The Cash Before the Storm: Forecast-based Financing at Red Cross

With natural disasters on the rise, The Red Cross is investing in prediction and prevention to save money, time, and lives.

The apocalypse is near.

Since 1970, the number of annual disasters worldwide have more than quadrupled.[i] In 2015 alone, natural disasters affected nearly 100 million people, leaving 22,773 dead, and resulting in $66.5bn of economic damage. This number is only projected to rise.[ii]


A costly bottleneck in disaster relief

Technological advances in radars, satellites, and machine-learning have improved our ability to predict catastrophe. Yet, disaster relief efforts remain predominately reactionary – particularly when it comes to raising the funds necessary to mobilize operations. According to UN figures, more than five time as much funding is spent on disaster response than disaster risk reduction.[iv]

Consider funding operations at the International Red Cross, a global humanitarian organization and leading disaster responder. The Red Cross operates with roughly 97 million volunteers, members, and staff worldwide. Its national subsidiary, The American Red Cross, responds to more than 700,000 US disasters on an annual basis alone.[v]

The Red Cross conducts some variation the following operations when disaster strikes:

  1. Assess damage
  2. Determine tasks required to relieve damage
  3. Triage tasks
  4. Unlock funding based on determined plan
  5. Mobilize (predominately) volunteer task forces[vi]

In developed nations, where funds are readily available, this process typically flows quickly. However, fundraising faces unpredictable timing in developing nations dependent on foreign aid. The result is a bottleneck that delays relief. An example of this catastrophic bottleneck was seen in 2010, when a flood occurred on the Mono River in Togo, West Africa and it took 34 days for international funding to reach the Red Cross in Togo.[vii] 

Forecast-based financing promises to save time, money, and lives

Enter Forecast-based financing (FbF) – identified as one of the five Red Cross innovations of 2017.[viii] FbF leverages advancements in forecasting algorithms to anticipate and deploy the funds needed to trigger preventative measures before disaster strikes.

FbF operates by establishing a risk vs. cost threshold. Each threshold level is associated with a standard operating procedure agreed upon by humanitarian responders, meteorological services, and communities. Thus, forecasts with greater likelihood of disaster trigger more expensive operating procedures and vice versa.[ix]

The Red Cross pilots forecast-based-financing

In the near-term, The Red Cross has identified flood risk as a primary focus for FbF, piloting efforts in Peru, Bangladesh, and West Africa.[x] Flood risk was selected as the focus for several reasons:

  • Existing self-learning algorithm: The Red Cross is coupling FbF with the roll-out of their existing FUNES flood risk prediction technology.[xi]
  • Relationships with International Hydropower Association (IHA): Hydropower dams collect a wealth of data that can serve as a reliable input to FUNES.[xii]
  • Closing a communication gap: Hydropower plants can often predict floods, yet they face communication gaps between operators and affected communities. The Red Cross aims to overcome that gap with FbF.[xiii] 

In September, 2016, one of these early pilots paid-off. Using FUNES, the Red Cross anticipated a dangerous rise in the Nangbeto river dam and triggered funds to deploy operating procedures across 30 downstream communities.  By the time the dam threatened to overflow, the Red Cross had successfully distributed cholera prevention hygiene kits, waterproof shelters, live radio spots, and other evacuation supplies. [xiv]

Future outlook

The Red Cross will continue monitoring its early pilots while simultaneously improving its predictive technology, such as FUNES, in order to further roll-out FbF around the globe. Ultimately, the Red Cross aims to extend FbF to broader disaster relief efforts, such as drought and fire.[xv]

In order to gain widespread adoption of FbF, The Red Cross will need to develop and refine two key factors:

  1. Quality data collection: the success of FbF relies on effective predictive modeling. Currently, data collection is often aggregated across volunteer sources using SMS. While crowdsourcing is cheap, it may lack accuracy at a mass scale. Increased investment in data collection techniques or partnerships with reliable data sources (i.e. IHA) will be essential to fine-tuning self-learning algorithms.
  2. Threshold alignment: Mapping the appropriate operating procedures to the right funding and risk thresholds is key. This will require actively evaluating the successes and failures of FbF in its early implementation and adjusting future models accordingly. In order to do so, communication and collaboration between communities, humanitarian leaders, and experts will be essential on an ongoing basis.

The Red Cross faces a critical junction: increased pressure to deliver global disaster relief yet increased technological opportunities to help address such demands. Is FbF the best digital investment to make in order to address bottlenecks in the disaster relief process? What additional efforts must The Red Cross consider in order to convince its global partners to support FbF on a mass scale?


(785 Words)


[i] “Weather-related Disasters are Increasing,” The Economist, August 29, 2017,, accessed November 12, 2017.

[ii] “2015 Disasters in Numbers,” The United Nations Office for Disaster Risk Reduction, January 25, 2016,, accessed November 13, 2017.

[iii] “Weather-related Disasters are Increasing,” The Economist.

[iv] Jane Lubchenco, Jack Hayes, “New Technology Allows Better Extreme Weather Forecasts,” Scientific American, May 1, 2012,, accessed November 12, 2017.

[v]“What we Do,” The American Red Cross,, accessed November 13, 2017.

[vi]Juan Bazo, “Implementing Forecast-based Financing Mechanism in Peru,” Columbia University,, accessed November 11, 2017.

[vii] Joshua Hill, “Red Cross Develops Innovative Mechanism to Predict & Prepare for Flood Risks,” Clean Technica, March 28, 2017,, accessed November 11, 2017.

[viii] “How to smartly utilize the window between forecast and hazard,” IFRC, February 1, 2017,, accessed November 11, 2017.

[ix] Ibid.

[x] Ibid.

[xi] Hill, “Red Cross Develops Innovative Mechanism to Predict & Prepare for Flood Risks.”

[xii] Ibid.

[xiii] Ibid.

[xiv] Ibid.

[xv] Jaime Catalina, “Forecast-based Financing,” Understand Risk,, Accessed November 11, 2017.


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Student comments on The Cash Before the Storm: Forecast-based Financing at Red Cross

  1. I am in agreement that the FbF is a valuable tool for predicting where storms will hit, but I think the Red Cross needs to adjust their fundraising and relief efforts to align with this model. Based on my knowledge of the Red Cross from working there as a consultant for a year, one of the biggest problems that the organization has is that people are extremely generous in donating to specific disasters, which the Red Cross can then only spend on that disaster. As many have pointed out, including the following Slate article, this often leads to the Red Cross squandering funds and a lack of transparency surrounding where the millions of dollars they typically raise have actually gone.

    Rather than fundraising on an almost purely reactive basis, they need to do more proactive fundraising for general disaster relief in keeping with this new predictive approach. One issue with a “proactive” fundraising model is that it makes them the bearer of bad news, but as disasters related to climate change occur more frequently people will come to better understand the need for these broad support measures.
    They then also need to further standardize their approach to disaster relief efforts, either using funds exclusively for immediate emergency care and giving the remaining funds to local organizations better equipped to handle long-term rebuilding, or expanding their expertise to include ongoing rebuilding support.

    1. Thanks, Francie! So fascinating you worked with the Red Cross for a year – I completely agree with your concerns around a lack of transparency. This particularly rings true when funds are proactive and therefore less directly or immediately tied to a specific disaster. That is a communication challenge that will be difficult but critical to solve!

  2. The statistics on the increase of natural disasters in recent years are truly striking. Having organized fundraisers for the Red Cross in response to past natural disasters, such as the devastating 2010 earthquake that struck Haiti and caused over 300,000 deaths, I am a big believer in the value that the Red Cross provides in saving lives. [1]

    Unfortunately, people are much more apt to donate reactively than proactively. The article indicates that “more than five times as much funding is spent on disaster response than disaster risk reduction.” It is much easier for people to give money to help with relief of a disaster that has already occurred than give money to a disaster that might occur in the future. Forecast-based financing presents a tremendous opportunity for funding pre-emptive relief efforts, as this article discusses in detail. But I would argue that these predictive algorithms could be better used to trigger evacuations of regions anticipating natural disasters, similar to mandatory evacuations imposed in the U.S. immediately preceding the landfall of hurricanes. I think these tools used in combination (to preemptively deliver relief supplies and to sound evacuation alarms) will ultimately have the most impact and save the most lives.

    [1] Richard Pallardy, “Haiti Earthquake of 2010”, Encyclopædia Britannica, June 28, 2017,, accessed November 2017.

  3. Great article! While FbF is a great concept, I wonder about its compatibility with the markets and environments disaster relief situation tend to arise in. On a central or regional level, Red Cross could certainly benefit from improving their demand forecasting so that there are less stock-out and wastage situations. While FbF can help with forecasting, it won’t necessarily lead to better outcomes because of challenges with rapid last-mile delivery logistics. Given poor road infrastructure, getting products where they need to be rapidly is costly. Given limited funds, critics might argue that deciding to actually deliver products should be contingent on an actual disaster having occurred. Otherwise many of the gains from improved upstream supply chain via demand forecasting may be offset by wastage of transport dollars and expired products that may have to be recollected if unused.

  4. Ginny, thanks for sharing this on such an important and fascinating concept. I completely agree that this is a system that absolutely should be implemented. Wish that we had this in New Orleans back in 2005.

    One of the additional challenges that I think the Red Cross will need to address is the marketing and communication of the FbF system with the public. Given that ‘Contributions’ constituted ~23% of the American Red Cross budget and presuming the 5:1 reactive spend to proactive spend ratio rings true for contributions, a shift toward more proactive spend may challenge 19% of the funding base and cause the program to be unable to generate adequate funding necessary to invest in the data collection improvements that you mention.[1] I fear of the instance when the Red Cross acts proactively to reduce the impact of a disaster that their algorithms get incorrect, given the negative press during Hurricane Harvey surrounding the Red Cross’s ineffectiveness during the 2010 Haiti earthquake.[2]

    If the Red Cross plans to further the FbF program, I believe that it needs more adequate marketing to show its positive impacts in disasters that have occurred. I had never heard of the program and certainly hadn’t heard of the differences it made in Togo, Peru or Bangladesh. I think that building awareness will be key to ensuring continued funding for the program and of the Red Cross in general.

    Relatedly, I completely agree that threshold alignment and standardized operating procedures are key to the program’s viability. However, I think that they need to take it a step further and be more transparent regarding those thresholds, as I could not find them as I looked online. By providing the public with its procedures and making the public aware of the benefits that the program entails, I believe that they will be able to more effectively mitigate the risk of reduced donations and may entice more people to donate proactively to the cause, as Francie mentions.


    1. Thanks for your response. I absolutely agree that marketing will be key here. As Francie mentioned, people are prone to spending on specific disasters, which makes it harder to build the in-the-moment fundraising impact for a broad, anticipatory program. The way the Red Cross communicates the need for FbF will be essential to its success. More broadly, the Red Cross faces skepticism in how it spends its dollars, so transparency will be vital in proving ROI and closing-the-loop with key donors.

  5. Thanks for your post, Ginny! Your article initially reminded me of the IBM Watson case that we read in class – FbF relies on a large supply of historical data to inform its predictive capabilities.

    I agree that FbF could revolutionize the way that disaster relief is delivered. A key risk however is when the predictive capabilities of FbF fail or overestimate the severity of a disaster and too many supplies and funds are delivered to a specific region. In this case, it could be costly to bring those supplies back. However, given the increasing frequency of disasters and their severity, I believe that it is better to be overprepared rather than not having enough support in a potentially devastating situation.

    In response to your question about how the Red Cross can convince partners to support its FbF efforts, I think that cost-benefit analyses will prove valuable. If you can show how providing aid in advance can actually reduce the long-term costs of supporting the region post-disaster, governments and other organizations will be more likely to partner.

    In the article linked below, I found another successful example of FbF at work. The Red Cross provided aid to Bangladesh before a major flood which allowed locals to buy food for themselves and fodder for their cattle. After the floods, the Red Cross found that the number of people forced to sell their cattle or other assets to cope with the flood was lower.

    1. Thanks for the response – this absolutely tied back to Watson, and I think we will hopefully see how such platforms are starting to align quite well with use-cases that have significant impact in the world.

      I love the Bangladesh example. Continued data collection on the success of FbF will be critical in making the case for widespread adoption!

  6. Great article Ginny!

    I had personally not heard about FbF being applied in the Red Cross and I believe it makes absolute sense. As pointed out by other commentators, a big challenge in my view will be to educate the public to this method of contribution and getting everyone to understand this is a much more effective way of investing money into relief aid than a more classic reactionary approach. In addition to this challenge, the second one you pointed out in your article, making sure the preventive operations designed and funded are aligned with predictions of disaster costs, should be a key focus for the Red Cross.

    Another point I would encourage the Red Cross to think about is how to re-route aid in the cases where this alignment does not happen. In other words, what should Red Cross do in the cases where, fortunately, the predicted disaster is not as severe as expected or it does not happen at all and aid has already been deployed? How should that potential waste of resources be avoided and how would logistics work in a cost-efficient way for these resources to be re-deployed in some other area of disaster?

  7. Thanks Ginny for your post! The FUNES technology is an incredibly powerful way of using data to mitigate damage due to natural disasters. While its critical for The Red Cross to leverage digitalization trends to direct solutions toward emergency preparedness, it could be even more impactful to raise funds to support structural investments to help mitigate the severity of disasters. For example, investing in structural improvements to poorly-built houses, hospitals and other buildings in areas prone to earthquakes could save lives in the event of an earthquake. (Source: As a another example, mangroves could be planted to mitigate the threat of tidal surges. (Source:

    In both these instances, digitalization could help by collecting data on disaster prone areas, including frequency and magnitude of natural disasters, as well as structural elements in place to mitigate effects of disasters. Similarly to unlocking funding for operating procedures of disaster response, such digitalization could be used to inform funding for these infrastructure and environmental improvements.

  8. Ginny, thank you for such an interesting article! It is great to see that the Red Cross is turning to data analysis to improve their operations, become more proactive and allocate resources better.

    I think that models such as FbF can actually contribute a great deal towards increasing transparency and to increase the appeal for donations. The traditional fundraising method is based on trust. Because money is only raised after a disaster has hit and there is an immediate need for action, people feel compelled to donate money to a trusted organization with the expectation that they will identify the best relief methods and allocate funds appropriately to help as fast as possible. Unfortunately, the transparency of these allocations is not as high as it could be, leading to some skepticism about whether the Red Cross and other relief organizations are really doing a good job [1]. This could mean that funds will be harder to collect in the future and, therefore, communication should be improved to turnaround the situation.

    This is why I believe focusing on prevention activities and utilizing data models to predict disasters will help regain some of this trust. As other other colleagues have already identified, raising funds for preventive measures is certainly more challenging. Yet, when you combine this appeals with an informed plan, backed with modelling and data, it seems to be more transparent than current campaigns. Transparency allows donors to hold the Red Cross accountable and therefore, to trust them. The Red Cross is therefore incentivized to keep improving these models, which is a win-win situation.

    I believe that the Red Cross could seek partnerships with high-tech companies to help them establish a good program for data collection and for improving the algorithms faster. At the same time, this data-driven approach will be very compelling when seeking development funds from countries and Intergovernmental Organizations, given that it will hopefully have positive results from the pilots.

    I am looking forward to seeing the evolution of this model!


  9. Really enjoyed reading this article on an increasingly important topic!

    I initially thought that this article was going to be about climate change and the increasing number of natural disasters, but it was actually about using technology to aid in the relief efforts, even better! This is the first I have read of FbF or FUNES, to be able to predict and deploy funding is a great social use of technology. One limit of FbF seems to be it’s reliance on FUNES. Perhaps FbF can become even more impactful if other inputs like FUNES can be designed. Perhaps a system that partners with seismologist to predict severe earthquakes? I also wonder if prediction systems like FbF could be linked with relief efforts after the natural disaster. FbF predicts and deploys capital to fund the relief effort pre-disaster, but could there be advancements made in the physical deployment of the aid that FbF is funding post-disaster? I would think technology can play an even greater role in the post-disaster relief. With the increasing number of natural disasters, the opportunity to make a positive change in relief efforts is high. I look forward to seeing future ways the Red Cross will use technology to aid the efficiency of their relief efforts.

  10. Good to learn more about the Red Cross, and the nuances behind FbF. Curious: Do you personally believe that FbF is the best option for every market? I do believe it is imperative to bake in some degree predictability and nuance into operations so that every region is not given an equal likelihood for every disaster – an impossible thing to manage, and an inefficient way to allocate resources. However, my mind does jump to incidences where natural disaster came out of left-field: would the reliance on FbF hamper efforts to fundraise and provide immediate relief in those events? I also wonder if you believe the Red Cross does have fantastic, data-based algorithms on which FbF is grounded, should they open that data up and publicize their findings to prompt other entities (NGOs, Governments, Non-Profits) to mobilize around the regions they deem most at risk?

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