BIG DATA & BANKING
India accounts for only 7% of Global Financial Analytics Workforce, but has the highest growth rate.
There are only 60,000 Analytics Professionals in Indian Financial Sector.
78,000 positions to be filled in the current year.
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Banking & Big Data
- Not long time back, a typical banking professional would keep track of his customers, walk ins and prospective clients in order to be sure of how to serve them best?
- Banks would gain trust of their customers and serve them for a considerable long time. But today, people are often engaged in multiple ways different cities, even countries. Banks hence have a challenge working the traditional personalised banker way.
- Big Data come to rescue to resolve the complexities of customers lifestyle, needs, travel and preferences. Big Data makes it easy for banks to personalise services for each individual.
Why Big Data Cannot Be Ignored?
- Banks cannot make sense of information generated by their own business: Banks generate several Gigabytes of data every day. Log data, transactions, help line data, emails, social media interactions and external feeds are a few of them. The data is unstructured and not usable without deployment of Big Data.
- Big Data is Business Idea(s): Each data set gathered over a period of time narrates a unique story. Big Data helps banks to not only read the story but understand it in terms of the next big business idea.
- Risk Management: Big Data considerable improves the extrapolative power of riosk models currently being used by Banks and other financial institutions.
Challenges
- Data analytics are still being perceived to work in isolation. This does not help as along with identifying business opportunities, banks also need to identify security threats, the occurrence of fraud and possible solutions.
- Big Data should connect across all departmental and organizational silos. Many traditional banking institutions have still not begun their Big Data activities.
- Demonitisation followed by digitisation has added enormous volume of data to the Banking System. The datasets are big, and also high on variety and velocity.
3Vs of Big Data
McKinsey says, “Big Data is Next frontier for Banking, Innovation and Competition”. The value of Big Data for the banking industry is immense. The banks that are able to leverage the 3Vs will move ahead substantially as compared to their competition.
Role of Big Data
- Big Data reduces the Cost of Fraud Detection immediately. It also has a significant impact on Sanction Management Cost.
- Big Data offers banks significant insight on new revenue streams.
- Banks can build an insight into customer intelligence and behaviours that they may be able to monetise in the future.
- Big Data can easily be used to enhance account and relationship management.
- Big Data also helps in early detection of potential problems vis-à-vis how the credit lines are being used?
Advantages of an upskilled Workforce
- Big Data comes with many challenges of its own. One of the key challenges is sheer complexity. How does the banking firm understand Big Data?
- How does the Bank make sense of structured and unstructured data inflowing into their system?
- Good talent is scarce. Finding that fine balance of Hard Science and Business Acumen is even rarer.
- An upskilled workforce will have relevant knowledge of SAS/R/SQL/Python programming along with Statistical knowledge along with Data Visualisation.