1. Measuring People’s Economic Resilience to Natural Disasters upon transactional data

2. Motto: “to bring the age of opportunity to everyone”
67 million customers
9,153 branches
30,958 ATMs
137,310 employees

3. Electronic payments data1 1 1 1 1 1 1 1 1 10 1 10 1 1 1
high resolution datasource
[throughout time and space]
high sample size
50%population uses a bank account in México
35% market share BBVA Bancomer
1,1·106 commercial premises accepting cards
1,500·106 registers per year in the country
20·106 Bancomer cardholders

4. Electronic payments data1 1 1 1 1 1 1 1 1 10 1 10 1 1 1
reflect facts
[vs surveys and its biases]
high availability
[at a low cost, though data science skills are required]

5. Electronic payments data1 1 1 1 1 1 1 1 1 10 1 10 1 1 1
privacy protection
[anonymized data]
[no individual activity: statistical aggregations]
highly descriptive
[customers: age, gender, purchasing power]
[stores: location, type of activity/service]

6. Previous research Territorial Analysis
commercial fabric performance and evolution [area of influence]
behavioural consumption patters [differences by sociodemography]
events impacts [new infrastructures, legal framework changes, etc.]

7. Previous research Tourism people and money flows
[usual environment definition according to digital footprint]
areas of interest and trends

8. United Nations Global Pulse and BBVA cooperation aims
Explore the potential usefulness of transactional data to describe social dynamics within the frame of data for social good initiatives
Provide new metrics about impact and resilience of people and services affected by natural disasters

9. Why? Hybrid model, both: CSR & new business model Our motivations:
Consultancy Activity and API services platform
commercial data opening for corporations
freemium access for research and education
Our motivations:
Research & Innovation, learnings
Talent catching and team engagement
Positive use cases and communication about data-based projects

10. Partnership model
Analysis conducted by BBVA team, with substantive guidance from UNGP team
Data itself never leaves BBVA Data & Analytics
Insights generated shared in aggregates, to protect privacy

11. What was the economic impact of the hurricane?
We registered economic activity through Sales payments (PoS) and ATM cash withdrawal activity
What was the economic impact of the hurricane?
What had been the normal activity without Odile?
How did people prepare?
Are different groups equally resilient?
Some messages to highlight:
Measure the impact of Hurricane Odile.
50% of the population has a bank account
25k card payments a day at BBVA Bancomer
30% of all bank account holders (=100k active clients in BCS, out of 637)
Anonymized
Dimensions: gender + income (3 levels) + categories (~50%) + origin

12. Baja California Sur Daily card payments in
Data density: 131 zipcodes were clustered into
8 clusters [using k-means algorithm]
Hurricane Odile
landfall
(Sept 15th, 2014)
Since Around 15k card transactions every day (25k if we take into account cash withdrawals in ATMs).
Large category 3 hurricane, 110kt; one of the most distructive in recent years.

13. How did we do it? -30% PoS -12% ATM
A bayesian model was used to define ‘normality’ based on the activity recorded in other states not affected by Odile
How did we do it?
30% fewer card transactions and 12% less cash withdrawn were made in the month after the hurricane.
Building normality model: Google’s Casual Impact R package:

14. The time it takes the consumption pattern to reach 90% of the normal activity
Recovery time
General recovery time: ~22 days

15. Recovery time t1 t2 t3 t4 South: 40 dias SJCabo vs 2 días Mulegé.
La Paz t4
Todos los Santos
South: 40 dias SJCabo vs 2 días Mulegé.
Highlight La Paz.
Income levels and gender.
San José del Cabo

16. What if we had found outliers in terms of resilience?
better infrastructure?
better preparation?
better reaction?
underperforming location 
overderperforming location 

17.
VIZ
Hurricane’s story.
Hand Miguel.

18. Conclusions
Transaction data can be used to estimate preparation to an announced natural disaster, its local economic impact and the recovery pace after a shock
Data could inform targeted responses to the most affected communities

19. Potential next steps
Further research is needed to understand similarities and differences in disaster recovery patterns
New tools and approaches could operationalize such insights for use on the ground; to get such, local authorities engagement is required

20. Many other SDGs can be measured through financial data