UDSKRIFT 🔗
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Welcome back to the TX Show. I'm John, and with me, as always, is Rakesh.
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Today, we're diving into one of the most exciting shifts in the insurance landscape,
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how agentic AI is redefining parametric insurance and helping carriers better manage climate risk.
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If you're listening to this, you probably don't need convincing that climate
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events are becoming more frequent, more severe, and frankly, more expensive.
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Floods, wildfires, heatwaves, storms, these are no longer once-in-a-century events.
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And that reality is putting insurers under a lot of pressure to do three things at once.
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One, respond faster when events happen.
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Two, price smarter before they happen. And three, deliver payouts with precision
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when customers need it the most.
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That's exactly where agentic AI steps in.
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It's not just tuning risk models a little bit. It's fundamentally changing how
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insurance operates, especially when you combine it with parametric triggers.
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Rakesh, set the stage for us. Why is this such a big deal right now?
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Thanks, John. If you think about
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traditional insurance, it has always been reactive. The event happens.
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The customer reports a claim. Someone assesses the loss, collects documents,
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and then the carrier decides whether and how much to pay.
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That workflow is slow, it's manual, and in a big catastrophe,
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it's often completely overwhelmed.
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With agentic AI and parametric insurance, we're seeing a paradigm shift.
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Instead of waiting for someone to prove a loss, insurers can predict,
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validate, and trigger payouts automatically based on predefined parameters,
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things like rainfall levels, wind speed, temperature thresholds,
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or even river height at a particular location.
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So if we know, for example, that wind speeds above 120 miles per hour in a defined
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radius almost always lead to a certain level of damage,
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you can build a product where once that parameter is met, the event is confirmed
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by AI using trusted data sources, and the payout is triggered automatically.
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No lengthy claims process, no ambiguity about whether a loss is covered,
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just speed, transparency, and trust.
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And that's such an important point, trust.
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When people are dealing with a flood or a storm or a heat wave that disrupts
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their business, what they remember is not your risk model.
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They remember, did my insurer show up for me quickly, fairly,
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and without putting me through pain?
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Parametric insurance plus agentic AI gives carriers a real chance to show up better.
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So let's break this down into parts. We'll start Start with AI-powered risk
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assessment, then move into automated claims of verification,
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basis risk, new opportunities, and finally the future of AI in climate risk.
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Historically, risk models were pretty static. You'd build them on years of historical
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loss data, maybe some vendor models, and you'd revisit them occasionally.
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Climate doesn't behave in nicely stationary patterns anymore.
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The past doesn't perfectly represent the future.
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Eugenic AI changes that because it brings dynamic intelligence to the table.
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It can pull in real-time data from satellites,
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IoT devices, and weather systems, combine that with historical data sets,
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and continuously refine climate risk profiles for different regions,
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segments, or portfolios.
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Rakesh, can you make this real with a use case? Sure. Let's take a coastal parametric
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cyclone product as an example.
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Traditionally, you might use historical storm tracks, historical wind speeds,
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historical loss experience, and then you design a product with a fixed trigger.
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Say, if a storm of category 3 or higher crosses this zone, we pay X.
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But with agentic AI, you can go much deeper.
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It can track live storm formation using satellite feeds, monitor wind speeds
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in near real time from multiple data providers,
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blend that with local vulnerability data, building density, construction type,
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elevation, and so on, and even factor in climate projections for that region.
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Now, the AI is not just saying Category 3 equals X. Instead,
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it helps you design hyper-precise parametric triggers.
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Different payouts for different bands of wind speed adjusted for local vulnerability
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and
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recalibrated over time as more events and claims data come in.
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That means better product accuracy, fairer pricing, and more resilient portfolios
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that adapt as the environment changes.
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Right. And that adaptability is huge. because one of the reasons parametric
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insurance sometimes gets a bad reputation is when the trigger doesn't line up
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with the actual experience on the ground.
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We'll come back to that in the basis risk section.
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But the key idea here in part one is agentic A, I turns risk assessment from a static,
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backward-looking exercise into a living learning system that continuously refines
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how you understand climate risk.
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Anything you'd add before we move to claims, Rakesh?
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Just one quick point. This also allows more frequent product updates.
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Historically, launching or tweaking a product could take months.
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With a modern AI-enabled parametric setup, you can iterate, pilot in one geography,
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learn from behavior and outcomes, and then refine your triggers or pricing for the next launch.
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That agility is critical in
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a climate environment that's changing faster than our old models assume.
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Let's talk about claims, arguably the most emotional part of the insurance journey.
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Traditionally, claims processing has been one of the biggest pain points.
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It's slow, it's heavily manual, it's costly, and in a big event,
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it can feel overwhelming for both the insurer and the insured.
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With agentic AI and parametric triggers, that picture changes dramatically.
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Imagine this. A cyclone hits a coastal city.
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Wind speeds recorded by trusted data sources exceed the agreed threshold.
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The system automatically cross-checks those readings against the policy parameters.
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And as soon as everything lines up, payouts are triggered, sometimes within
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hours, even without a traditional claim being filed.
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Rakesh. Walk us through what agentic AI is doing behind the scenes there.
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There are a few layers. Event detection.
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The AI agent monitors multiple data streams, meteorological feeds,
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satellite data, maybe even local IoT sensors.
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It identifies that a climate event matching the policy's domain,
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say cyclone or extreme rainfall, is occurring.
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Trigger validation. It checks whether the predefined parameters have been met.
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For example, wind speeds above
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X miles per hour sustained for Y minutes within this geo-fenced zone.
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Policy matching. It maps impacted locations back to covered policies and evaluates
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which policies, what limits, what payout structure apply.
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Decision and payout once everything checks out. It initiates payouts automatically.
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No manual review for standard cases and only escalates the exceptions.
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So the AI is not just detecting the event.
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It's validating it against policy terms, checking data authenticity,
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and initiating the payout.
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And that's such a radical improvement over call center, plus forms,
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plus assessor visit, plus waiting.
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For customers, particularly those who've just experienced catastrophic events,
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getting money quickly can be the difference between rebuilding versus shutting
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down, staying afloat versus defaulting, or even basic safety and shelter.
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So for them, it's not just efficiency, it's peace of mind and dignity.
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Exactly. And from the insurer's side, you also gain consistency in how similar
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events are treated, lower operational costs per claim, and richer data on how
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events unfold and how policies respond.
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That feeds back into better product design and better risk management.
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Let's move to verification, because this is where trust and regulatory comfort really come into play.
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One concern people often have with automation is, how do you know the data is
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right? And what if someone tries to game the system?
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Agentic A, I can add tremendous value here by combining computer vision,
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natural language processing, and cross-data validation.
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Rakesh, give us a sense of what that looks like in practice.
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Think of a layered defense. At the data level, the AI cross-checks multiple
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independent sources. satellite imagery, ground sensors, meteorological data.
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At the policy level, it validates that the event matches what's actually covered.
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Correct location, correct dates, correct peril.
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At the customer level, if needed, it can analyze photos, documents,
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or descriptions for consistency.
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For example, if a farmer in a parametric drought program uploads photos or descriptions,
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a Gentic AI can use computer vision to understand crop condition,
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use NLP to interpret text descriptions,
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and cross-validate with rainfall or soil moisture data for that location.
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If something looks off or inconsistent, they can flag it for human review.
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So you're essentially getting speed for straightforward cases and extra scrutiny
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for edge cases without making everyone go through friction.
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And that's critical for building trust.
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Fewer disputes, lower fraud, faster settlements, and better relationships with
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regulators because you can show your logic and your audit trail.
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Yes. And something I really like here is it frees up human adjusters.
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Instead of spending their time on small straightforward claims that could be
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automated, they can focus on complex cases, large commercial accounts,
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or situations where empathy and judgment really matter.
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So AI doesn't replace them, it amplifies them All right, time to talk about
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one of the toughest topics in parametric insurance, basis risk.
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For anyone new to that term, basis risk is what happens when the parametric
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trigger doesn't perfectly align with the actual loss experienced by the customer.
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So you can end up with a payout even when the customer didn't suffer much damage,
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or worse, no payout even though the customer clearly experienced a loss.
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That's where parametric covers have historically been criticized.
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Rakesh, how does Agenic AI help with this? First, it helps by reducing the gap
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between the trigger and the actual experience.
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Agentic AI can continuously learn from live event data, compare that against
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claims, outcomes, and customer feedback, and then recalibrate triggers over
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time. Let's take a simple example.
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Suppose you have a rainfall-based parametric product. The trigger is less than
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X millimeters of rain over Y days, but you observe that farmers are still reporting
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losses when rainfall is slightly above that threshold due to local factors—soil type,
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irrigation issues, microclimate, and so on.
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Agentic A, I can discover patterns in, where mismatches occur,
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what local variables seem to matter, and how often payouts didn't match the actual experience.
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It can then suggest adjusting thresholds for specific microregions,
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layering in additional parameters like soil moisture or temperature,
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or revising payout curves to better match reality.
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Over time, the models evolve and the basis risk shrinks.
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So we move from a one-size-fits-all threshold to something a lot more nuanced
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and data-driven, and that leads to fairer and more reliable experiences for
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both insurers and policyholders.
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Because frankly, if customers keep feeling like the trigger doesn't represent
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their reality, they won't renew. Exactly.
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And it also helps regulators feel more comfortable because you can demonstrate
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how you designed the triggers, how you're monitoring performance,
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and how you're improving them based on evidence.
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That's a much more compelling story than we set the threshold once and never revisited it.
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Let's talk about the upside, new business opportunities. Parametric insurance
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powered by Agentic A, I is not just about doing the same thing a little bit better.
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It's expanding the boundaries of what's insurable.
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We're seeing opportunities in segments like smallholder farmers,
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renewable energy operators, tourism and hospitality supply chains vulnerable
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to climate volatility, and even public sector risk transfer and resilience programs.
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Rakesh, pick one or two examples and walk us through.
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Let's start with smallholder farmers. In many markets, they're highly exposed
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to climate risk, often underinsured or entirely uninsured and difficult to serve
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through traditional indemnity products because admin costs are high and data is limited.
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Parametric products combined with agentic AI allow you to use satellite imagery
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and weather data to assess drought or flood events, set simple transparent triggers,
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and push payouts directly to farmers' mobile wallets when conditions are met.
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AI helps by
scaling this across millions of small policies, continuously learning
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which triggers work best.
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In each region, and optimizing portfolio performance.
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That's financial inclusion plus sustainable business. Another example, renewable energy.
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Wind farms, solar parks, hydro plants. They all depend heavily on weather.
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Parametric covers can protect revenue when wind speeds are too low or too high,
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output when solar radiation is below expected levels, or water levels for hydro plants.
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Agentic AI ingests real-time performance data, correlates it with weather conditions,
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helps design covers that align
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with actual revenue risks, and automates payouts when deviations occur.
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You get products that are not only innovative, but also scalable and manageable
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from a risk perspective.
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That's such a powerful narrative. It's not just innovation for efficiency sake,
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so it's innovation with impact.
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You're enabling coverage for segments that were traditionally seen as too risky
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or too expensive to serve with traditional indemnity insurance.
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And for carriers, that's also a way to differentiate in the market and build
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new partnerships and align with sustainability and ESG goals. Let's look ahead.
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Agentic A. I won't just influence underwriting or claims in isolation.
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It will reshape how insurers think about capital allocation,
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reinsurance structures, and overall portfolio resilience.
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Rakesh, how do you see this playing out over the next few years?
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I see three big shifts from static to dynamic portfolios.
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Instead of setting risk appetite once a year, carriers will use AI to continuously
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monitor climate exposure and adjust portfolios in near real-time,
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by region, by product, by segment.
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Smarter reinsurance and risk transfer. At Gentic A, I will help carriers structure
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parametric covers, not only for end customers, but also for their own reinsurance and ILW programs.
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They'll be able to simulate scenarios
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more accurately and negotiate better terms backed by robust data.
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Integrated resilience strategies. Insurers won't just be payers of claims.
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They'll be partners in
resilience, advising governments, businesses,
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and communities on how to mitigate risk.
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Parametric Plus A becomes a tool for planning, not just for repairing.
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There's also a capabilities and governance angle here.
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If you want to be an AI-first insurer, especially in climate risk,
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you need the right data foundations, clear model governance at the right talent
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mix, actuaries, climatologists, data scientists, engineers, business leaders,
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and strong ethics and transparency frameworks.
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Because these are powerful tools, you want to make sure they're used in ways
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that are, one, fair, two, explainable,
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and three, aligned with your brand promise. I completely agree.
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The key message for insurers is climate risk is permanent, but your ability
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to manage it doesn't have to be static.
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With agentic AI, you can move from reactive, manual fragmented approaches to
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something predictive, adaptive, and responsible.
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It won't happen overnight, but the ones who start now will be in a much stronger
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position a few years down the line. That brings us to the end of today's episode of the TX Show.
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We've explored how agentic AI is transforming parametric insurance,
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making payouts faster, making them fairer, and making them more predictable
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in the face of climate change.
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We talked about AI-powered risk assessment, automated claims and verification
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of mitigating basis risk, unlocking new business opportunities,
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and what the future of AI and climate risk might look like.
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If you're an insurer, an MGA, a broker, or even a regulator listening to this, the takeaway is simple.
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Parametric insurance plus agentic AI isn't science fiction anymore.
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It's a practical toolkit you can start experimenting with today.
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Join us next time as we discuss what it truly means to become an AI-first insurer
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and how forward-thinking carriers are already getting there in the real world.
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Rakesh, thanks for another great conversation. Thanks, John.
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And thanks to everyone listening.
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If you found this helpful, share it with a colleague who's thinking about climate
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risk or parametric products. We'll see you in the next episode.