Big data to improve index insurance products

Parallel session 2: Big data to improve index insurance products

For insurance to help smallholder agriculture adapt, at scale, to variable and changing climates, it must address several challenges, including:

  • Developing indexes (e.g. multi-scale) that capture a sufficient proportion of the climate-related risks important to smallholder farmers;
  • Deploying supporting M&E systems that reduce information asymmetries, transaction costs, basis risk and moral hazard;
  • Adopting efficient and scalable approaches that tailor insurance products to local needs and contexts.

It will be difficult for an insurance program to scale to tens of millions if it still relies on face-to-face interaction to capture farmers’ input into product design, on expensive ground-based methods for claims settlements, or on individual spreadsheets to administer customer contracts.

This session explores what “big data” approaches offer for addressing these challenges. The overlay of meteorological datasets, remotely sensed crop and rangeland health, farm & agronomic survey data, customer preferences and supply chains leads to a much more nuanced approach to climate risk management, especially as databases develop over time.  It can allow holistic product bundles tailored to the needs of different kinds of farmers, better understanding of climate risk for index design, or much more effective integration between insurance customers and other value chain stakeholders in a growing smallholder contract farming context.  Insurance can act as an incentive for farmers to share vital information about planting dates, seed types and other agricultural practices known to control the largest fraction of yield variability in smallholder settings. 

However, both practical and ethical questions remain. What is the most appropriate data to collect, hold and use, and by whom?  What are the regulations and privacy implications of big data for insurance?  What are the most effective methods of data collection and proven case studies for big data in agricultural insurance? This session seeks to discuss and shed light on some of these issues.

Chair/Moderator: Jim Hansen, Flagship Leader, Climate Risk Management, CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS)

Speaker: Pierre C. Sibiry Traore, Director of Research and Development, Manobi

Rapporteur: Pramod Aggarwal, Regional Program Leader for South Asia, CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS)

About the parallel sessions:

There will be 3 breakout parallel sessions that will provide opportunities to focus discussions around partnerships and pathways for tackling the big challenges surrounding the following themes:

  1. Connecting insurance with farmers’ needs
  2. Big data to improve index insurance products
  3. Connecting insurance to climate-smart technologies and practice

Each of the parallel sessions will be structured as interactive sessions, where both presenters and audiences are given the opportunity to interact, collaboratively explore and have in-depth discussions surrounding the theme of the session.

The interactive session will follow the ‘World Café’ format, which is designed to mimic a café environment to enable a flow of conversation between participants. The session consists of four meeting stations (roundtables) with a designated host on each table. The role of the host will be to introduce the topic of the table and also summarize the discussions taking place. Participants are divided into four subgroups and will rotate round the room, spending approximately 10 minutes at each of the stations. Each group is invited to discuss and write their comments on post-its that they position and organize on the flipcharts provided.

See the event page on the CCAFS website: Scaling up agricultural adaptation through insurance