In today’s scenario, finance and banking services don’t like to wait to conduct data analysis and get results. Most of the evaluation takes place in a real-time, making decision-making quicker and accurate for these services. Hopefully, this shift towards datafication will keep on growing and improving customer experience, compliance, fraud detection and other aspects of this sector.

1. Datafication in
Banking and
Finance

2. Banking and finance are one of the most
complex sectors to handle.

3. Hundreds of regulations, risk factors of
managing finance, and a huge demographic
of customers - make it really difficult for
banks and finance companies to provide
satisfactory services.

4. It’s also obvious that banking
and finance services contain a
big amount of data.

5. The smartness of a company lies in utilizing that
data to improve processes.

6. Every customer interaction,
transaction, and other processes
create electronic data to record, store
and utilize.

7. Smart companies incorporate
advanced technologies and get the
maximum out of their data.

8. In today’s scenario, finance and
banking services don’t like to wait to
conduct data analysis and get results.

9. Most of the evaluation takes place in a
real-time, making decision-making
quicker and accurate for these
services.

10. Here are some of the applications of
big data in banking and finance
industry…

11. Customer segmentation is all about targeting customers
according to their behavior.
With the rise of data, banks have understood that product-
centric marketing is not effective.
1. Segmentation of customers

12. Analyzing huge amounts of data, companies find out valuable
information related to transaction demographics, personal
conditioning, and other factors.
Then, it becomes extremely easy for services to create
groups of customers according to their behavior.

13. It’s important for finance and banking companies to attain a
clear picture of potential risk in order to avoid hidden
financial dangers.
Data technologies allow services to gather loan history,
credit card details, and other information.
Combining data from multiple databases, services make risk
evaluation accurate and effective with data.
2. Risk assessment

14. Finance and bank services mainly target individuals
according to their buying nature.
Finding browsing habits of customers helps services
understand what they are looking for.
The collected data is then converted into strategies and
analyzed to meet company goals.
3. Personalization in marketing

15. There are many different forms of data that help in
personalizing services.
Companies collect data through social media profiles in
order to know the likes and dislikes of consumers and their
sentiments.
Technologies such as machine learning and NLP make
sentiment analysis of collected data easier.

16. Detecting a fraud is probably the most difficult job finance
and bank companies have to conduct.
Integrating machine learning with data allows services to
track every activity in real-time.
4. Predictive fraud analysis

17. Machines analyze daily activities of a bank and any
fraudulent activities are detected immediately.
Using this data, banks can automatically take actions such as
blacklisting a card, blocking an account or any other valid
action.

18. Auditing information, activities, finance and other
factors require data and evaluation technologies.
Maintaining a high standard of compliance, banks
and finance companies use data analysis to audit
security and privacy levels in their company.
5. Attaining compliance

19. Finance companies and banks are leveraging data to
improve internal and external business functions.
Data incorporation has become a necessity in terms
of customers, compliance, and business as well.

20. An industry completely based on money, banking and finance
companies have to rely on data.
The industry saves hundreds of hours with data analysis and
machine learning.
Here are a few companies using data in a unique manner to
improve processes.
Bank and finance companies that are
using data in unique ways

21. This financial services provider holds a customer base of
more than 200 million in over 160 countries.
Applying a comprehensive data-driven approach, Citibank
gathers data and segments it into a granular level.
1. Citibank

22. Then, machine learning is used to understand the potential
use of data in customer acquisition and retention.
A predictive model is created by algorithms that allow
authorities to modify processes before an error occurs.
1. Citibank

23. Financial services use credit scores to decide the eligibility of
a loan seeker. However, there are thousands of seekers that
have no credit score.
Using data and machine learning, Kreditech is resolving that
problem.
2. Kreditech

24. They collect data from a variety of points and conduct an
algorithm based analysis to find eligibility of a person.
It doesn’t take more than a few minutes for algorithms to
establish a credit score.
2. Kreditech

25. Lack of proper information and time-taking evaluation
presents the risk of losing customers.
To resolve this problem, ZestFinance has found a reliable
solution with data.
3. ZestFinance

26. Integrating machine learning in borrower data analysis
allows this company to collect and analyze data from
thousands of points.
This way, lenders obtain quality information without losing
opportunities.
3. ZestFinance

27. Most investors limit their investments due to the lack of
risk assessment.
Once you have all the potential outcomes visible,
investing become much more comfortable.
4. PeerIQ

28. PeerIQ is helping the investors by collecting data and
conduct a predictive analysis to provide information that is
useful for investment decisions.
Gaining helpful insights allows investors to get a clear
picture of their investments in advance and put their money
in the right products.
4. PeerIQ

29. Tala uses mobile data of hundreds of thousands of users and
creates useful insights related to their credit.
As mobile phones are used by almost every person, Tala is
able to collect a wide range of data and analyze to find
perfect borrowers.
5. Tala

30. Data categorizes people into two major categories, but there
are hundreds of factors that work on data.
Eventually, the company obtains a list of people who fit the
criteria of becoming a borrower.
5. Tala

31. A great amount of manpower is required for manual
auditing, even when data is available.
AppZen is resolving this problem by automating in auditing
with machine learning.
6. AppZen

32. A huge amount of data allows machine learning algorithms
to automatically audit business functions in a real-time.
Investing in machine learning data auditing has allowed
companies to reduce almost 50% of their general costs with
automated data auditing.
6. AppZen

33. Suppliers always look for reliable financing options
that are affordable.
Flowcast is making this possible with their API.
7. Flowcast

34. A huge data collection and organized insights allow
suppliers to find financing solutions that are most suitable.
Hence, a difficult task starts seeming extremely simple
with Flowcast.
7. Flowcast

35. Data and machine learning are two
pillars holding the future of banking
and financial services.

36. Many companies have understood this
and started moving forward, and
others are planning to do so.
This means that investments in data-
driven processes are going to increase
in the finance sector.

37. The future of banking sector holds a variety
of data-driven processes, which will
revolutionize the industry furthermore.

38. Hopefully, this shift towards
datafication will keep on growing and
improving customer experience,
compliance, fraud detection and other
aspects of this sector.

39. Looking to acquire meaningful
data from the web?
Share your requirements with
us at sales@promptcloud.com
www.promptcloud.com