They are drawn from 30 universities across the country Kenya Re Group Managing Director Dr Hillary Wachinga/HANDOUT Kenya Reinsurance Corporation is turning to artificial intelligence to improve flood risk assessment as insurers face potentially heavy losses from climate-related disasters.
It has brought together 90 university students to develop an artificial intelligence-powered catastrophe modelling solution, aimed at strengthening flood risk assessment and improving decision-making in the reinsurance business. The students, drawn from 30 universities across Kenya, are participating in the third Kenya Re AI4I Hackathon, which runs from October 7 to 9, 2026, under the theme “Redefining Reinsurance Business Processing with Agentic AI and Machine Learning.” The initiative seeks to harness emerging technologies to improve how the state-owned reinsurer assesses risks, processes business and makes underwriting decisions, while building a pipeline of technology talent for the insurance industry.
Kenya Re Group managing director, Hillary Wachinga, said the programme was established to strengthen collaboration between universities and industry, nurture emerging talent and develop technological solutions to business challenges. “The programme was established with a clear purpose to strengthen the connection between academia and industry, identify and nurture emerging talent, and harness new technologies to improve the way we do business,” Wachinga said in a speech delivered on his behalf by acting general manager for reinsurance operations, Paul Ahomo.
The students will apply artificial intelligence (AI) and machine learning to develop a flood catastrophe model for some of Kenya Re's key markets. The solution is expected to combine catastrophe data and analytical tools to help the corporation better understand flood exposure and potential financial losses.
Catastrophe modelling is particularly important in reinsurance because a single flood can affect multiple properties and businesses in the same geographical area, exposing insurers and reinsurers to substantial claims. The models combine information on hazards, the vulnerability of affected assets, insured exposure and potential financial losses to support risk assessment, underwriting and risk management.