Combatir el fraude se ha convertido en una prioridad absoluta para las entidades financieros. De hecho, ya están centrando sus esfuerzos en encontrar tecnologías que frenen un tipo de cibercrimen por el que pueden sufrir pérdidas millonarias. Para ello, y según un estudio realizado por TransUnion, los bancos apuestan en mayor medida por las inversiones en biometría (59% de los encuestados), la formación de empleados, con un 57%, y la contratación de profesionales especializados (56%). Asimismo, éstos creen, en más de la mitad de los casos, que los avances en inteligencia artificial y machine learning serán vitales.
Otra de las tendencias que pone sobre relieve la encuesta es la importancia, cada vez mayor, que están dando este tipo de compañías a la normativa, en especial al cumplimiento con el Reglamento General de Protección de Datos (GDPR, por sus siglas inglesas). Así, hasta el 64% de los profesionales relacionados con fraude confirma que este es el área que se espera mayor gasto en los próximos meses. Esto implica cierta reducción en otros baremos, también importantes, como detección y prevención. “ Las empresas tienen que dedicar demasiados recursos, económicos y profesionales, en cumplir con las reglamentaciones existentes, y eso les hacer perder el foco en otras necesidades como la lucha contra el fraude”, asegura Juan Antonio Villegas, director general de TransUnion en España. “Vivimos una época de excesiva y cambiante regulación y, aunque estamos de acuerdo con que se trata de medidas necesarias en su mayoría, el foco de las inversiones debe ser necesariamente acabar con los delincuentes, no cubrir posibles multas por incumplimiento”.
Reducción de falsos positivos
Uno de las instituciones que más están investigando este tipo de fraude es el Massachusetts Institue of Technology (MIT), cuyo equipo ha descubierto una nueva técnica de aprendizaje automático para reducir los falsos positivos en las transacciones bancarias. Este descubrimiento podría ahorrar a cada entidad cerca de 190.000 euros anuales.
Este nuevo enfoque, alejado de los modelos tradicionales, extrae más de 200 características detalladas para cada transacción individual para identificar mejor cuándo los hábitos de gastos de un titular se salen de la norma habitual. El experimento ya ha sido probado con un conjunto de datos de cerca de dos millones de compras de un gran banco y se han reducido falsos positivos en un 54% con respecto a otras formas de detectar errores.
Fuente: http://cso.computerworld.es
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