A Reactive Fraud Monitoring Engine for I...

2018 is a challenging year for European banks and their fraud management capabilities. In November, the introduction of SEPA Instant Credit Transfers will mean that the time allowed for transferring money to payment beneficiaries will have to be reduced from one business day, as it is currently, to a maximum of five seconds.  This dramatically reduces time for fraud detection.  Around the same time, the Revised Payment Service Directive (PSD2) will oblige banks to give access to third-party providers of financial services to customers' accounts through open APIs.  This introduces new fraud risks and the possibility for fraudsters to come up with new modus operandi.  All this takes place in a context where the General Data Protection Regulation (GDPR) is adding new constraints to the way customers' data may be accessed and used.  This presentation describes the architecture of a new fraud monitoring engine (FraME) for BNP Paribas Fortis bank to detect and react to fraud attempts in real-time given the above mentioned new rules and constraints. The presentation discusses the main challenges met during the construction of the engine and the solutions adopted to overcome them. FraME is a typical reactive system as defined in the Reactive Manifesto. It is elastic and resilient in order to detect fraud within a few hundred milliseconds and without any downtime while being available 24/7.  Fraud detection is obtained using machine learning models that are regularly retrained, tested, and deployed.  These models are completed by a set of detection rules that permit quick reaction to new fraudulent modus operandi. FraME is implemented using Apache Kafka, Apache Flink and a combination of micro-services.