Demo: IBM Big Data and Analytics at work in Banking - YouTube

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

more sources than ever before. Deeper insight by digging deeper into customer

information and behavior

enabling segment of one marketing and faster in sight

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 score 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 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 stole for his kitchen

the banking system also identifies this is a large purchase

and props Peter to take and archival 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 Peters 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 purchase is 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 an 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