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STHANA Profitability Forecast and Situation Analysis for Automated Teller Machines

机译:STHANA盈利预测自动柜员机的盈利预测与现状分析

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The French credit card system makes it highly profitable for banks to have heavily used Automated Teller Machines (ATM). "La Caisse d'Epargne", one of the major French bank, manages 5000 ATMs all over France. The goal of the Sthana system is to capitalize the knowledge spread all over the company into a system capable of issuing recommendations for existing ATM's and capable of forecasting a new ATM's activity. Sthana uses Data Mining and Case-Based-Reasoning techniques so as to extract information from existing data (including economic, geographical and internal bank data) and from the bank's ATM experts. The system builds up classifications on high level descriptors from raw data and eventually indicates a measure of the ATM's activity and profitability, highlights factors which could lead to higher profitability or pinpoints the ATM's vulnerabilities. An object-oriented model coupled with an extremely modular ssytem allows the data and rules to be customized for geographical units of the bank. Sthana has been deployed and customized for different geographical units, but the knowledge base is centralized in the bank headquarters in Paris.
机译:法国信用卡系统使银行拥有巨大的利润,拥有大量使用的自动柜员机(ATM)。 “La Caisse d'Epargne”是主要的法国银行之一,在法国管理5000个ATM。 STHANA系统的目标是将整个公司的知识利用进入能够为现有ATM的建议发出建议的系统,并能够预测新的ATM活动。 STHANA采用数据挖掘和基于案例的推理技术,以便从现有数据(包括经济,地理和内部银行数据)和银行的ATM专家中提取信息。该系统在原始数据中构建了高级描述符的分类,最终表示ATM的活动和盈利能力的衡量标准,突出显示可能导致更高盈利能力或精确漏洞的因素。与极其模块化SSYTEM耦合的面向对象的模型允许为银行的地理单位定制数据和规则。 STHANA已为不同的地理单位部署和定制,但知识库集中在巴黎的银行总部。

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