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Futurist and fintech entrepreneur Lex Sokolin explains the distinction between automation and artificial intelligence (AI), and how AI is transforming the coverage value chain—from chatbots to claims.
Highlights
- Automation is the manner of translating human technique to system process. It’s programmed from the top-down, with a regarded workflow and known effects.
- Artificial intelligence (AI) is the digitization of human intelligence to system intelligence. It calls for big data, fed into a mathematical algorithm, to be able to create correlations among heaps of various parameters. It allows choices at scale, however not like automation, the workflow and consequences aren’t regarded.
- Sales and claims sellers are examples within the coverage value chain where automation and AI can be applied. However, in standard, chatbots haven’t been very effective at replicating human interactions.
How AI is transforming insurance, with Lex Sokolin
Welcome again to the Accenture Insurance Influencers podcast, in which we ask industry leaders approximately traits and technology shaping the destiny of insurance: self-driving motors, fraud-detection era, and patron-centricity.
Lex Sokolin is a futurist and fintech entrepreneur. In our final episode, he explained why developments in banking and wealth control could maintain treasured classes for insurers, specially on the subject of working with—now not against—insurtechs. In this episode, Lex dispels a few myths about AI, and appears at how AI may be carried out to the present coverage price chain.
The following transcript has been edited for length and readability. When we interviewed Lex, he turned into the global studies director at Autonomous Research; he has for the reason that left the organization.
I experience like a variety of humans confuse AI––the utility of AI––with the software of automation. Can you highlight the difference between the ones ?
If you think about digitization or automation, for me that splits into sorts of vectors. The first one is human method to gadget method, some thing that someone does in a manual manner, in a workflow. Take that and positioned it into software program.
We have enjoy with this each single day: consider going into Excel and typing in a mathematical formula. You’re defining a ruleset according to which software program will compute some thing. Or you cross one step similarly and say, “Let’s construct software program for account opening.” Instead of a human being coming into the workplace and filling out paperwork, you can seize that on a mobile platform.
That taking of that facts and filling out paperwork, that’s all programmed pinnacle down. We recognise what the workflow is. It’s sufficiently simple for us to sketch it out and flip it into “if this, then that” guidelines, after which the final results is fully deterministic from in which we started and what type of data we introduced. We recognize how it works. We can reverse engineer the code and apprehend what’s taking place very easily.
The 2d way you can have digitization is from human intelligence to gadget intelligence. And inside system intelligence, there are extraordinary processes to creating effects that feel like intelligence, that sense like there may be an detail of judgment to it. The one that’s popular proper now could be device mastering enabled by mathematics referred to as neural networks.
What neural networks do thoroughly is remedy a problem in a probabilistic way to create an intuition for what some thing is. If you’re a human being looking at a photo of a cat, you know that it’s a cat and not a dog, and there’s a technique in our mind by which that happens. The photograph of a cat has no which means to a computer until it is transformed into information. You need hundreds of thousands of versions of that information cat to be aggregated and fed right into a mathematical set of rules that’s capable of create correlations between heaps of different parameters so as to say, “this is much more likely to be a cat” or “this is more likely to be a dog.”
AI continues to be software program. It’s still a device, but the foundational piece isn’t the pinnacle-down common sense of “if this, then that.” The foundational piece is big facts units on which the software program sits, or is educated, and people foundational statistics units came out of the Internet. And once you have the ones statistics units, you’re able to practice these exceptional mathematical algorithms on top. You can basically placed into a ruleset how a person would make a judgment, and then you could lift out that judgment and you may plug it in into a software system. And so now, at scale, you can do things like make choices on whether somebody must be getting more credit and get their next loan. And with every new piece of records you update that.
A lot of this got here out in advertising. Amazon is superb at providing you with pointers about what you should purchase next, and Netflix and Spotify know your tastes in video and song inside the same manner. And in coverage, there are masses of various ways that AI can be used at the manufacturing layer, on the running claims layer, on the portfolio management layer, as well as inside client distribution.
So, very distinct worlds. Automation is the, “if this, then that” command, a Soviet Union significant making plans world, wherein you define all the effects that are deterministic. And then the AI international is probabilistic, based on present statistics which you train the neural networks on, and it’s a lot more like codifying a human intuition after which deploying it at device scale.
Finally, one of the things that plagues the thought leadership on this area is portray with a very broad brush. Pictures of cyborgs and numerous network diagrams to make it feel futuristic. This stuff [AI], end of the day, is all just a set of human gear that people advanced which will be extra effective, if you want to scale their wondering and truly do greater. Even though it sounds threatening or very bold, I don’t suppose AI is any exceptional than the invention of the cloud, or energy, or the wheel or fireplace or language—or any of those foundational things in human development.
So with regards to AI, which answers, or styles of answers, are the most mature?
I’d say that the maximum mature elements of artificial intelligence are the ones which can be being constructed by the large tech businesses. The massive tech companies are prompted to recreate quite a few the human senses. They want to determine out a way to offer services and products to people in a way that’s intuitive and is chosen by using the people in those structures.
What I imply via that is the sense of imaginative and prescient, the experience of listening to, the potential to create speech. Those are matters which can be very mature in phrases of the generation itself, how nicely-educated the networks are and what data is available for that education. When you reflect onconsideration on self-using vehicles, that’s additionally a version of gadget imaginative and prescient.
Those are the mature technologies and in massive element due to the fact they’ve been built via the huge tech groups, whether within the west or in or inside the east. When you take that and use it on the monetary services enterprise and to insurance, it’s not a marvel to peer the stuff that’s getting used the maximum to be the only that maximum aligns with human potential.
That makes feel. So how do you see AI being applied to the conventional insurance price chain? In distribution, for example?
If you reflect onconsideration on coverage sales dealers, what's it that they do? Well they have got a role of being physically gift wherein a client is. You can think about that as nearly a billboard for the financial product, and within the US alone I suppose there are 370,000 coverage sales dealers—so there’s a function for AI there. How do I find the patron? How do I get to where they're? Artificial intelligence will let you parent out, primarily based on choices and surfing records and so on, where your client phase lives.
The 2d step from this is taking the customer and engaging them in some type of communique. In the physical world you may have a person that comes to your house or is going out to a domain to do an assessment. In the digital international, the phone is your attention platform.
So that’s surely vital to wrap your head round due to the fact there are best five to ten seats at the phone for monetary apps. Whereas inside the bodily world you may have as many branches as you need, and you could send out as many humans as you like—inside the cell global there’s only 5 seats you may take. It’s extremely crucial for monetary incumbents to determine out a way to stay inside those attention structures and have attributes which are native to the ones interest systems.
Chatbots are one of those things. (And for me, chatbots and voice are basically the identical.) Chatbots as matters that stay either in the cellphone as a standalone app, or that live inside something like Facebook Messenger as a standalone bot. If you consider downloading Lemonade or Leo or something like that and being capable of talk in the app, that’s just a native feature of how you need to construct your customer support characteristic. We’re in a world in which most of the attention sits with the huge tech businesses, and now not with billboards or other kinds of traditional media marketing. So that’s hugely essential.
Of route, the caveat is chatbots haven’t been very powerful at replicating a human interplay. It’s in reality tough to locate the road between the human and the machine, and the negotiation of that line is where you can make or destroy the client enjoy. If you have got a consumer that’s entering your app and trying to talk about something together with your chatbot and it’s a irritating enjoy and they’d as a substitute communicate to a person, you’re certainly going to lose them. And if you don’t have an easy way to push out of that conversational flow into a human channel, again you’re just going to lose that consumer.
And then in other instances, as well as consistent with generational lines, you might have a far higher revel in with the customer that is capable of get onboarded via the cellphone, is able to get underwritten through a smartphone, is able to take a photo of their passport to get via Know Your Customer and Anti-Money Laundering compliance (KYC AML), or is capable of take a picture of the damage to their automobile and push that through to the coverage enterprise or for claims evaluation.
There’s simply a negotiation among how frustrating it's far to paintings with a chatbot as opposed to how fine it's miles in an effort to do these items robotically and quick. And I suppose that’s nonetheless being observed or explored. I’d say we don’t have a very last answer there but––in component due to the fact the underlying technology nevertheless has a number of room to move.
Amazon Alexa and Google’s AI assistants are nevertheless of their very, very rudimentary degrees, and I would I could assume the following 10 years to be these platform shifts where the massive agencies compete for being capable of do verbal exchange nicely. So that’s the primary piece––insurance income sellers and the position that they play.
I’d additionally flag the claims system. Within claims processing, there’s about 250,000 people, so the magnitude is also pretty huge. And then if you look at underwriting headcount and people who work on the models, you’re getting to about 280,000 people. There is an identical amount of opportunity for automation the use of this technology and all of the exceptional elements of the value chain.
I love your description of the cellphone as an interest platform. Thank you very lots for taking the time to speak with us these days, Lex. Some simply interesting matters to head away and reflect onconsideration on.
Wonderful, my delight.
Summary
In this episode of the Accenture Insurance Influencers podcast, we talked about:
- The distinction between automation and AI. Automation is a case of “if this, then that,” where results are nicely-defined and understood. AI is a probabilistic result from a trained neural community, deployed at system scale—in which the consequences may be unexpected.
- AI may be deployed as chatbots to interface with customers as coverage marketers do nowadays; however, there may be work to be achieved to enhance how chatbots replicate human interactions.
- Claims and underwriting are different factors in the insurance policy lifecycle where there can be opportunities to set up AI.
- In the virtual international, the telephone is an “attention platform” with constrained actual property for monetary apps. Insurers would be prudent to determine out a way to stay inside attention systems.
For more guidance on AI in insurance:
- Check out Accenture research on how AI is redefining insurance.
- Learn the significance of having a momentum mindset closer to AI.
- See why early movers are prevailing the AI race in insurance.
In the subsequent episode, Lex will speak the ethics of AI. How does bias creep into AI-pushed decisions and what can insurers do approximately it? Finally, given the big topics we’ve protected in this series—disruption, innovation, insurtech and AI, to call a few—what can incumbent insurers do to stay competitive with out sacrificing shareholder fee?
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