Smarter digital banking with big data

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1 IBM Software White Paper Financial Services Smarter digital banking with big data Transform customer relationships and improve profitability

2 2 Smarter digital banking with big data Contents 2 Introduction 2 Capitalizing on customer mobility 3 Benefitting from a smarter mobile banking strategy 3 Interacting through a customer s daily experiences 8 Creating an expanded value proposition Introduction Years after the global financial crisis of 2007, banks are still struggling to determine how they will return to pre-crisis profit margins. The combination of ultra-low interest rates, continued instability in financial markets, stricter regulation and lower-performing assets are all impacting top and bottom lines. To increase profitability, many banks are looking to expand the value proposition they offer to customers. This new value proposition is based on the banks ability to develop new products and services that generate alternative revenue streams by fitting seamlessly into customers daily lives. To deliver this value proposition, banks need to leverage big data and analytics to better understand a customer s behavior and needs. Banks have always benefitted from customer information based on account activity and segmentation. With the advent of big data technologies, banks can understand their customers in greater depth and predict their needs by analyzing all available customer information. Armed with new capabilities to make sense of this data, banks can gain the insight necessary to provide personalized products and services in real time. For example, many banks have lacked the data and analytics to examine the correlation and timing of purchases; when a customer buys groceries, does he also buy fuel for his car? That data and related insight is now available. The era of big data in banking has arrived. Banks can capture and analyze larger volumes of data from a variety of data sources and types relating to their customers, including correspondence, social media, web clicks and transactional information across multiple channels. Employees can derive insight from free-form contact center notes and take action in real time. Banks can monitor customer behavior, update campaigns and deliver relevant, in-context offers immediately and take smart advantage of growing channels such as smartphones and tablets. Capitalizing on customer mobility Customer preferences for interacting through multiple channels and using mobile devices are driving banks to adopt a mobile digital strategy that makes the most of big data. Mobile devices, including tablets and smartphones, are overtaking web and branch touch points, a trend that shows no sign of stopping. Mobile interaction represents an opportunity for banks to provide their customers with the ultimate multichannel experience of doing business anywhere, anytime using ubiquitous cellular technology. To foster lasting connections with customers, banks need to expand capabilities to capture and manage data across all touch points and implement the most appropriate marketing, social business and mobile technologies.

3 IBM Software 3 Interacting through a customer s daily experiences What does the intersection of big data analytics and mobility look like from a street-level view? Here is an example. Peter and his wife bought their home with a mortgage from Leading Bank. They recently decided to remodel. An avid cook, Peter heads out on a weekend morning and buys a set of chef s knives for the new kitchen using a bank card. Benefitting from a smarter mobile banking strategy For banks, smart means moving beyond mobile banking as a mere destination: that is, one more place to go for routine transactions such as balance inquiries or payments. Instead, mobile banking must be seen as an interactive service that works with customers based on their needs. By coupling IBM analytics for big data with a smart mobile strategy, banks can increase wallet share and assets under management while lowering the organization s operating ratio by using more efficient channels. Mobile technologies enable a bank to extend the customer experience by interacting with customers during their dayto-day experiences instead of simply responding to discrete channel touch points. Big data analytics enable the bank to better predict the customer s most likely actions, determine next best actions and offer consultative advice in financial matters. Anticipating customer needs before the competition can lead to improved customer relationships and enhanced revenue streams. The bank s system recognizes that Peter is making a purchase related to his home and analyzes his available financial data including spending patterns, income, savings balance, available credit, loans, credit score and level of risk. The system also analyzes his related activity on social media and discovers that Peter loves to cook, enjoys gourmet restaurants, blogs about his dining experiences and Would love to have a new, restaurant-style six-burner gas stove. Key capability: Big data empowered analysis of clientspecific spending patterns As data volumes increase, banks require cost-effective horsepower to perform this type of personal spending analysis across their entire customer base not just creating categories of spending ranges that contain many customers, but individually understanding each customer. Achieving this granularity of understanding across many individual customer dimensions, particularly where information about each dimension may require analysis of unstructured data, requires economies of scale in technology that are not possible with traditional warehousing approaches.

4 4 Smarter digital banking with big data Using big data capabilities and predictive analytics to analyze all of the available data about Peter, the bank anticipates similar home purchases, but also knows that Peter is nearing his credit limit. To seize this business opportunity before Peter is offered a credit card from a retailer, the bank sends him an offer to extend his line of credit. Peter receives this offer on his smartphone (see Figure 1). He reviews and accepts the terms using his smartphone. The line of credit is automatically added to his account overview along with his mortgage, checking and savings. Key capability: Big data detailed analysis of client-specific spending patterns Leveraging a detailed understanding of Peter enables the bank to make more granular decisions about the amount of risk it will be taking when it offers him a line of credit. The bank can choose a set of terms and a credit limit optimized for both Peter and the bank resulting in a customized, personal offer. Peter is pleased with the proactive service from the bank, and uses the credit line increase to purchase a stove. The banking system identifies this large purchase and prompts Peter to take advantage of the bank s free Digital Vault (see Figure 2). Using this service, customers can take a picture of their receipts and warranty cards at the point of sale and store them safely in a bank database for future retrieval. Credit extensions based on real-time activity and historical spend analysis Digital vault triggered by real-time spending activity, with cross-sell capabilities Figure 1. Line of credit extension offer delivered via smartphone. Figure 2. Customers get a prompt to archive receipts using the bank s free Digital Vault.

5 IBM Software 5 Key capability: Operational decision management; real-time messaging and alerting based on an event The prompt allows Peter to go into the application and post a photograph of the receipt. Optical character recognition (OCR) technology recognizes this as a home-appliance purchase and offers an extended warranty based upon his street address. Peter accepts and purchases the warranty on his new appliance. Key capability: Content analytics and content management for OCR analysis and text analytics It s now 11:30 a.m. Previously, Peter opted into a service provided by Leading Bank offering discounts at many of the bank s small business customers. Analyzing Peter s regular lunchtime purchase behavior and preferences, the bank sends him a personalized offer from one of its nearby merchants, Villa Spicy. This analysis is based on time of day, location, past purchase history and promotional appeal (cash back vs. points). The offer appears on Peter s smartphone (see Figure 3). Peter shares the offer with his friends through social media: Anyone up for lunch at Villa Spicy? 20% cash back from Leading Bank. Multiple likes and comments come in, and several friends join him. As Peter pays his bill, the bank sends an alert to verify the purchases made today knives, stove and lunch preventing fraudulent charges to his account. Peter authorizes the transaction, avoiding the embarrassment of a denied charge (see Figure 4). Real-time, location-based merchant offers driven by historical spend analysis Mobile fraud resolution Figure 3. Targeted alerts present a lunch discount offer and a map showing the location of the restaurant. Figure 4. Users get a prompt to verify purchases, preventing fraudulent charges.

6 6 Smarter digital banking with big data Key capability: Big data detailed analysis of client-specific spending patterns and history, combined with available offers within geographical proximity Later that day, Peter receives an alert and logs into his account through his tablet. He looks under the My Offers tab to see personalized offers just for him. He sees that the bank is offering him its Smart Sweep service. Peter wants to learn more about Smart Sweep, so he starts a video chat to learn more about Smart Sweep in the banking application on his tablet (see Figure 5). Key capability: Big data, operational decision management detailed analysis combined with rules that determine when money should be automatically transferred Personal, online interaction for mobile customer service and assistance with context A video window pops up, and Peter is greeted by a bank customer service representative who offers to share more detail about the Smart Sweep service. Because the representative knows that Peter was reviewing this bank feature on his tablet, he can anticipate and better answer Peter s questions. The rep explains that Smart Sweep is a bank account that automatically transfers amounts that vary from a certain level into a higher interest-earning investment option at the close of each business day. The result is that the system transfers money from a checking to a savings account. Peter adds this feature to his account, and also checks his balances. For this and other customer interactions, big data is essential for cost-effectively analyzing hundreds of detailed attributions per customer, each constantly changing based on daily customer activity. In My Offers, Peter also sees a home equity line of credit (HELOC) offer based on predictive analytics including analysis of not only Peter s micro-segment, but also his real-time behavior and social media. Peter accepts, scans his driver s license and his most recent pay statement, and signs the agreement digitally (see Figure 6). The bank approves the HELOC. Key capability: Big data detailed analysis of client-specific behavior compared with aggregated anonymous data from other customers Figure 5. Customer service video chat capability using a tablet.

7 IBM Software 7 Online account opening and document submission, with process tracking capabilities While Peter is still logged in, he also views the bank s new Spending Manager feature to gain insights into how his spending changes from month to month. Peter can compare his spending to his financial peers in his geographic location, income and age bracket (Figure 7). With help from big data analytics, banks can develop new products and services that help customers manage their finances, save money, and benefit from services and offers that fit seamlessly into their daily lives. These capabilities enhance the customer experience and promote customer retention, while generating new revenue streams for the banks. Soft, people like you offers based on payment/deposit analysis and peer comparison Figure 6. Online scanning of driver s license and pay statement with digital signature. Figure 7. Comparison to financial peers based on aggregated anonymous data.

8 Creating an expanded value proposition IBM solutions for big data and analytics help banks capture and analyze information that was previously unavailable. IBM solutions for mobile banking enable banks to leverage this information with new and optimized mobile technology to create an expanded value proposition and a strong competitive advantage. Combining mobile technology and big data analytics enables a variety of customized, near-real-time responses that help banks increase customer satisfaction with each interaction. Over time, these successful interactions help improve the bank s up-sell and cross-sell efforts and incrementally build lifetime customer value and loyalty. With a smarter digital banking strategy, banks can better attract and retain customers, improve profitability and heighten the ability to respond to industry changes. Building a platform for success IBM offers a complete platform and set of products to extend core banking capabilities to digital channels. With this platform, banks can add new products and services that leverage advanced analytics to deliver a premier digital customer experience. These products and services include: Core banking: Core banking and financial services functionality such as checking, savings, credit cards, lending, payments, investments and insurance delivered through traditional channels Mobile banking: Platforms, products and services to deliver digital vault, spending manager, financial manager, merchant offers, mobile payments and other value-added offerings through digital channels Analytics: Advanced business analytics and big data analytics for predictive analysis, segmentation, campaign management, operational decision management, performance management, life-event modeling and service-level monitoring Copyright IBM Corporation 2014 IBM Corporation Software Group Route 100 Somers, NY Produced in the United States of America November 2014 IBM, the IBM logo, and ibm.com are trademarks of International Business Machines Corp., registered in many jurisdictions worldwide. Other product and service names might be trademarks of IBM or other companies. A current list of IBM trademarks is available on the web at Copyright and trademark information at ibm.com/legal/copytrade.shtml This document is current as of the initial date of publication and may be changed by IBM at any time. Not all offerings are available in every country in which IBM operates. THE INFORMATION IN THIS DOCUMENT IS PROVIDED AS IS WITHOUT ANY WARRANTY, EXPRESS OR IMPLIED, INCLUDING WITHOUT ANY WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND ANY WARRANTY OR CONDITION OF NON- INFRINGEMENT. IBM products are warranted according to the terms and conditions of the agreements under which they are provided. The client is responsible for ensuring compliance with laws and regulations applicable to it. IBM does not provide legal advice or represent or warrant that its services or products will ensure that the client is in compliance with any law or regulation. Please Recycle For more information To learn more about IBM solutions for big data, mobility and the financial industry, please contact your IBM representative or IBM Business Partner, or visit: ibm.com/big-data/banking IMW14706-USEN-01

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