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

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

Electronic payments data 1 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

Electronic payments data 1 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]

Electronic payments data 1 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]

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.] http://senseable.mit.edu/urban-lens/

Previous research Tourism people and money flows [usual environment definition according to digital footprint] areas of interest and trends https://public.tableau.com/profile/bbva.data.analytics#!/vizhome/DashboardSectur_20161121_public/DashboardRivieraMaya

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

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

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

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

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 2014. 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.

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: https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/41854.pdf

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

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

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

www.bbvadata.com/odile VIZ Hurricane’s story. Hand Miguel.

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

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

Many other SDGs can be measured through financial data