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How financial service firms use big data to meet business goals

Vince Dell'Anno and Brian McCarthy, Accenture Analytics | March 26, 2015
The pressing challenge is to drive more continuous value and unearth opportunities more rapidly.

The data exploration shouldn't end there. Simultaneously, financial services firms should create and execute on an innovation agenda. Along with seeking the specific outcome, companies can test and play with their data through data discovery techniques to find patterns in the data that weren't clearly evident and could drive value for the business.

For example, banks and insurance companies have identified fraudulent behavior by applying this data discovery technique. One company discovered that people who input information faster into fields online were more fraudulent, and conversely people who spelled the first and last name online with an upper case first letter were less likely to be fraudulent.

Step Three: Mobilize the data
To realize the true value hidden in data, financial services companies should look at this asset as if it were a supply chain, enabling it to flow easily and usefully through the entire organization--and eventually throughout each company's ecosystem of partners.  To build a data supply chain, firms should begin by following two important steps: utilize a data service platform that makes all data accessible to those who need it when they need it, and integrate data from multiple sources.

With the new external data sources becoming available that can unearth new insight opportunities and the new big data tools and technology entering the space, a foundation has been established for companies to create an integrated, end-to-end data supply chain for their business and uncover new data insights that can bring a competitive advantage.

Experience the big data benefits
Following are a few examples showcasing how financial services companies are innovating and solving business problems with big data:

Through big data analytics, banks and insurance companies can provide customers with a richer experience.  To accomplish this goal, firms should explore micro-segmentation of customer data obtained from all transaction touch points mobile, online, call centers, etc. and analyze it so customers with similar needs can be presented with relevant and timely offers on their desired channel, from a mobile app to social media. 

Another example: A bank could create an overlay of customer sentiment data on top of customer survey data. This data analysis could help firms learn if they are giving customers the right service, refunding money for the right reasons, or charging people the right fees. If data insights uncovered negative or incorrect results for the consumer, the bank could then take steps to right the wrongs and improve the customer's experience. In real-time.

Also, a life insurance company could underwrite risk better. For example, a company could text mine decades of hand-written claims adjusters' notes, in its current unstructured data form, and place all the newly created datasets (i.e. attributes of the policy or claim) into a structured database along with the existing online insurance policy documents. The database housing the combined data could provide insurance companies with a better place to start when looking to analyze data to underwrite risk more effectively.

 

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