Tracking early signals in prearranged finance for disasters

As disasters become more frequent and severe, knowing where funding is already in place is essential for effective, timely responses. Prearranged financing (PAF) is designed to enable faster, more predictable responses to disasters, by establishing clear rules for when and how funds will be released if particular trigger conditions are met. An up-to-date view of how PAF is evolving – where it’s active, where it's growing, and where there may be gaps – is crucial to help governments and funders working to strengthen disaster preparedness.

The Centre for Disaster Protection is a technical and policy advisory organisation focused on disaster risk finance. In October 2024, the Centre published its second State of Prearranged Financing for Disasters report, using IATI’s near real-time data to track evolving trends in funding. IATI data provided timely insights that complement and expand what retrospective data sources like the OECD DAC Creditor Report System (CRS) can show.

“Over the past two years, our work has shown just how powerful IATI data can be when it comes to tracking pre-arranged disaster finance” – Michèle Plichta, Senior Researcher at the Centre for Disaster Protection

In 2022, around 85% of PAF was provided by five major donors: the World Bank, German Federal Ministry for Economic Cooperation and Development (BMZ), UK Foreign, Commonwealth & Development Office (FCDO), Inter-American Development Bank (IDB) and Asian Development Bank (ADB). Each of these donors reported consistently and comprehensively to using the IATI Standard, enabling analysis of more recent trends CRS data had not yet captured.

This strong, timely reporting to the IATI Standard made it possible to extend the analysis to 2023. The research found that following large-scale disbursements in response to the COVID-19 pandemic and other shocks in 2020-21, overall PAF fell in 2022 due to fewer major triggering events, and the exhaustion of many contingent loan and grant agreements. However, IATI data revealed what slower sources could not: that renewed or expanded PAF agreements resulted in an increase in PAF provided by key donors in 2023, driven primarily by large increases from the World Bank.

To speed up and strengthen the analysis, the Centre used a machine learning model to scan IATI data for relevant transactions. Trained on CRS data, the model searched project titles and descriptions to assess whether they met the Centre’s criteria for PAF. Where the model was uncertain, records were reviewed manually. This approach made it possible to identify more relevant data points faster – enabling a more thorough view of PAF that would be difficult to achieve through manual review alone.

“Over the past two years, our work has shown just how powerful IATI data can be when it comes to tracking pre-arranged disaster finance” said Michèle Plichta, Senior Researcher at the Centre for Disaster Protection. “The ability to see what’s happening now opens the door to making more timely decisions that can result in more effective planning and preparation for disasters”.

By making this kind of timely, structured analysis possible, IATI data is helping to build a clearer, more dynamic picture of global disaster finance. The Centre for Disaster Protection’s report demonstrates the growing value of IATI as essential infrastructure that enables faster, more informed decision-making in a world where delays can cost lives. As more organisations publish timely, comprehensive data to the IATI Standard, the potential to monitor and strengthen PAF – and broader crisis response systems – continues to grow.