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Big data sources provide a significant opportunity for governments anddevelopment stakeholders to sense and identify in near real time, economicimpacts of shocks on populations at high spatial and temporal resolutions. Inthis study, we assess the potential of transaction and location based measuresobtained from automatic teller machine (ATM) terminals, belonging to a majorprivate sector bank in Indonesia, to sense in near real time, the impacts ofshocks across income groups. For each customer and separately for years 2014and 2015, we model the relationship between aggregate measures of cashwithdrawals for each year, total inter-terminal distance traversed by thecustomer for the specific year and reported customer income group. Resultsreveal that the model was able to predict the corresponding income groups with80% accuracy, with high precision and recall values in comparison to thebaseline model, across both the years. Shapley values suggest that the totalinter-terminal distance traversed by a customer in each year differedsignificantly between customer income groups. Kruskal-Wallis test furthershowed that customers in the lower-middle class income group, havesignificantly high median values of inter-terminal distances traversed (7.21Kms for 2014 and 2015) in comparison to high (2.55 Kms and 0.66 Kms for years2014 and 2015), and low (6.47 Kms for 2014 and 2015) income groups. Although nomajor shocks were noted in 2014 and 2015, our results show that lower-middleclass income group customers, exhibit relatively high mobility in comparison tocustomers in low and high income groups. Additional work is needed to leveragethe sensing capabilities of this data to provide insights on, who, where and byhow much is the population impacted by a shock to facilitate targetedresponses.

Assessing the use of transaction and location based insights derived  from Automatic Teller Machines (ATMs) as near real time sensing systems of  economic shocks