US 11,749,070 B2
Identification of anomalies in an automatic teller machine (ATM) network
Ashok Kumar, Plano, TX (US); Jessica Boettner, Plano, TX (US); Kenneth M. Fischer, Austin, TX (US); Prabhakar Rao Bolleddu, Plano, TX (US); Carl Parziale, Charlotte, NC (US); and Lakshmipriya Varada, Allen, TX (US)
Assigned to Bank of America Corporation, Charlotte, NC (US)
Filed by Bank of America Corporation, Charlotte, NC (US)
Filed on Apr. 16, 2020, as Appl. No. 16/850,649.
Prior Publication US 2021/0327223 A1, Oct. 21, 2021
Int. Cl. G06Q 20/40 (2012.01); G07F 19/00 (2006.01); G06F 17/18 (2006.01); H04L 9/40 (2022.01)
CPC G07F 19/209 (2013.01) [G06F 17/18 (2013.01); G07F 19/206 (2013.01); G07F 19/211 (2013.01)] 18 Claims
OG exemplary drawing
 
1. A computing platform for monitoring an automatic teller machine (ATM) network, comprising:
at least one processor;
a communication interface communicatively coupled to the at least one processor; and
memory storing computer-readable instructions that, when executed by the at least one processor, cause the computing platform to:
receive, corresponding to an ATM fault code and for the ATM network, observed fault volumes for a first set of time intervals;
build, based on the observed fault volumes for the first set of time intervals, a seasonal autoregressive integrated moving average with exogenous factors (SARIMAX) model of fault volumes, wherein the building the SARIMAX model comprises:
generating a plurality of parameter combinations corresponding to a plurality of SARIMAX models;
building, based on the observed fault volumes in the first set of time intervals, the plurality of SARIMAX models, each associated with a corresponding parameter combination;
calculating Akaike information criterion (AIC) values associated with each of the plurality of SARIMAX models;
determining an intermediary SARIMAX model, among the plurality of SARIMAX models, with a lowest AIC value;
determining, based on the intermediary SARIMAX model, that observed fault volumes in a first subset of time intervals, among the first set of time intervals, are outliers;
generating corrected observed fault volumes for the first set of time intervals by replacing observed fault volumes in the first subset of time intervals with corrected values, and
using, for building the SARIMAX model, the corrected observed fault volumes for the first set of time intervals and a parameter combination associated with the intermediary SARIMAX model;
determine, based on the SARIMAX model, predicted fault volumes for a second set of time intervals;
receive observed fault volumes for the second set of time intervals;
determine, based on the predicted fault volumes for the second set of time intervals and the observed fault volumes for the second set of time intervals, that one or more of the observed fault volumes for the second set of time intervals are anomalous; and
perform, based on the determining that the one or more of the observed fault volumes in the second set of time intervals are anomalous, a remedial action associated with the ATM fault code, and wherein the ATM fault codes comprise an indication of a network issue, a software issue, a mechanical issue, an inoperable cash unit issue, and a check deposit issue.