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Datafication in
Banking and
Finance
Banking and finance are one of the most
complex sectors to handle.
Hundreds of regulations, risk factors of
managing finance, and a huge demographic
of customers - make it really difficult ...
It’s also obvious that banking
and finance services contain a
big amount of data.
The smartness of a company lies in utilizing that
data to improve processes.
Every customer interaction,
transaction, and other processes
create electronic data to record, store
and utilize.
Smart companies incorporate
advanced technologies and get the
maximum out of their data.
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.
Here are some of the applications of
big data in banking and finance
industry…
Customer segmentation is all about targeting customers
according to their behavior.
With the rise of data, banks have unde...
Analyzing huge amounts of data, companies find out valuable
information related to transaction demographics, personal
cond...
It’s important for finance and banking companies to attain a
clear picture of potential risk in order to avoid hidden
fina...
Finance and bank services mainly target individuals
according to their buying nature.
Finding browsing habits of customers...
There are many different forms of data that help in
personalizing services.
Companies collect data through social media pr...
Detecting a fraud is probably the most difficult job finance
and bank companies have to conduct.
Integrating machine learn...
Machines analyze daily activities of a bank and any
fraudulent activities are detected immediately.
Using this data, banks...
Auditing information, activities, finance and other
factors require data and evaluation technologies.
Maintaining a high s...
Finance companies and banks are leveraging data to
improve internal and external business functions.
Data incorporation ha...
An industry completely based on money, banking and finance
companies have to rely on data.
The industry saves hundreds of ...
This financial services provider holds a customer base of
more than 200 million in over 160 countries.
Applying a comprehe...
Then, machine learning is used to understand the potential
use of data in customer acquisition and retention.
A predictive...
Financial services use credit scores to decide the eligibility of
a loan seeker. However, there are thousands of seekers t...
They collect data from a variety of points and conduct an
algorithm based analysis to find eligibility of a person.
It doe...
Lack of proper information and time-taking evaluation
presents the risk of losing customers.
To resolve this problem, Zest...
Integrating machine learning in borrower data analysis
allows this company to collect and analyze data from
thousands of p...
Most investors limit their investments due to the lack of
risk assessment.
Once you have all the potential outcomes visibl...
PeerIQ is helping the investors by collecting data and
conduct a predictive analysis to provide information that is
useful...
Tala uses mobile data of hundreds of thousands of users and
creates useful insights related to their credit.
As mobile pho...
Data categorizes people into two major categories, but there
are hundreds of factors that work on data.
Eventually, the co...
A great amount of manpower is required for manual
auditing, even when data is available.
AppZen is resolving this problem ...
A huge amount of data allows machine learning algorithms
to automatically audit business functions in a real-time.
Investi...
Suppliers always look for reliable financing options
that are affordable.
Flowcast is making this possible with their API....
A huge data collection and organized insights allow
suppliers to find financing solutions that are most suitable.
Hence, a...
Data and machine learning are two
pillars holding the future of banking
and financial services.
Many companies have understood this
and started moving forward, and
others are planning to do so.
This means that investme...
The future of banking sector holds a variety
of data-driven processes, which will
revolutionize the industry furthermore.
Hopefully, this shift towards
datafication will keep on growing and
improving customer experience,
compliance, fraud detec...
Looking to acquire meaningful
data from the web?
Share your requirements with
us at sales@promptcloud.com
www.promptcloud....
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Datafication in Banking and Finance

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.

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Datafication in Banking and Finance

  1. 1. Datafication in Banking and Finance
  2. 2. Banking and finance are one of the most complex sectors to handle.
  3. 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. 4. It’s also obvious that banking and finance services contain a big amount of data.
  5. 5. The smartness of a company lies in utilizing that data to improve processes.
  6. 6. Every customer interaction, transaction, and other processes create electronic data to record, store and utilize.
  7. 7. Smart companies incorporate advanced technologies and get the maximum out of their data.
  8. 8. In today’s scenario, finance and banking services don’t like to wait to conduct data analysis and get results.
  9. 9. Most of the evaluation takes place in a real-time, making decision-making quicker and accurate for these services.
  10. 10. Here are some of the applications of big data in banking and finance industry…
  11. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 33. Suppliers always look for reliable financing options that are affordable. Flowcast is making this possible with their API. 7. Flowcast
  34. 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. 35. Data and machine learning are two pillars holding the future of banking and financial services.
  36. 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. 37. The future of banking sector holds a variety of data-driven processes, which will revolutionize the industry furthermore.
  38. 38. Hopefully, this shift towards datafication will keep on growing and improving customer experience, compliance, fraud detection and other aspects of this sector.
  39. 39. Looking to acquire meaningful data from the web? Share your requirements with us at sales@promptcloud.com www.promptcloud.com

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