fbpx



Data Science Use Cases in Banking

TOP 5 DATA SCIENCE USE CASES IN BANKING

By Wahyu Kwan   |   December 21, 2020

You’ve probably heard of a saying like this somewhere in the news or internet, “Big data and AI are evolving and taking over the world.” Yup, you guessed it right. Data science is taking over, and the banking industry is already revolving around that fact. In order to keep up with other competitors, banks are starting to realize that data science technologies can help them make data-driven decisions hence improving their overall operational efficiency.

In this blog post, we will go through different use cases of how Data Science is playing a significant role in the banking industry.

FRAUD DETECTION

You might experience this or you might not, but it’s no longer a mystery that many criminals commit cybercrimes by hacking into someone’s bank account and purchasing luxuries they couldn’t have purchased otherwise. The term “fraud” in the banking sector is very sensitive and crucial. It is one of, if not the most, important concern for all banks to detect frauds as soon as possible and provide restrictions to minimize losses. With the help of Data Science, it is relatively easier to achieve this level of security and avoid losses. 

There are 3 major steps in which the banks are using Data Science to detect any suspicious activity:

  • Collect big data samples for model estimation
  • Analyze and train models to make a prediction
  • Test the model for accuracy and deployment

All 3 data sets above work in different ways and each requires its own data science experts to implement various data mining techniques such as clustering, association, forecasting, classification, etc.

Top 5 Data Science Use Cases in Banking

One example of an efficient fraud detection algorithm is when a bank holds an unusual amount of transaction or when it holds a transaction that occurred in a different country than where you’re currently living. With this system, you will be warier of your account activities and have more sense of security with the bank.

MANAGING CUSTOMER DATA

In the world we’re living now, with the rising popularity and usage of digital banking, banks generate millions of new datasets every day and the numbers are not going down anytime soon. It is humanly impossible for someone to gather, analyze, and store such massive data alone. Thus, Data Scientists are using various data science tools to help them manage these datasets. With different machine learning algorithms, banks can now isolate relevant data such as customer behaviors, patterns, and interactions. After analyzing the data, Data Scientists can then utilize these insights to help them personalize each customer and generate new revenue generation strategies accordingly.

RISK MODELING

It’s a top priority for investment banks to have a proper risk management strategy. It is important to identify and evaluate risks before regulating financial activities and deciding the right pricing for financial instruments. There are 2 ways in which data science help investment bankings:

Credit Risk Modeling – Data Scientists analyze customer’s previous history and credit reports. The result of the analysis allows the bank to predict if you are capable of repaying your loan, hence giving banks the capability to decide whether to go through with the loan or not.

Investment Risk Modeling – In order for financial advisors to give advice that results in more profit, investment banks use risk modeling to detect risky investments. You wouldn’t want to invest your money in an advisor that is blind to data, would you?

With the help of the latest Data Science technologies, banking organizations are now developing effective risk modeling strategies therefore leading them to better data-driven decisions.

Top 5 Data Science Use Cases in Banking

CUSTOMER SEGMENTATION

All organizations target their customers and segment it into groups for various reasons, including the banks. The group can be formed based on two factors: their behavior, or as we call it behavioral segmentation, or specific characteristics (e.g. age, gender, income, etc.) which is called demographic segmentation. 

Data Scientists use methods like clustering to accurately group customers. After they are done grouping customers, the banks will then use this information to predict Customer Lifetime Value (CLV) for different customer segments. CLV is what organizations use to measure how valuable their customer is. It is important for banks to discover high-value customers or segments as it helps them sustain beneficial relationships and retain profitable customers.

RECOMMENDATION ENGINES

Have you ever checked your email and noticed that you’ve got an email from your bank offering you discounts to your favorite ice cream store? You wondered to yourself, “how on earth can they know my favorite ice cream store?” Data Science and Machine Learning. Banking organizations collect and analyze user’s activity to accurately predict and suggest the most relevant items that might pique the user’s interests. To make an accurate prediction, Data Scientists first need to identify customer profiles, then capture data to avoid repeat offers.

CONCLUSION

These are only a couple of use cases in which Data Science has helped the banking industry, and thanks to the rapidly growing technology, there will always be new techniques for banks to adapt and have a competitive edge over the others be it within the security or customer service. Now if you find this post helpful, don’t forget to share it with your friends to shock them with this mind-boggling information. Happy learning, Algoritans!

Yuk belajar data science di Algoritma Data Science Education Center! Kamu bisa ikut berbagai kelas data science untuk pemula, salah satunya di program Academy kami.

PELAJARI LEBIH LANJUT

The last comment and 51 other comment(s) need to be approved.

Related Blog

Distributed Processing
Apa Itu Data Analysis Expressions?
jadi data scientist
Cara Menjadi Data Scientist Handal
Distributed Processing
Mengenal Apa Itu Distributed Processing

You’ve probably heard of a saying like this somewhere in the news or internet, “Big data and AI are evolving and taking over the world.” Yup, you guessed it right. Data science is taking over, and the banking industry is already revolving around that fact. In order to keep up with other competitors, banks are starting to realize that data science technologies can help them make data-driven decisions hence improving their overall operational efficiency.

In this blog post, we will go through different use cases of how Data Science is playing a significant role in the banking industry.

FRAUD DETECTION

You might experience this or you might not, but it’s no longer a mystery that many criminals commit cybercrimes by hacking into someone’s bank account and purchasing luxuries they couldn’t have purchased otherwise. The term “fraud” in the banking sector is very sensitive and crucial. It is one of, if not the most, important concern for all banks to detect frauds as soon as possible and provide restrictions to minimize losses. With the help of Data Science, it is relatively easier to achieve this level of security and avoid losses. 

There are 3 major steps in which the banks are using Data Science to detect any suspicious activity:

  • Collect big data samples for model estimation
  • Analyze and train models to make a prediction
  • Test the model for accuracy and deployment

All 3 data sets above work in different ways and each requires its own data science experts to implement various data mining techniques such as clustering, association, forecasting, classification, etc.

Top 5 Data Science Use Cases in Banking

One example of an efficient fraud detection algorithm is when a bank holds an unusual amount of transaction or when it holds a transaction that occurred in a different country than where you’re currently living. With this system, you will be warier of your account activities and have more sense of security with the bank.

MANAGING CUSTOMER DATA

In the world we’re living now, with the rising popularity and usage of digital banking, banks generate millions of new datasets every day and the numbers are not going down anytime soon. It is humanly impossible for someone to gather, analyze, and store such massive data alone. Thus, Data Scientists are using various data science tools to help them manage these datasets. With different machine learning algorithms, banks can now isolate relevant data such as customer behaviors, patterns, and interactions. After analyzing the data, Data Scientists can then utilize these insights to help them personalize each customer and generate new revenue generation strategies accordingly.

RISK MODELING

It’s a top priority for investment banks to have a proper risk management strategy. It is important to identify and evaluate risks before regulating financial activities and deciding the right pricing for financial instruments. There are 2 ways in which data science help investment bankings:

Credit Risk Modeling – Data Scientists analyze customer’s previous history and credit reports. The result of the analysis allows the bank to predict if you are capable of repaying your loan, hence giving banks the capability to decide whether to go through with the loan or not.

Investment Risk Modeling – In order for financial advisors to give advice that results in more profit, investment banks use risk modeling to detect risky investments. You wouldn’t want to invest your money in an advisor that is blind to data, would you?

With the help of the latest Data Science technologies, banking organizations are now developing effective risk modeling strategies therefore leading them to better data-driven decisions.

A pie chart showing different groups of people in different segments of group

CUSTOMER SEGMENTATION

All organizations target their customers and segment it into groups for various reasons, including the banks. The group can be formed based on two factors: their behavior, or as we call it behavioral segmentation, or specific characteristics (e.g. age, gender, income, etc.) which is called demographic segmentation. 

Data Scientists use methods like clustering to accurately group customers. After they are done grouping customers, the banks will then use this information to predict Customer Lifetime Value (CLV) for different customer segments. CLV is what organizations use to measure how valuable their customer is. It is important for banks to discover high-value customers or segments as it helps them sustain beneficial relationships and retain profitable customers.

RECOMMENDATION ENGINES

Have you ever checked your email and noticed that you’ve got an email from your bank offering you discounts to your favorite ice cream store? You wondered to yourself, “how on earth can they know my favorite ice cream store?” Data Science and Machine Learning. Banking organizations collect and analyze user’s activity to accurately predict and suggest the most relevant items that might pique the user’s interests. To make an accurate prediction, Data Scientists first need to identify customer profiles, then capture data to avoid repeat offers.

CONCLUSION

These are only a couple of use cases in which Data Science has helped the banking industry, and thanks to the rapidly growing technology, there will always be new techniques for banks to adapt and have a competitive edge over the others be it within the security or customer service. Now if you find this post helpful, don’t forget to share it with your friends to shock them with this mind-boggling information. Happy learning, Algoritans!

Yuk belajar data science di Algoritma Data Science Education Center! Kamu bisa ikut berbagai kelas data science untuk pemula, salah satunya di program Academy kami.

PELAJARI LEBIH LANJUT

Related Blog

Real Time Processing
Perbedaan Batch Processing dan Real Time Processing
Metode Pengolahan Data
Tipe, Langkah, dan Metode Pengolahan Data
Batch Processing
Mengenal Batch Processing dan Implementasinya
The last comment and 51 other comment(s) need to be approved.