Catholic Relief Services was founded in 1943 by the Catholic Bishops of the United States to serve World War II survivors in Europe. Since then, we have expanded in size to reach more than 130 million people in more than 100 countries on five continents.
For over 75 years, our mission has been to assist impoverished and disadvantaged people overseas. Although our mission is rooted in the Catholic faith, our operations serve people based solely on need, regardless of their race, religion or ethnicity.
I am William Martin, and I have more than ten years of experience as a political economist deployed in response to conflicts, natural disasters, or protracted crises. As a Technical Advisor–Cash and Markets based interventions for the Humanitarian Response Department at Catholic Relief Services (CRS), I contributed to double the portfolio of cash transfer programming, championed cash and market-based approaches across sectors particularly with Shelter, and spearheaded initiatives on digitalization of cash transfers.
I am very happy to present you CRS challenge of the day: taking advantage of Cash Delivery Data
Cash saves lives and restores livelihoods and infrastructures In the aftermath of a disaster, CRS does more than just give food, water and shelter to those directly affected. We provide life-saving help and work to restore local economies. When assessments show the local markets can support needed items or services, CRS provides people with vouchers or cash to use at local shops or organized fairs. Vouchers are redeemable for a range of items, including food, hygiene kits, shelter materials, living supplies, and agricultural seeds and tools. Cash provides people the most freedom to make choices about what their families need to survive and recover with dignity. CRS pioneered cash-based programming in 2001 with its innovative voucher agricultural fair in Uganda. In FY17, CRS and its local partners transferred more than $72 million in cash and vouchers to beneficiaries in 44 countries, more than doubling the FY16 amount of cash and voucher transfers.
Cash is changing the humanitarian assistance landscape. CRS needed to elaborate a vision In recent years, the use of Cash Transfer Programming in humanitarian assistance has grown significantly. In 2016, it is estimated that $2.8 billion in humanitarian assistance was disbursed through cash and vouchers. This represents 10% of total humanitarian aid or more than $25 billion. This percentage is shifting dramatically upwards due to recent advocacy efforts and trends in donor financing, particularly among European donors, and within the U.S. An increase up to 30% in cash response within the next few years is forecasted. At the World Humanitarian Summit (WHS) in May 2016, the U.N. Secretary General called for cash to be the default method of support for crisis-affected people where the situation allows. One of the primary commitments born of the WHS’ Grand Bargain was to increase the use and coordination of CTP in humanitarian response. Other commitments of the Grand Bargain will intersect with this commitment, particularly that of increasing investments in capacity building of local and national actors to respond, and an increased focus on the inclusion of beneficiaries in decision making.
after Typhoon Damrey made landfall, it took, on average across the 8 agencies, 62 days for provision of the first cash distribution. The fastest first cash distribution took place within 21 days, while the slowest took place 94 days after the disaster Cash transfers and vouchers—totaling $3.8 million, or 25 percent of the total mobilized response budget—reached 41,300 households (approximately 150,000 individuals) in 9 out of the 15 typhoon‑affected provinces from November 2017 to May 2018.
The problem: After a crisis, it is difficult to make decision, get the funding, and have an holistic response timely and at scale because of the low-tech data collection combined with consensus-based decision-making process of the coordination mechanisms. In a lot of sectors robots help making better and faster decisions for doctors, lawyers or investors, why not do the same for humanitarian interventions?
The Goal: Generate a more objective response analysis at the beginning of new humanitarian crisis, and limit human intervention in the decision of funding of the intervention, intervention strategy and response options, including delivery mechanism.
CRS: 300 project live with mobile data collection on more than 50 million beneficiaries 450 millions people have their data collected in developing world. Why increase of data collection in humanitarian assistance: increase digital payment and financial regulation (Know you Custumers aka KYC, Antimoney laundering and counter financing of terrorism akak AML/CFT regulations)
What king of data is collected: Verification doc (ID, diploma, certificate) Personal documents (health, bank records) Material circumstances (employment history, work adress, experience of human right violation, education) Background data (family history, etc.) Biometric and genetic data, biographical data (age, sex, race, gender, religion, etchnicity, nationality, belief, ect..) Image and recordings Corroborating material (official, unofficial report, hotline report, medical report, pshychological report) Metadata
What is the challenge: CRS is accumulating a large amount of data from beneficiaries registrations and assessment over the years. Combined with secondary data (data from other humanitarian organization publicly available, satellite imaging, etc.) all of this data could be analyzed objectively by an algorithm and turned into suggested interventions (based on previous experience in the same / similar country).
What have been done so far :CRS is in the process of standardizing and storing into the cloud its data points into a global data warehouse. CRS also uses API such as the CAT system to perform on the same platform beneficiary management, data management and transfer of humanitarian assistance. CRS is promoting innovative collaborative model with other organizations of the CCD initiative to improve coordination by revisiting and offering more efficiency to the humanitarian value chain, particularly around cash delivery. Moreover, with START, CRS is piloting a parametric insurance initiative in Senegal. Finally, CRS is exploring an area-based approach for coordination and intervention for humanitarian assistance.
Limitation: aggregation of very diverse data and labor intensive makes it very difficult (excep success for CAT with aggregation beyond project level + policy change to keep and centralize data on Gateway)
Could you list one or more user stories for the most important cases that should be solved by this solution?
For example: As a project manager, I want to know automatically each day which clinic performs below average so that I can intervene right away. As a country director, I want to see all our weekly reporting data on a single website so I can give donors better updates
Cloud industry companies to aggregate existing and new data per area of intervention Companies using algorithm for scientific-modelling based scenario used for forecasting (weather, epidemic, conflict, etc…). Technology solution provider to gather individual data (heart rates, etc…) and community level data (e.g. water level) “old school” humanitarian responders to respond where the technology is not available, or to “correct” decision suggested by roboadvisor because of the good knowledge of area of intervention and qualitative approach Government for regulatory environment and decision making on strategy of intervention (sovereignty of government in natural disaster is important. However might be different in context setting, or at least will requires better data protection/beneficiary privacy to not compromise HLP) Fintech, FSP as solutions will be more monetized Insurance companies, investors (see Insurance Linked Securities model such as parametric insurance and catbonds) Blockchain industry providing digital ID and smart contracting A bunch of lawyers and ethics committee to solve new problems these solutions will bring
Sandbox testing with existing (huge) data set available, particularly in protracted crisis. Micro testing in small area of intervention where technology is available (urban crisis) Build upon technology and experience existing in finance, insurance, medical and law sectors on roboadvisor. Standardizing data points (e.g. HXL langage) for technical feasibility Aggregating data points for technical feasibility Strengthening data protection and beneficiary privacy. If data well anonymize and minimize, then no problem to be aggregated and run through AI for analysis Discussing “ownership” and sharing of data. Serious discussion needs to happen about who has the authority to “own” data, who can responsibly safeguard data (government? NGO? UN? private sector? All but must be compartimentalized? No one?)
Challenge 1 big data and cash
TakingAdvantage of Cash Delivery Data
What do users wants?
As a person regularly affected by natural disaster, I want to receive as quickly as possible
humanitarian assistance in a “one stop shop” manner so that I am able to resume quickly
my activities and be better prepared and resilient for the next crisis
As a humanitarian coordinator, I want to be assisted by a roboadvisor to help me
identifying needs, market and other infrastructures and services capacity in close to real
time so that we can appeal for right amount of funding and adjust our intervention and
supply of the humanitarian assistance to the ever changing context of the response.
As a financial service provider, small vendor, faith-based organization -not in clusters
system-, local partner, I want to have access to close to real time context and need
analysis, and response scenario to be able to provide what people need the most at my
community and individual level directly so that I direct my service where it is needed the
most (B2B, B2B, P2P)
Building the new humanitarian ecosystem
What are the possibilities you see for better leveraging
emerging data science &AI capabilities?
• Sandbox testing
• Build upon technology and experience existing in
finance, insurance, medical and law sectors on
• Standardizing data points (e.g. HXL langage)
• Aggregating data points
• Strengthening data protection and beneficiary privacy.
• Discussing “ownership” and sharing of data.