Climate Meets Code: Chubb Announces Winners of the InnovateHER AI-ML Challenge 🏆
After a rigorous evaluation round, Chubb has announced the top three winners of the InnovateHER AI-ML Hiring Challenge, organised in partnership with MachineHack. The hackathon focused on GenAI applications for climate risk intelligence, drawing participation from some of the most innovative women in the AI and machine learning field. Meet the Winners: Each participant built GenAI-powered solutions tackling real-world climate risk and insurance challenges, standing out for their technical depth and practical use cases. Out of a total score of 20, the top three participants selected for interviews with Chubb are: 🥇 Vaishnavi Sonawane – Polisure (Score: 18) Sonawane’s winning project, Polisure, is a full-stack climate risk intelligence platform for underwriters, actuaries, and ESG teams. It integrates regulatory monitoring, AI-powered portfolio optimisation, and real-time risk scoring across five insurance domains. Built using LangGraph and Claude AI, the application combines climate APIs, geospatial mapping, and LLM-driven insights to deliver actionable intelligence for insurance professionals. Click here to check out the GitHub repository. 🥈 Poornima Devi – AI Agent for Insurance News & Reports Digest (Score: 17) Devi created an autonomous GenAI agent that curates and summarises the latest updates in climate risk and InsurTech. Built using LangChain, Gemini Flash, and Streamlit, the tool allows users to input a topic and receive concise, relevant news and report summaries pulled from the web using Google Custom Search and scraping tools. The agent is designed to help insurers stay updated without information overload. Click here to check out the project. 🥉 Nikita Chelani – InsureClimate AI (Score: 16.5) Chelani’s submission, InsureClimate AI, is a Streamlit-based research assistant that fetches and summarises regulatory, climate, and financial news from global sources. Using Tavily API, Gemini AI, and cosine similarity analysis, it delivers credibility-scored summaries and custom risk reports. It also includes a user feedback loop to continuously improve output quality and generate downloadable PDFs for compliance and analytics teams. Click here to learn more about the project. Why Did This Challenge Matter? The InnovateHER AI-ML challenge is part of Chubb’s broader initiative to empower women developers in emerging tech fields. By building real-world applications that intersect climate risk and AI, these innovators showcased the impact GenAI can have in transforming the future of insurance. 🙌 A Big Thank You to All Participants! To every woman developer who took part, your passion and talent were truly inspiring. We hope this challenge was a stepping stone in your tech career journey.
The hackathon focused on GenAI applications for climate risk intelligence, drawing participation from some of the most innovative women in the AI and machine learning field.
The hackathon focused on GenAI applications for climate risk intelligence, drawing participation from some of the most innovative women in the AI and machine learning field.
[]
["GenAI", "machine learning", "TPU", "AI", "ML", "LangGraph", "LangChain", "Streamlit", "analytics", "R"]
["AI", "machine learning", "ML", "analytics", "GenAI", "LangChain", "LangGraph", "Streamlit", "TPU", "R"]
Since 2012, AIM has been chronicling the technological progress in artificial intelligence by highlighting the innovations, key players, and challenges shaping the future of our world. Through dedicated journalism, we promote and discuss ideas from smart, passionate, action-oriented individuals who strive to change the world.