Have you ever imagined a world without Banks? Just try it, and you will find it very difficult to live in a world without banks. Banks are one of the most important parts of the financial economy of any country. On the other hand Machine Learning and AI are one of the most trending technologies these days.
In this blog we will be discussing different aspects of machine learning on the banking sector.
Importance of banking sector in economy of any country?
Banks offer various kinds of accounts and provide loans based on the requirements. Apart from it, there are other various activities like investments in market and different funds. Overall, the banking sector has a wide impact on the economy directly and indirectly.
To achieve these goals banks have a lot of activities on different branches and areas. As there is a lot of competency in the banking sector, all the banks are competing with each other to provide more and more services to customers and at the same time optimize resources and increase profit. On the other hand, we have machine learning and AI to solve real-time problems. Machine learning and AI are being used widely to unwrap future possibilities and changing the game in the banking sector. In this blog, we will be discussing how machine learning and AI can benefit the banking sector and provide solutions to the most critical problems.
So, before going into the details of machine learning in the banking sector lets understand what are major functions are provided by banks.
What services are provided by banks?
Here are some of the major services provided by banks:
- Provide financial services to individuals and families like credit, deposit and money management.
- Commercial banks focus on products and services for business.
- There are investment banks that work on providing finance.
- Online banks provide online banking facilities to customers.
- Mutual funds
Apart from it, there are many other services provided by banks. To accommodate all these services banks use various tools and technologies. A lot of area of banks is covered by online banking and most of the facilities are available online via the internet.
How banks are using machine learning?
There are many banks across the globe that are leveraging machine learning and AI in their daily routine and getting benefits out of it.
For example, top banks in the US like JPMorgan, Wells Fargo, Bank of America, City Bank and US banks are already using machine learning to provide various facilities to customers as well as for risk prevention and detection. Similarly in India, there are various banks that are taking interest in using machine learning in different fields.
Use cases of machine learning in the banking sector:
If we talk about how these banks are using machine learning, mostly the banks are using them for fraud detection, customer services, investment modeling, customer modeling, risk prediction, risk prevention, and investments. On the basis of these points we can divide these use cases into the following major categories:
- Provide customer support:
This is an area that is very important for customers. Using machine learning banks can automate there initial 24*7 customer support so that customers get quick responses for there queries and problems. It not only makes the customers happy but also helps banks to filter and prevent less critical conversations which can be solved by automation. Chat-bots are an example of this kind of support and most of the banks have chat-bots to handle customers during initial conversations and optimize resources.
- Fraud Detection in real-time:
Preventing frauds is one of the most challenging tasks of banks and using machine learning banks can detect fraud in real-time to prevent loss. Most of the banking tasks by a customer are done via online banking and it becomes very important to provide security and accuracy for the transactions in real-time. Machine learning models can help with fraud detection in real-time.
- Customer data management:
Most of the banks are supporting online transactions these days which generates big data and its really important to keep track of actually relevant data and events happening on a day to day activities.
Using data science, data scientists can identify important data and information using machine learning.
- Risk modeling for investments:
Investment in the market is a large area where the banks are utilizing the money and making a profit from it. As the investment in various stocks and funds is a very risky task, risk modeling becomes a very important and crucial part of financial modeling.
Using machine learning, we banks can evolve models that can provide suggestions and predictions for investment in various fields and stocks.
- Marketing Strategy Planning:
Marketing is one of the most important areas for any business these days. Choosing the correct audience and target the right audience is one of the most difficult and important tasks in marketing strategy.
Machine learning models can be trained to solve these problems as well. using machine learning and AI, banks can enable real-time analysis and target the correct audience for different products and services.
- Customer Segmentation:
Customer segmentation is one more optimization done by most of the banks. Based on this segmentation banks can offer various services to different customers.
Clustering and Segmentation are one of the most used use-cases for machine learning. Using machine learning banks can do this task with more accuracy and intelligence with automation.
Apart from all these use cases, there are other various use cases that we will be discussing in further blogs. But hope this gives a brief about the benefits the banking sector can get from the machine learning algorithms.
We will be discussing individual topics in upcoming blogs shortly. In the meanwhile if you want to check other blogs on machine learning, you can checkout them here.