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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

  • Using big data capabilities and predictive analytics,

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

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

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

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

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

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

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

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

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

  • 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.

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

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

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

  • 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.

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

  • 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.

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

  • 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;

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

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

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

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B1 US bank big data customer analytics credit data

Demo: IBM Big Data and Analytics at work in Banking

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    Chris Lyu posted on 2021/05/31
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