This ppt aims to describe the future trends of Digital banking and finance using Big Data Analytics.

1. BIG DATAANLYTICS – A POWERFUL MARKETING
TOOL FOR BANKING AND FINANCE INDUSTRY

2. 1.Introduction

4. Sources of Big Data

6. 2. Objectives
• To study the need for big data analytics in banking and finance industries.
• To study Big Data Analytics application in new age financial processes.
• To study the emerging strategies of bankers using big data analytics.

7. 3. Need for Big Data Analytics
• To Increase revenue and Profitability –> helps to focus on target products
or potential areas. This way financial institutions can design and launch
their products to generate maximum revenue and profit.
• To enhance customer engagement and loyalty –> Mine of information,
which will help to know customer behavior that helps to build better
relations with customers.
• Optimizing assets and risk mitigation –> As banks are always exposed to
risks like NPA’s, Good use of data analytics can help to analyze the risk and
help to make steps to minimize the risk exposure.

8. Example to portray importance of big data : Reliance Jio
• Free for a period more than 180 days.
• Jio reached the 100 million user mark adding
consumers at an average of 600,000 a day.
Meanwhile, Reliance Jio posted a net loss of Rs
22.50 crore for the six months.
• Focused heavily on collecting data from the
customers by observing internet browsing
volumes, browsing time periods, average voice
call time, voice call traffic, etc.,
• Started analyzing the best plan that the
customers can afford and which will generate
maximum profit to the company.

9. Worldwide revenues for big data analytics will grow from about $18 billion in 2014
to about $92 billion by 2026, Wikibon predicted Figure is shown below:

10. Fraud
detection
Compliance &
Regulatory
Customer
Segmentation
Personalized
Marketing
Risk
Management
Areas of application in new age financial processes

11. •After the sub-prime crisis in 2007-2008,
The key challenges faced by the bank.
•Reduced liquidity
•Rising interest rate
•Customer attrition
•Risky lending were
Application of Data analytics in ICICI Bank

12. Employed analytical models developed with Business Intelligence.
Debt collection performance improvement resulted in credit loss savings.
In case of automobile loans, the bank achieved 50% increase in debt
collection.
Automated and centralized allocation for credit cards and personal loans
reduced manpower requirement by about 80%.
The turnaround time for debt recovery reduced from 5 to 6 days to 3 to 4
hours.
Application of Data analytics in ICICI Bank

13. Emerging data analytical Strategies implemented by leading banks
•In the UK, Lloyds Banking group works with Google
and uses tools such as Google Big Query and Data
Flow to analyze customer behavior, understand their
requirements, and deliver solutions real-time.

14. • To improve customer service,
• RBS used data about agents’ performance to provide
personalized coaching and also leveraged artificial
intelligence solutions to help staff answer customer inquiries
more quickly.
• RBS also uses data to connect better with customers, for
example, sending messages on their birthday or having staff
call a customer who could borrow at a lower rate.

15. In the United States, US Bank deployed an analytics
solution that integrates data from online and offline
channels to provide a unified view of the customer.
By supplying the call center with more relevant leads and
providing recommendations, Capgemini said, the bank
improved its lead conversion rate by more than 100% and
delivered better and personalized experiences.

16. In Turkey, according to KPMG Nunwood, Garanti Bank’s mobile
app provides customers with alerts about deals on their favorite
brands.
Uses GPS to notify them if they are close to a store with a special
offer, and estimates the amount they will have in their account for
the rest of the month based on past spending habits.

17. Conclusion
•Various big data applications such as structured data
analytics, text analytics, web analytics, multimedia
analytics and mobile analytics.
•Research attention.
•Scope for applications of analytics.
•Career option
•finance professional to adopt business analytics skills.
•‘big data’ boom through the finance industry too.

18. Vijayakumar P
B.Tech, MBA (NET, SET), M.Com (IBO)
Assistant Professor, KKC, Puttur.
C Rani
MBA, M.A (Psy)
Assistant Professor, KKC, Puttur.