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  • Banks face many challenges as they strive to return to pre-2008 profit margins.

    為了能再度重返 2008 年之前的榮景,銀行界正面臨許多挑戰 。

  • These challenges include reduced interest rates, instability in financial markets, tighter regulations and lower performing assets.

    這些挑戰包括:減少的利率、金融市場的不確定性、日益嚴格的法律規範,以及更低的營運資產。

  • Fortunately, banks taking advantage of big data and analytics can generate new revenue streams with personalized offers, targeted cross-sell and improved customer service.

    好消息是,懂得利用大數據分析技術的銀行能藉由提供客製化優惠、目標導向的交叉銷售,以及改善客戶服務來產生新的收益流。

  • Big data and analytics provide more insight by analyzing a higher volume and variety of data types from more sources than ever before.

    大數據分析技術可以提供「更多的洞察力」經由分析比過往更大量且更多樣的資料類型。

  • Deeper insight by digging deeper into customer information and behavior enabling "segment of one" marketing.

    「深層的洞察力」,透過挖掘客戶的資訊和行為以便進行單一市場的行銷區隔。

  • And faster insight by performing real time analysis of customer information to deliver offers at the point of decision.

    還有「快速的洞察力」 ,透過即時客戶資訊分析,以求能在決策時機點提供優惠。

  • Big data and analytics can analyze many types of customer information including:

    大數據分析技術可分析許多類型的客戶資訊,包括:

  • spending patterns, behavior, channel usage, product portfolio, bank interactions, credit information, social media and customer profitability.

    購物模式行為、通路使用、產品組合、銀行往來交易活動、信用資訊、社群媒體,以及客戶獲利能力。

  • Here's a day in the life customer scenario as an example of big data and analytics in action.

    接下來我們以某實際客戶做為大數據分析的範例:

  • Peter is a customer of leading bank with a mortgage, checking and savings accounts and a line of credit.

    Peter 是某知名銀行的客戶,他在銀行有房貸、支票和存款帳戶以及許多借貸。

  • Peter is remodeling his kitchen and decides to buy a new set of chef knives.

    Peter 最近正在改造他的廚房,並且決定要購買一套新的廚師刀。

  • The bank recognizes that Peter has made a number of household purchases lately and analyzes his financial and transactional data,

    銀行辨識出 Peter 近期已進行了多次家用採購,並分析他的財務和交易資料,

  • including spending patterns, income, savings balance, available credit, loans, credit scores and level of risk.

    包括消費模式、收入、存款餘額、可用額度、貸款、信用點數和風險程度。

  • The bank also analyzes his related activity on social media and learns that Peter loves to cook, enjoys gourmet restaurants,

    同時銀行也分析了他在社群媒體上相關的活動,並從中得知 Peter 喜歡烹飪並且喜歡美食,

  • he blogs about his dining experiences and indicates he likes a new restaurant style gas stove.

    因為他在社群媒體上記錄了他的用餐經驗,並提到他喜歡一款新的餐廳式瓦斯爐。

  • Using big data capabilities and predictive analytics,

    經由使用大數據的功能和預測分析,

  • the bank anticipates similar home purchases that knows that Peter is nearing his credit limit.

    銀行端不但可以預測類似的家用購買行為,同時可知道 Peter 的信用額度已接近上限。

  • The bank wants to seize this business opportunity before Peter is offered a credit card from a retailer.

    銀行為了能在 Peter 向其他家銀行辦理信用卡之前抓住商機,

  • The bank sends Peter an offer to extend his line of credit

    會向 Peter 提出延展貸款時間的優惠。

  • He uses the additional credit to buy the professional-style stove for his kitchen.

    如此他便會用額外的信用額度替他的廚房添購專業用的瓦斯爐。

  • The banking system also identifies this is a large purchase and props Peter to take and archival a photo of the receipt and warranty,

    銀行的系統辨識出這是一筆金額很大的採購,因此建議 Peter 要取得並將購買收據和保證書拍照歸檔。

  • as well the system recognizes this is a home appliance purchase and offers an extended warranty to Peter based upon his zip code.

    同時系統也辨識出這是一筆居家用品採購,並會根據郵遞區號提供 Peter 延展保固期。

  • It's now 11:30 a.m., analyzing Peter's regular lunchtime purchase behavior and preferences,

    現在是早上 11 點,銀行經由分析 Peter 固定的午餐時間購買行為以及偏好,

  • the bank sends him a personalize offer from one of Leading banks nearby merchants Chefwich.

    會將附近商家 Chefwich 的個人化優惠傳送給 Peter。

  • The system prompts Peter share the offer with his friends through social media.

    系統會提示 Peter 將個優惠透過社群媒體與他的好友們分享。

  • As Peter pays his bill, bank sends an alert to verify that he is authorized the purchases made today, preventing fraudulent charges to his account.

    當 Peter 付帳時,銀行會傳送驗證通知確認他當日的購買交易,以避免有人詐領他的帳戶。

  • Later, Peter logs into his account with his tablet computer.

    Peter 稍後用他的平板電腦登入到登入帳戶,

  • He looks in my offers to find his personalized offers.

    他看了一下「我的優惠」,找到他的個人化優惠。

  • After analysis of his spending patterns, the bank suggests that Peter sign up for their Smart Sweep service.

    系統在分析他的購買模式之後,銀行會建議 Peter 註冊他們的Smart Sweet服務。

  • Peter also sees a home equity line of credit offer based on an analysis of his financial condition as well as information on his home from third party sources.

    Peter 同時會看到一整列的房屋貸款優惠,這些是根據他的財務狀況以及來自第三方來源關於他住家的資訊分析得出。

  • Finally, the bank recommends that Peter sign up for Overdraft Protection to avoid the frustration of any future fees.

    最後,銀行還會推薦 Peter 註冊透支保障,以避免未來付款出現透支的狀況。

  • While Peter is logged into his account, he also views the spending manager feature to gain insight into how his spending changes from month to month.

    當 Peter 登入帳戶時,他同時會檢視支出管理員功能,以了解他不同月份的支出變化。

  • Peter can compare his spending to financial peers in his geographic location, income and age bracket.

    透過大數據提供的功能,Peter 可以比較在其所在地理位置,同樣和他從事財務工作者的收入和年齡範圍。

  • With new capabilities provided by big data and analytics, banks can develop new products and services that help customers manage their finances and save them money;

    透過大數據分析技術提供的新功能,銀行可以開發新的產品和服務,幫助客戶管理財務並替客戶省錢;

  • deliver relevant services and offers that fit seamlessly with customers daily lives.

    提供替客戶每日生活所需,量身打造相關服務和優惠。

  • Improve the customer experience and promote customer satisfaction and retention and at the same time generate new streams of revenue for the bank

    改善客戶經驗並促進客戶滿意度以及客戶回頭率,同時也替銀行帶來新的收益流。

Banks face many challenges as they strive to return to pre-2008 profit margins.

為了能再度重返 2008 年之前的榮景,銀行界正面臨許多挑戰 。

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IBM大數據和分析的應用 (Demo: IBM Big Data and Analytics at work in Banking)

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    Chris Lyu 發佈於 2021 年 05 月 31 日
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