66% of data are more pure than the residual 34%?
And if the effective data handoff is just 80% and there are 6 users/sharers the outcome is really scary.
"The concern also is that each player places a priority on data that are really important to the respective player, even though data beyond the key stuff is also shared. And consider timeliness, volume, composition, tests for bias (Y or N), any characteristic you want. In Animal Farm all animals were considered equal, just some animals were noted as more equal than others. It can be said similarly for data sharing."
The Instech London Report describes a large range of companies that can help extract, ingest, normalise and analyse internal and external data sources without which the most effective algorithms in the world will be hobbled by lost, hidden and/or corrupt data.
It's the reason even new digital claims management platforms deployed by leading name insurers often require policy holders to type in name, address, zip code, policy number etc to kick off eFNOL. The data was not extracted to prefill eFNOL.
Wait until you try to integrate three or four point solutions into the new Quote and Buy or Claims Management platform say Tractable, Shift and SightCall.
The devil is always in the detail of data in multiple and disparate data silos and systems.
Really helpful to read this report from Instech London and find companies that can help solve these challenges and allow incumbent insurers fight the competition from scalable insurtechs that have gained a competitive advantage with data mastery. And leverage the potential of AI and embedded insurance.
And just to add more companies that can help insurers, brokers and those enterprises that wish to embed insurance in their products and services: -
Major core platforms Guidewire, Duck Creek, Majesco, ICE, Pega, Innovation Group and so on. Good on breadth of functionality but sometimes lacking in specifics e.g. claim management. And traditionally involving Capex, considerable SI costs and high annual licensing costs though that is changing slowly.
Quote & Buy, MTA & Renewal Platforms like Go-Insur, HUGHUB and iptiQ that enables an insurer to allow a customer to interactively manage all their policies from one dashboard
Digital Claims Platforms RightIndem, Synergy Cloud, Snapsheet, ClaimsGenius, Salesforce Industries, Five Sigma, 360Globalnet, etc
Insurance Ecosystems Core Logic with Symbility, Verisk, LexisNexis, Mitchell International, CCC etc
Point Solutions Weathernet, Tractable, Audatex/Solera, Shift, Friss, Sprout.ai, and many others
Combined claims services and technology providers Crawford & Company, Sedgwick, Davies Group, Claims Consortium Group. Control€xpert etc.
No-Code/Low-Code app building platforms i.e. build yourself but with built in accelerators from Unqork, Netcall, Ushur …
Embedded insurance Wakam, Wrisk, Qover, Trov, Cover Genius, Bsurance, Kasko, Inshur etc
"What's to be done will all that messy data struggling to make it's way from the client to broker to insurer? Or from all the parties joined across insurance ecosystems ?