hadoop is present in nearly every vertical today that isleveraging big data in order to analyze information and gain competitiveadvantages. many financial organizations firms are already using hadoopsolutions successfully and the ones who are not have plans to do so. if theydon’t, they risk enormous market share loss. followingare a few of the most intriguing and essential big data and hadoopuse cases.
如今，hadoop幾乎存在于各個方面，其通過利用大資料來分析資訊和增加競争力。許多金融機構和公司已經開始使用hadoop成功地解決問題，即便他們本沒有計劃這樣做。因為如果他們不這樣做，就會面臨市場佔有率損失的巨大風險。以下是一些特别有趣和重要的大資料和hadoop用例。
fraud detection:fraud, financial crimes and data breaches are some of the most costlychallenges in the industry. hadoop analytics help financial organizationsdetect, prevent and eliminate internal and external fraud, as well as reducethe associated costs. analyzing points of sale, authorizations andtransactions, and other data points help banks identify and mitigate fraud. forexample, big data technology can alert the bank that a credit or debit card hasbeen lost or stolen by picking up on unusual behavior patterns. this then givesthe bank time to put a temporary hold on the card while contacting its accountowner.
詐騙偵測（fraud detection）：詐騙是金融犯罪和資料洩露中成本最大的挑戰之一。hadoop分析可以幫助金融機構檢測、預防和減小來自内部和外部的詐騙行為，同時降低相關成本。銷售、授權、交易以及其他的資料分析點能夠幫助銀行識别和減少詐騙。例如，大資料技術通過提取異常行為模式，能夠提醒銀行信用卡或借記卡的丢失或被盜。進而給銀行提供時間暫時當機異常賬戶，直到聯系到賬戶持有人為止。
risk management:every financial firm needs to assess risk accurately, and big data solutionsenable them to to do so by effectively evaluating credit exposures. banksanalyze transactional data to determine risk and exposure based on simulatedmarket behavior, scoring customers and potential clients. hadoop solutions allowfor a complete and accurate view of risk and impact, enabling firms to make thebest, most informed decisions.
風險管理（risk management）：任何一家金融公司都需要準确地評估風險，而大資料解決方案就使他們能夠有效地評估信貸風險。銀行分析交易資料，基于模拟市場行為、評估使用者和潛在使用者，來确定風險和洩露。hadoop的解決方案對風險和後果具有全面而準确的考慮，使企業能做出最好、最明智的決策。
contact center efficiencyoptimization: ensuring customers are satisfied is of utmostimportance when it comes to finances, and big data can help resolve problemsquickly by allowing banks to anticipate customer needs ahead of time. analyzingdata within the contact center provides agents with timely and concise insightthat satisfies customers quickly and efficiently, ensuring cost effectivenessand even improving cross-sales success rates.
客服中心效率優化（contact center efficiencyoptimization）：確定使用者滿意無疑是最重要的。涉及到金融業，大資料允許銀行提前預測使用者需求用以快速地解決問題。客服中心的資料分析提供了媒介，及時、簡潔的洞察力，能夠快速滿足使用者的需求，進而確定了效率成本甚至提高了交叉銷售的成功率。
customer segmentation foroptimized offers: big data provides a way to understand customers’ needs at a granular level so that banks and financial organizationscan deliver targeted offers more effectively. in turn, these more personalizedoffers result in higher acceptance rates, increased customer satisfaction,higher profitability and greater retention. detailed information aboutcustomers derived from social media and transactions can be utilized to reducecustomer acquisition costs as well as turnover.
客戶分類優化産品（customersegmentation for optimized offers）：大資料提供了一種方法從粒度級别來了解客戶的需求，以至于銀行和金融機構能夠更有效地提供有針對性的優惠。轉而，這些更加個性化的産品帶來更高的接受度，提高客戶的滿意度，制造更高的利潤和更好的客戶保留。來自于社交媒體和交易的顧客詳細資訊則可以用來降低使用者的采購成本以及周轉率。
customer churnanalysis: everybody knows that it’s cheaper to keep acustomer than it is to go out and find a new one. big data and hadooptechnologies can help financial firms keep retain more of their customers byanalyzing behavior and identifying patterns that lead to customer abandonment.when are customers most likely to leave for the competition, and why? whatcauses customer dissatisfaction? where did the firm fail? this information fordetermining how to avoid customer abandonment is priceless. it’s imperative for financial firms to learn the right steps toimplement in order to meet customer needs and save their most profitablecustomers.
客戶流失分析（customer churn analysis）：大家都知道開發新客戶比留住老客戶的成本要高，大資料和hadoop技術可以通過導緻客戶放棄的行為分析和識别模式來幫助金融公司來留住他們的客戶。什麼時候客戶會最可能因為競争對手而離開？什麼原因？導緻客戶不滿意的因素是什麼？公司失敗在哪裡？這些決定如何避免客戶放棄的資訊都是無價的。為了迎合客戶需求，使客戶利益最大化，學習用正确的步驟來執行對金融公司公司來說勢在必行。
sentimentanalysis: hadoop and advanced analytics tools help analyze social media inorder to monitor user sentiment of a firm, brand or product. if a bank isrunning a campaign, big data tools can monitor social media by name and reporton it by hashtag, campaign name or platform. analytics on the fine-graineddetails are insightful, and the bank could then make decisions more accuratelybased on these insights in terms of timing, targeting and demographics.
情感分析（sentiment analysis）：hadoop和先進的分析工具有助于分析社會媒體來達到監控企業使用者的情感，品牌或産品的目的。如果一家銀行參加競選，大資料工具可以通過名稱，和标簽報告以及競選活動名稱或平台報告來監控社會媒體。細節分析是富有洞察力的，銀行可以基于這些根據時間，目标和人口特征的見解來準确地做出決策。
customerexperience analytics: as consumer-facing enterprises,financial institutions need to take advantage of the customer data that residesin all of the silos across various lines of business. these include portfoliomanagement, customer relationship management, loan systems, contact center,etc. big data can provide better insight and understanding, allowing firms tomatch offers to a customer or prospect’s needs. this thenhelps the firm to optimize and improve profitable and long-term customerrelationships.
客戶體驗分析（customer experience analytics）：作為面向客戶的企業，金融機構需要利用到存于各種業務線筒倉的客戶資料。這些包括投資組合管理，客戶關系管理，貸款系統，呼叫中心等等。大資料可以提供更好的洞察和了解，幫助公司迎合客戶需求以及前景需求。這些都可以幫助企業優化提高利潤，并維護長期的客戶關系。
the bottom line is that allenterprises, especially financial firms, need to use big data and hadooptechnologies to their fullest potential now, particularly with the overwhelmingamount of data and transactions amassed on a daily basis. in order to remaincompetitive and maintain current customers while attracting new ones, financialfirms should start planning to utilize big data technologies today or risklosing more customers to competitors utilizing these tools. that doesn’t necessarily mean in every way possible –it just means in the best way possible for each organization.
底線是所有的企業，尤其是金融公司，需要使用大資料和hadoop技術充分發揮他們的潛力，特别是對于每天交易所積聚的海量資料。為了保持競争力，維持現有客戶并吸引新客戶，金融公司應該從今開始計劃使用大資料技術，否則會因為競争對手對這些技術的使用而失去更多的客戶。那并不意味着要使用每一個可行的方式— 而隻是運用對每個機構最好的可行方式。
big data and hadoop technologies arepowerful and help financial organizations stay ahead in the market. set them inmotion and watch them deliver results.
大資料和hadoop技術非常強大，可幫助金融機構在市場上保持領先。運用了這些技術就能看到他們傳輸的結果。
原文釋出時間為：2014-08-05
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