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Predictive Analytics and Solutions for Financial Services

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云 Hortonworks 是领导者。阅读 Forrester Wave。

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Who’s looking at you?

Your ocean of data. Is it secure? Leading banks and capital markets firms are using Hortonworks Data Platform and Hortonworks DataFlow to process huge amounts of data from traditional and non-traditional sources. Compliance teams can analyze both data-in-motion and data-at-rest to detect suspicious activity in real-time.


Regulatory risk is present in all of these businesses and there is always internal risk. A few rogue individuals can cause extraordinary losses if their malicious activities go unnoticed.

Banks, insurance companies, fintech financial services and securities firms that store and process huge amounts of data in Apache™ Hadoop® have better insight into both their risks and opportunities. Predictive analytics in finance can provide deeper analysis and insight to help improve operational margins and protect against one-time events that might cause catastrophic losses.




Hortonworks Data Platform can store and analyze multiple data streams and help regional bank managers apply predictive analytics to control new financial account risks in their branches. They can match banker decisions with the risk information presented at the time of decision, to control risk by sanctioning individuals, updating policies, and identifying patterns of fraud. Over time, the accumulated data informs algorithms that may detect subtle, high-risk behavior patterns unseen by the bank’s risk analysts.



零售银行已经借助于 Hortonworks Data Platform 作为通用的跨公司 Data Lake,用于来自不同业务部门的数据:抵押贷款、个人银行、个人信贷、批发银行和财政银行。次级市场的内部经理和消费者均可从数据中获得价值。利用单点数据管理,银行可以操作安全和隐私措施,比如去除标识、遮蔽、加密和用户身份验证。

利用 Hadoop“星空图”保持次秒级 SLA

Ticker plants collect and process massive data streams on stock trades, displaying prices for traders and feeding computerized trading systems fast enough to capture opportunities in seconds. Applying predictive analytics to the financial markets is useful for making real-time decisions, and years of historical market data can also be stored for long-term analysis of market trends.

Hortonworks 的一位客户以 HDP 作为其基石,重新架构了其星空图。在没有使用 Hadoop 时,他们的星空图无法保存 10 年以上的交易数据。现在,每天有数 GB 的数据从几千种服务器日志订阅源中涌入。现在,这些数据每秒查询次数是是之前的 3 万倍以上,而 Apache HBase 支持超快速查询,可满足客户的 SLA 目标。除了这些之外,保留时间范围更超过了 10 年。


Hortonworks 的另一家客户从事投资服务,每天需要处理 1500 万条交易和 30 万宗交易。过去由于存储限制,该公司会归档历史交易数据,这限制了这些数据的可用性。最近一段时间,每天的交易数据只有在营业结束之后才能用来进行风险分析。这导致了会产生洗钱活动或流氓交易(不可接受的风险)的时间窗口。

Now Hortonworks Data Platform supports their AML software and accelerates the firm’s speed-to-analytics and also extends its data retention timeline. A shared data repository across multiple LOBs provides more visibility into all trading activities. The trading risk group accesses this shared data lake to processes more position, execution and balance data. They can do this analysis on data from the current workday, and it is highly available for at least five years—much longer than before.