کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
383660 660829 2012 20 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
The design of FFML: A rule-based policy modelling language for proactive fraud management in financial data streams
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
The design of FFML: A rule-based policy modelling language for proactive fraud management in financial data streams
چکیده انگلیسی

Developing fraud management policies and fraud detection systems is a vital capability for financial institutions towards minimising the effect of fraud upon customer service delivery, bottom line financial losses and the adverse impact on the organisation’s brand image reputation. Rapidly changing attacks in real-time financial service platforms continue to demonstrate fraudster’s ability to actively re-engineer their methods in response to ad hoc security protocol deployments, and highlights the distinct gap between the speed of transaction execution within streaming financial data and corresponding fraud technology frameworks that safeguard the platform. This paper presents the design of FFML, a rule-based policy modelling language and encompassing architecture for facilitating the conceptual level expression and implementation of proactive fraud controls within multi-channel financial service platforms. It is demonstrated how a domain specific language can be used to abstract the financial platform into a data stream based information model to reduce policy modelling complexity and deployment latencies through an innovative policy mapping language usable by both expert and non-expert users. FFML is part of a comprehensive suite of assistive tools and knowledge-based systems developed to support fraud analysts’ daily work of designing new high level fraud management policies, mapping into executable code of the underpinning application programming interface and deployment of active monitoring and compliance functionality within the financial platform.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 39, Issue 11, 1 September 2012, Pages 9966–9985
نویسندگان
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