Big Data for Banking Sector

In the field of nanotechnology, scientists of different fields such as physics, chemistry and biology work under the same roof. By the same way, in the field of Big Data, not only programmers, also statisticians, decision makers, system admins all work together. Big data is taking our modern computer based society to a new height. Still it’s the beginning of the journey and has started to reveal it’s possibility to different fields, such as social network, marketing, health care, law enforcement as well as banking and insurance Sector. Today we’ll explore the possibilities of big data in banking and financial sector.

Why Big Data in Banking Sector?

Big data deals with…
1. large amount of data (Volume),
2. coming from different sources and in different formats such as transaction data, customer photo, CCTV images (Variety)
3. that needs to be processed very fast (velocity).

Banking data has all these 3 properties. That’s why big data fits in banking sector.

There are 5 major areas of implementing Big Data in Banking sector:
1. Fraud Detection
2. Analyze customer sentiment and feedback
3. Analyze transaction history to discover trends around the year
4. Analyze customer’s spending history and behavior to offer personalized product
5. Customer Recognition

1. Fraud detection:
Currently banks detect frauds depending on ip address, unusual login time and “using wrong pin multiple time” etc. Big data changed the process of fraud detection. By analyzing large amount of data, applying algorithm and fraud pattern, it has become possible to detect and prevent frauds or unusual activity instantly.

2. Analyze customer sentiment and feedback:
It is easy to get new customers. But retaining them for long time is challenging. So it is important to analyze customer’s complain, suggestion, satisfaction and make decision based on analysis result. For example improving a service that is performing poor and dissatisfying customer, relocating ATM booth etc.

3. Analyze transaction history to discover trends
Banks have large amount of transaction history. By analyzing those data, we can discover trends of spending and savings of customers. We can find when and where users spend more amount of money. For example, before festivals customers spend more in shopping malls. By discovering these trends banks can offer better services for it’s all customers.

4. Analyze customer’s spending history and behavior
In this case, banks analyze single customer’s transaction history and behavior. This analysis helps management of banks to offer personalized products. For example, banks can offer dual currency products with benefits to the customers who travel frequently abroad. Banks can also offer special discounts to customers who make online transactions more often.

5. Customer Recognition: It is time consuming to recognize a customer when he or she goes to bank to withdraw money or perform other transaction operation. By using realtime analysis power of Big data, banks can analyze photo, signature, voice and behavior to recognize customer. This can save time and let bank stuffs server more customers.

Useful links:

Deriving Business Insight from Big Data in Banking

Big Data in Financial Services and Banking