The Great Unlock: AI/ML and Decision Management for Transformation | Episode 6


AI/machine learning (ML) and decision management: two separate innovations that are even more powerful in tandem. This phenomenon is known as the “Great Unlock” and when it comes to insurance, these elements together are unlocking, and accelerating, greater opportunities for carriers and customers. Tune in to our latest podcast where host Mark Sidlauskus, Sapiens Marketing Director, discusses the AI/ML/decision management “Great Unlock” with Rafael Goldberg, Sapiens Head of Decision, and how it is transforming the business value of today’s insurance industry.

Host: Mark Sidlauskas
Marketing Director
Guest: Rafael Goldberg
Head of Decision
The Great Unlock: AI/ML and Decision Management for Transformation
Episode 6
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The Great Unlock: AI/ML and Decision Management for Transformation

Mark Sidlauskus: Hi everyone. Welcome to the Sapiens Insurance 360 podcast. I’m your host, Mark Sidlauskas, Sapiens Marketing Director. I’m glad that you’re out there listening. This is where we discuss the latest news, trends, and issues from across the insurance solutions and technology spectrum. We have a very special guest for today’s program, Sapiens’ own Rafael Goldberg, Head of Sapiens Decision. Rafi has 20 years of experience in product strategy and delivery, sales and account management, and software implementation and adoption. Today we’ll be discussing the Great Unlock: AI and Machine Learning (ML) and Decision Management for Transformation. Rafi, welcome to the program.

Rafael Goldberg: Thanks, Mark. It’s great to be here. Great to see you.

Mark Sidlauskus: So let’s get started. For those of you listening, you may be wondering what we mean by the great unlock. So Rafi, can you shed some light on what this term means?

Rafael Goldberg: Absolutely. The Great Unlock is something I heard on a podcast that I like to tune into, the ‘All-In’ podcast. David Freedberg there with several of his cohorts was speaking about that. They always talk about AI and ML and talking about the combination of two things that by themselves are already great, but when you put them together you really get a ‘wow’ factor. And he was talking about the combination of probabilistic and deterministic. And when I heard that, now I’ll go into those terms in a minute, I just thought, wow, that is just perfect for our industry. And what we’re doing: decision. And so let’s first talk, I’ll unpack that.

Let’s first talk about the unlock and you know, marketing expert and entrepreneur Jeff Galloway explains an unlock [is] where the discovery of an accelerator for a brand, product, or service is invisible and plain sight. So from, from popular culture, you may think of Jay-Z and Beyonce amazing separately, but when you put them together, wow. And I think as you pointed out to me, you gotta love this one, peanut butter and jelly. You know, peanut butter and jelly, independently great things. But when you put them together, I think you mentioned the average school-age child has consumed 1,500 peanut butter jelly sandwiches by the time they graduate. So that’s what we’re talking about when we talk about the Great Unlock and probabilistic, everyone is is very excited about artificial intelligence and machine learning. And these are certainly, machine learning in insurance is something that’s being used often. In decision management, deterministic that is, based on a set of conditions and set of definitions. I know exactly what my decision will be based on the data that I get. Those are two things that both impact how a decision is made, but they’re kind of different sides of the same coin. And so we’ve been thinking a lot of decision, about how to combine those two capabilities to create a better outcome for the business and for the customer.

Mark Sidlauskus: Okay. That’s great point, Rafi, a great explanation. And I don’t think Beyonce beats my peanut butter and jelly, so thanks for that explanation. So what new business value do you see emerging from the Great Unlock?  What are the most compelling use cases?

Rafael Goldberg: Yeah, so I think the compelling use cases that are emerging in insurance are really across the value chain. So if you think about starting with what products are we going to create to take to market, and that is a very expensive and time-consuming process. And by using AI, ML, and decision management, we can accelerate or a business can accelerate that, which is really the name of the game. Getting new products to market fast and at less cost. So that’s the first. The second is how do you market those products to the customer? So based on characteristics of a customer and the characteristics of a product, how do I match those? That’s a great fit for, for this combination of capability. Then, how do I price and quote and underwrite and then write through the servicing process and claims and across our value chain, you can combine AI and ML with decision management, and you can turbocharge, you can really unlock those opportunities for business and for the customer.

Mark Sidlauskus: That is quite a lot. So we got product development, which is taking new products to market faster, which is essential for business agility and how to compete today. We’ve got marketing to customers, so matching the products to the right customers, and then really everything else in between. We have pricing, quoting, claims, underwriting. So it’s really hitting everything across a value chain. I mean, is this for use all across the enterprise?

Rafael Goldberg: Absolutely. And that’s one of the things that we’re excited about, is that it’s not kind of relegated to a dark corner or a basement somewhere. It’s for use by the business. And that was kind of a founding principle for decision and for decision management, was let’s take rules, which is really the underpinning of a policy and an insurance business, and unlock it, you know, shine the light on it and provide the business with tools to be able to manage that and free up IT to do the hard engineering problem instead of interpreting ambiguous requirements. And we see the exact same thing available here by combining it with machine learning and being able to provide the business with visibility into machine learning models that are combined with decision models. So all in one canvas. So, so yes, we see it really throughout the enterprise and enabled for all of the enterprise.

Mark Sidlauskus: So right now, a lot of the machine learning work is done by data scientists who don’t like to integrate with the systems. But now I guess we’re giving business analysts and other business folks the ability to do this themselves. Is that what this is all about?

Rafael Goldberg: It’s so we see that, we see the discipline of machine learning and data scientists are using Python to program those models. We see that as continuing. What we see by combining is providing business analysts in the business that are managing decision models, these deterministic models, providing them the opportunity to manage the models that the data scientists are creating in one place. And so we’re not advocating for a data scientist to go away, but we are advocating for the business to gain insight into what’s happening with those models of how they’re being combined. So it’s really doing away with the black box that I think is often, that I hear when I speak to clients. They’re concerned with machine learning models and think of it from the customers’ perspective as well, if they’re getting an underwriting decision. But it’s not clear why, you know, that’s a liability there. And so this is an opportunity to make it more transparent.

Mark Sidlauskus: Well, that’s a huge value to have, transparency and traceability and especially during an audit where you can prove to the regulators what’s actually happening and the model.

Rafael Goldberg: Exactly.

Mark Sidlauskus: What are some of the organizational challenges you would anticipate going forward? There’s always bumps in the road, what are you seeing out there?

Rafael Goldberg: Yeah. So I think for organizational challenges, it’s change management. And I think for that, it’s always an education journey. And I, I hope these kinds of discussions are helpful. So whether it’s podcasts or conferences or blogs or just being, talking about this stuff, I do think that the explosion of excitement and use of generative AI tools like ChatGPT and Bard and others is making it a lot easier. You know, nine months ago before, before the release, this was a very esoteric conversation. All of a sudden, this is a very real conversation. And so it’s become a lot easier, a lot faster. So that’s really exciting to see. So I think the organizational challenge is really one of getting on the same page. But that’s what this is all about, right?

So decision management was all about knocking down the wall, you know, the wall, where is this with the requirements over to IT and hope for the best. And IT would be frustrated by that. And, you know, decision management had reduced that factor. And I think combining AI/ML with decision management is going to be another opportunity for increase in collaboration. So I’m quite bullish and excited about it.

Mark Sidlauskus: So as you’re out there talking to customers, what advice would you give them in terms of getting started in their own unlock journey? Because they’ve already made investments in AI, machine learning, and rules management systems and what have you. What would your advice be to get them started?

Rafael Goldberg: Yeah, so I think to get started, you want to, there’s two pathways that we’ve seen be successful. One is to take an existing implementation, one a business that is not high volume or high frequency of change, so that it’s a stable area where you can take, if you’ve implemented machine learning models for that and you have rules, deterministic rules, and you can combine those and see opportunities through the value chain. So that’s one goal. And then you can run dark production against your live production and see how your results improve and where you need to make changes until you’re getting a better result. And then you can take that model and apply it again and again. The other is greenfield, where, you know, you don’t yet have, you want to stand up a new product or send up a new implementation. And that’s really an exciting opportunity, because you’re not bound by any kind of legacy situation.

Mark Sidlauskus: Should be for a stable area or a greenfield opportunity, to begin with.

Rafael Goldberg: Exactly.

Mark Sidlauskus: So this is awesome. You’ve given us a lot to think about today, especially how ML and decision management can unlock the potential that’s really hidden in plain sight. Now we can all ask ourselves how we’ll take advantage of this combination going forward. So thanks, Rafi, for unpacking such an important topic. And no doubt we’ll continue to revisit this as the industry evolves. It’s been great to have you with us today!

Rafael Goldberg: Thanks, Mark, it’s been great to be here!

Mark Sidlauskus: So to everyone listening, thank you for spending the time with us today. We love hearing from you, so if you have any comments or would like to follow us on social media, please reach out to us on our channels. We’ve got a lot of great topics ahead, so please be sure to tune in and subscribe next time to Sapiens Insurance 360.

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