Quality Checks on Granular Banking Data: An Experimental Approach Based on Machine Learning?

Abstract

We propose a new methodology, based on machine learning algorithms, for the automatic detection of outliers in the data that banks report to the Bank of Italy. Our analysis focuses on granular data gathered within the statistical data collection on payment services, in which the lack of strong ex ante deterministic relationships among the collected variables makes standard diagnostic approaches less powerful. Quantile regression forests are used to derive a region of acceptance for the targeted information. For a given level of probability, plausibility thresholds are obtained on the basis of individual bank characteristics and are automatically updated as new data are reported. The approach was applied to validate semi-annual data on debit card issuance received from reporting agents between December 2016 and June 2018. The algorithm was trained with data reported in previous periods and tested by cross-checking the identified outliers with the reporting agents. The method made it possible to detect, with a high level of precision in term of false positives, new outliers that had not been detected using the standard procedures.

Keywords: banking data, data quality management, outlier detection, machine learning, quantile regression, random forests

JEL Classification: C18, C81, G21