The Impact of the Microchip on the Card Frauds

Ardizzi, Guerino (2012): The Impact of the Microchip on the Card Frauds.

Abstract

The issue of frauds through payment cards has received a great deal of attention from authorities. A large share of card frauds can be ascribed to the phenomenon of counterfeiting of debit cards, widely used payment instrument in “face-to-face” transactions. With the advent of the Single Euro Payment Area, the European banking community has shared and almost reached the ambitious goal of replacing all the cards (and accepting terminals) with chip compatible ones, which are supposed to be harder to clone than the magnetic stripe card. Using a bi-annual balanced panel data of over one hundred Italian banks, in this paper we estimate for the first time the real impact on card frauds caused by the chip card migration. The results confirm the positive effects of the new technology: the ratio between fraud and ATM-POS transactions (card fraud loss rate) is reduced significantly if the chip card is present.

• Item Type: MPRA Paper
• Language: English
• Keywords: fraud, debit card, payment instrument, security, chip, technology, prevention, EMV, SEPA
• Subjects: D - Microeconomics > D1 - Household Behavior and Family Economics > D12 - Consumer Economics: Empirical Analysis; E - Macroeconomics and Monetary Economics > E4 - Money and Interest Rates > E42 - Monetary Systems ; Standards ; Regimes ; Government and the Monetary System ; Payment Systems; C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C23 - Panel Data Models ; Spatio-temporal Models; E - Macroeconomics and Monetary Economics > E2 - Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy > E21 - Consumption ; Saving ; Wealth

References

Ardizzi G. e E. Iachini (2012). “Why are Payment Habits so Heterogeneous Across and Within Countries? Evidence From Europe and Italy, Unpublished, Banca d’Italia.

Banca Centrale Europea (2010). “Seventh single euro payments area (SEPA) progress report”, October.

Banca d’Italia (2012), “Survey on Households Income and Wealth”, January.

Banca d’Italia (2010), “Annual Report 2009”, May.

Banca d’Italia (2011), “Annual Report 2010”, May.

Beck, N. e J. Katz (1995), “What to do (and not to do) with time-series cross-section data”, American Political Science Review 89: 634–647.

Cameron C. e K. P. Trivedi, (2005), “Microeconometrics: Methods and Applications”, Cambridge University Press, New York.

Caimi C., Ghisellini R. e A. Giacomelli (2006). “Forecasting model of fraud”, unpublished Si Holding.

Capgemini (2011). “World Payment, Report 2011”. Central Office for Means of Payment Fraud, (2010). “Rapporto statistico sulle frodi con carte di pagamento”, 1/2011, Ministery of Treasury. http://www.dt.tesoro.it/it/antifrode_mezzi_pagamento/rapporti_statistici/carte_pagamento.html

European ATM Security Team-EAST (2011). “ATM Fraud Analysis Report”, version 18/7/11.http://www.european-atm-security.eu

European Payments Council (2009), “SEPA Cards Framework, version 2.1”, Brussels. http://www.europeanpaymentscouncil.eu/knowledge_bank_detail.cfm?documents_id=330.

Giacomelli A. (2008), “Non si gioca con le carte”, in Internal Audit, May-August.

Hoechle D. (2007) “Robust Standard Errors for Panel Regressions with Cross-Sectional Dependence”, The Stata Journal.

Kosse A. (2010).“The Safety of cash and debit cards: a study on the perception and behaviour of Dutch consumers“, DNB Working paper 245, De Netherlandsche Bank.

Kosse A. (2011). “Do Newspaper Articles of Card Fraud Affect Debit Card Usage?”, ECB Working paper, no. 1389.

Paulina M., e A. Paba (2010), “A discret choice approach to model credit card fraud”, MPRA Paper No. 20019.

Podestà F., (2002). “Recent Developments in Quantitative Comparative Methodology: the Case of Pooled Time Series Cross-Section Analysis”, DSS Papers Soc 3-2002.

Koenker R., (2004). “Quantile Regression for Longitudinal Data”, Journal of Multivariate Analysis, vol 91, Issue 1, October, 74-79.

Sahin Y. e E. Duman (2011).“Detecting Credit Card Fraud by Decision Trees and Support Vector Machines“, IMECS, March 16-18.

Shen A., Tong R. e Y. Deng (2007).“Application of Classification Models on Credit Card Fraud Detection, IEEE.