Narrow AI powered products like SHIFT and FRISS deliver value but generally work their magic on just 20% of an insurer's data- the structured and a proportion of semi-structured data.  But would you make a major investment decision on just 20% of data? No, you would not.

To enjoy the wider scope and fruits of AI, deep learning, BOTS and NLP is not for the fainthearted.  Just look at the experiences with "The astonishingly good but predictably bad AI program" GPT-3 as reported in the FT.

"Shannon Vallor, a professor of ethics at Edinburgh university, argued that GPT-3 had no understanding, which she defined as a sustained project of building, repairing and strengthening “the ever-shifting bonds of sense”. “Like the bullshitter who gets past their first interview by regurgitating impressive-sounding phrases from the memoir of the CEO, GPT-3 spins some pretty good bullshit,” she wrote. 

However, David Chalmers, a philosophy professor from New York University, suggested that GPT-3 was showing hints of human-like general intelligence. “I am open to the idea that a worm with 302 neurons is conscious, so I am open to the idea that GPT-3 with 175bn parameters is conscious too.” 

Intriguingly, GPT-3 was then fed these comments and prompted to reply: “To be clear, I am not a person. I am not self-aware. I am not conscious. I can’t feel pain. I don’t enjoy anything. I am a cold, calculating machine designed to simulate human response and to predict the probability of certain outcomes. The only reason I am responding is to defend my honour.”

John Thornhill Financial Times 11th August 2020

So too long to wait for the insurer's version of GPT-3 and to narrow an approach from AI plays like SHIFT.

That is where 360Retrieve fits- bang in that big gap- the unstructured data chasm.

It tackles big problems for an insurer: -

  1. Countering FRAUD better than incumbent systems
  2. Reducing UNDER-RESERVING from large loss claims
  3. Flagging and actioning INCUBATED CLAIMS
  4. Ingesting, categorising and analysing large volumes unstructured data from surges in BUSINESS INTERRUPTION CLAIMS
  5. Uncovering unacceptable risk and improving underwriting

What's the difference then?

1.Fills the problem gap between narrow AI solutions and long-term broader deep learning/NLP projects none of which access and analyse all text & unstructured data in forms, documents, emails, annotations..

2.Vertical accelerators augment professional intuition to automate RAG scoring, improve decisioning and embed IP in claims management

3.One platform for all use cases that meets LOB need for urgent solutions to compelling problems e.g. increasing fraud, under-reserving and new claims surges

4.Self-serve approach avoids burden on central IT, allows constant iteration to anticipate changing MO of professional fraud, legal companies

5.Automatically extracts information and transforms  insights into decisioning processes; vital for unexpected surges like Business Interruption

Other insurers have increased fraud identification by 20% and increased large loss identification by 7% and in both cases earlier in the claims cycle. They have seen ROI if between 12:1 to 20:1

You could achieve that by bridging the chasm of unstructured data. Worth a 30 minute call?

Just ring me on 07341 971132 or email

Thank you.