Big Data & Analytics for Financial Institutions



Read more: BigData in Telecom, BigData in Retail
 
Money and finance related matters are always meticulously thought of before making any decision. If one goes back in time, it is noticeable that key financial decisions (placing a deposit, drawing a loan or taking a personal insurance) have mostly been through social circle of 'trusted' individuals. In small towns it was quite common for banks/financial institutions to hire 'well connected' individuals who have a trusted social standing towards marketing new offers/schemes/policies.

What has changed for the financial landscape?

1) People spend more time online than in face-to-face social interactions
The below two statistics speaks volumes of significant changes in our 'social' interactions over the year. Humans today spend far more time online on the internet than ever before and this trend is only increasing.





2) Number crunching strategies no longer a differentiator:
Financial institutions have traditionally relied on strategies around interest rates and number crunching schemes/proposals to gain competitive edge. Also, post the historic 2008 financial meltdown there are increasingly stringent regulations impacting the growth further.

3) Untapped data from internal & external sources:
As indicated in (1) there is huge amount of data from various sources. With individuals spending more time online, has also meant that they leave key information about their interests, likes/dislikes online. With the maturing big data & hadoop technology platform it is now possible to assimilate data points from different data pools and develops crucial insights.


New possibilities through Big Data Analytics:

 1) Improved customer relations & proactive retention measures:
A lot of aspects discussed here for telcos do apply to financial institutions. It is now possible to build customer profiles based on customer data and help in vastly improve customer satisfaction and towards targeted marketing.


2) Better risk management especially for insurance firms
 In previous generations, insurance agents knew their customers and communities personally and were more intimately aware of the risks inherent in selling different types of insurance products to individuals or companies. Today, relationships are decentralized and virtual. Insurers can, however, access a myriad of new sources of data and build statistical models to better understand and quantify risk. These Big Data analytical applications include behavioral models based on customer profile data compiled over time cross-referenced with other data that is relevant to specific types of
products.

3) Lead generation through predictive analytics on demographical trends
The ability to offer customers the policies they need at the most competitive premiums is a big advantage for insurers. This is more of a challenge today, when contact with customers is mainly online or over the phone instead of in person. Scoring models of customer behavior based on demographics, account information, collection performance, driving records, health information, and other data can aid insurers in tailoring products and premiums for individual customers based on their needs and risk factors. Some insurers have begun collecting data from sensors in their customer’s cars that record average miles driven, average speed, time of day most driving occurs, and how sharply a person brakes.

Interesting video on how big data can make a difference in Banking:


Read more: BigData in Telecom, BigData in Retail

Comments

  1. Hi, This is Yasmin from Chennai. Thanks for sharing such an informative post. Keep posting. I did Big Data Training in Chennai at TIS academy. Its really useful for me to know more knowledge about Big Data. They also give 100% placement guidance for all students.

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