Making Risk Flow | The Future of Insurance
Making Risk Flow | The Future of Insurance

Unlocking Hidden Opportunities in Advanced Climate Modelling | Dave Wood, JBA Risk Management

03 February 2026 34:34 Cytora

Listen to episode

About this episode

In this episode of Making Risk Flow: Exploring the Ecosystem, host Jake Harding speaks with Dave Wood, Managing Director at JBA Risk Management, about how AI and deep learning are reshaping catastrophe risk modeling. Dave explains how neural networks can simulate extreme climate scenarios that fall outside historical records, helping insurers better understand compound and tail risks. 

The conversation also explores why model transparency and rigorous validation are essential as AI adoption accelerates, and how insurers can scale advanced weather simulation without sacrificing accuracy. Dave also shares practical guidance on integrating best-in-class AI tools into existing risk platforms, rather than building everything from scratch  in-house. 

Fan Mail: Got a challenge digitizing your intake? Share it with us, and we’ll unpack solutions from our experience at Cytora.

To receive a custom demo from Cytora, click here and use the code 'Making Risk Flow'.

Our previous guests include: Bronek Masojada of PPL, Craig Knightly of Inigo, Andrew Horton of QBE Insurance, Simon McGinn of Allianz, Stephane Flaquet of Hiscox, Matthew Grant of InsTech, Paul Brand of Convex, Paolo Cuomo of Gallagher Re, and Thierry Daucourt of AXA.

Check out the three most downloaded episodes:

  • The Five Pillars of Data Analytics Strategy in Insurance | Craig Knightly, Inigo
  • 20 Years as CEO of Hiscox: Personal Reflections and the Evolution of PPL | Bronek Masojada
  • Implementing ESG in the Insurance and Underwriting Space | Simon Tighe, Chaucer, and Paul McCarney, Moody's

Want to find AI jobs?

Join thousands of AI professionals finding their next opportunity

We respect your inbox. Unsubscribe at any time.

© 2026 Making Risk Flow | The Future of Insurance. All rights reserved.

Common Questions

Frequently asked questions

Quick answers about how DevFound's AI matching, resumes, and referrals work.

DevFound's AI Copilot ingests your profile, goals, and live job data to deliver curated matches in seconds. Every match includes a resume variant, suggested referrals, and interview prep so you can act immediately. The more feedback you provide, the sharper the Copilot becomes.

AI-led job searches shrink the hours spent sifting through boards and formatting resumes. DevFound pairs automation with your personal outreach, so you reserve energy for interviews and negotiation. Traditional networking still matters, but AI gives you a lift before you even send a message.

Modern AI roles expect comfort with production-grade code, data fluency, and practical ML tooling. The strongest candidates pair deep technical chops with storytelling—translating model impact to product, GTM, and exec partners. Continuous learning keeps you ahead as stacks evolve.

DevFound rewards active seekers. Keep your profile fresh, respond to match quality prompts, and enable alerts so you never miss a role. The AI prioritizes companies and teams that align with your feedback, accelerating both introductions and interview invites.

High-density tech hubs continue to host the deepest AI talent pools, yet distributed teams are catching up fast. Use DevFound filters to hone in on onsite, hybrid, or fully remote roles and watch openings expand across time zones.

DevFound aggregates thousands of remote AI openings and flags the nuances—core hours, async culture, and visa needs—up front. The Copilot also recommends how to position your distributed work experience so hiring managers know you can thrive on a remote team.