Machine Learning in Banking sector

Machine Learning in the Banking Sector: Top 11 Use Cases

Top 11 Use cases of Machine Learning in Banking Sector

In this article, we will explore the areas where machine learning can be used in the banking sector. Apart from that, we will discuss some of the machine learning solutions developed by top banks of the world. Machine Learning is the science of pattern extraction from data for making intelligent decisions. The main advantage of machine learning is that it does not require programming instead it learns the patterns from the data on its own.

In the banking sector, machine learning can be applied to automate day to day tasks. Further, it can also help in servicing customers through chatbots to save huge manpower that can be easily delegated to some other critical tasks. It can assist the bank in taking important business decisions such as the prediction of default loans and much more.

Below we will look through 11 potential use cases of machine learning in the banking sector.

1. Chatbots for real-time servicing customers

2. Prediction of Fraudulent Transactions

3. Financial Text Classification

4. Credit Risk Modelling / Early Stage Prediction of Default Loans

5. Credit Risk Profiling

6. Targeting Potential Customer Using Customer Segmentation

7. Customer Churn Prediction

8. Bankruptcy Prediction of Companies

9. Customer’s Complaint Sentiment Analysis

10. Document summarization & Information extraction

11. Detection of Money Laundering transactions

1. Chatbots for real-time servicing Customers

A chatbot is an online chat facility via text-messages that helps solve user queries and gives a feeling of human interaction. Banks can effectively utilize chatbots for various tasks like automation of real-time conversation regarding product queries, the recommendation of relevant products, collecting the basic details of potential customers of the bank, etc. This would mark an end to waiting in long queues for common tasks and hence results in happier customers.

Today, more advanced forms of chatbots are emerging i.e., contextual chatbots. The contextual chatbots have the ability to predict the user’s intent, sentiments, and remember the short-term conversation details just like humans. Banks can effectively utilize these chatbots to automate customer service by real-time conversation and delegate their manpower to some other critical tasks. Some of the top banks in the U.S. have already deployed chatbots for assisting their customers and solving their queries.

The Bank of America has introduced a virtual assistant named Erica to send important notifications to their customers, provide account summary, balance details, give smart suggestions about money-saving, etc. Similarly, Wells Fargo launched a virtual assistant on Facebook messenger platform to serve its customers for resetting of password, providing account details, answering common queries like where is the nearest ATM? how much money they have in their accounts? how much money they have spent last week? Through this chatbot, Wells Fargo can easily serve their customers on a social media platform Facebook Messenger.

Real-time conversation with bank's chatbot
Fig. Real-time conversation with bank’s chatbot

2. Prediction of Fraudulent Transactions

Due to the increase in usage of digital transactions, a huge amount of fraudulent transactions happen every day globally.

According to the Nilson Report, In 2018 fraud losses reached $27.85 billion worldwide and are projected to rise to $35.67 billion in five years.

Fraudulent transactions impact the image of the bank directly and create mistrust and dissatisfaction among customers of the bank. So, it is the need of the hour to build an effective fraud prevention solution that generates flags against such fraudulent transactions and blocks or suspends such transactions.

Machine learning can be used for building such a solution as banks have a huge amount of historical records of customer transactions.

The machine learning model can be built by training on historical data of customer transactions and after building the model, it can be deployed for real-time detection and prevention of fraudulent transactions.

Every transaction it processes adds to its repository of historical information, which means it will continuously learn the habits of fraudsters and can also outline important features responsible for predicting fraudulent transactions.

Prediction of fraudulent transactions
Fig. Prediction of fraudulent transactions

3. Financial Text Classification

As we know different businesses or companies applies for a loan in bank and banks generally use their financial documents to assess their credit worthines.

Banks can use Machine Learning based financial news classification to identify the basic sentiments of the applicant’s company (i.e., positive, negative, and neutral) in the market. Such systems will help banks in assessing the current financial status of the company.

Financial text classification
Fig. Financial text classification

With the use of Machine Learning based Financial Text classification, banks can easily assess the sentiments of the company from financial news or financial documents which will in turn help banks in deciding whether to give loans to the company or not.

4. Credit Risk Modelling / Early Stage Prediction of Default Loans

Generally, when the bank receives a loan application, the bank manager has to make a decision for loan approval based on the applicant’s profile. Two types of risks are associated with the bank’s decision:

  1. If the applicant is likely to repay the loan, then not approving the loan results in a loss of business to the bank
  2. If the applicant is not likely to repay the loan, i.e. he/she is likely to default, then approving the loan may lead to a financial loss for the bank.

Banks can leverage machine learning to predict the probability of default loans given input data in terms of borrower loan application.

Loan applications generally comprise of demographic features, credit profile features, and loan-related features.

The complexity of dealing with the above two issues multiplies when banks incorporate many dimensions they examine during credit risk modeling.

These additional dimensions typically include demographic features, loan-related features, credit profile features, or behavioral information such as loan/trade credit payment behavior.

Analyzing all of these dimensions to predict whether the applicant will repay the loan or not is a very challenging task, but machine learning techniques can help achieve this goal.

Further Interpretable machine learning models can help banks to check if the model is capturing appropriate features for the prediction of default loans.

Prediction of default loans using machine learning
Fig. Prediction of default loans using machine learning

5. Credit Risk Profiling

Credit risk profiling (finance risk profiling) is very important in terms of identifying specific loan segments that will default or repay the loan in the future.

The basic principle behind the credit risk profile suggests that 80% to 90% of the loan defaults originate from 10% to 20% of the lending segments. Profiling the segments helps in revealing useful information for credit risk management.

Banks often collect a vast amount of information on their borrowers and it consists of hundreds of variables that help in identifying payment behavior of borrowers.

Machine Learning can be used for credit risk profiling which learns the payment behavior of borrowers and can help determine which factors are most contributing in segregating borrowers to a particular segment or profile.

Bank customer risk profiling
Fig. Bank customer risk profiling

6. Targeting Potential Customer Using Customer Segmentation

Target marketing is important in the sense it is very cost-effective and helps in generating higher revenue. Generally, when a bank launches a new product, it has to make huge advertisement costs which include sending SMS to all customers, distributing printed advertisement leaflets to customers, TV advertisements, etc.

Target marketing helps in targeting those customer segments which will likely purchase the product and in this way it brings down huge advertisement costs and helps in generating higher revenue at less cost.

Machine learning can be used for automating the target marketing for banks, as banks have vast historical records of customer transaction data, demographic data, and spending and loan repayment habits of the customer.

Machine learning can make clusters of customer segments based on their data and help in targeting the corresponding customer base as and when the bank launches a new product.

Target potential customer using customer segmentation
Fig. Target potential customer using customer segmentation

7. Customer Churn Prediction

In any financial institution, it is much more expensive to get the new customer than retaining the existing one. According to Harvard Business Review companies can increase their revenue by 25% to 85% just by reducing the customer churn rate by 5%.

Banks should avoid the loss of customers while acquiring new ones in order to increase their profits and enhance their core competitiveness.

Banks maintain historical customer records of active and dormant accounts of customers.

So in this scenario, machine learning can be applied in predicting the churn rate of customers and outputs important variables. Based on those variables bank can restructure their products by offering them concessions in products and reduce the churn rates.

Bank customer churn prediction using machine learning
Fig. Bank customer churn prediction using machine learning

8. Bankruptcy prediction of companies

When any company applies for a loan in the bank, they have to submit their financial statement so that banks can assess their profitability and repayment capacity.

For this bank requires analysis of their financial statements to decide whether the company can repay the loan in the future or not. Machine learning can be used at this stage for the initial financial statement analysis for predicting corporate bankruptcy. In this case input to the machine learning model could be the financial parameters of the company.

9. Customer complaints sentiment analysis

Sentiment Analysis of the customer complaints of banks is an interesting task and critical for customer service improvement. Sentiment Analysis is most useful in the examination of individuals’ feelings from textual data. Machine learning in combination with NLP can be applied for classifying customer complaints into moderate, less severe, or most severe. For this task, properly labeled dataset is required so that the machine learning model can be trained on the data with proper pre-processing of raw texts using NLP and provide real-time predictions as soon as customer complaints arrive.

if you want to know how you can apply machine learning in combination with NLP for text classification kindly refer to this link.

Sentiment analysis of bank customer complaints
Fig. Sentiment analysis of bank customer complaints

10. Document summarization & Information extraction

One of the most challenging tasks for the banks is to analyze policy documents for regulatory compliance. In the past decades, banks are investing billions of dollars for compliance every year. The most painful task in this is to extract relevant information from hundreds of pages of policy documents. This task is really time-consuming and results in the huge expense of manpower. Automated text summarization using machine learning and NLP helps in summarizing lengthy documents into a few paragraphs or key points that help in interpreting the overall document. It will also highlight key phrases or sentences which are relevant in the document.

Machine learning based content summaries will definitely reduce the time in analyzing lengthy documents and will reduce the expenditure of banks in complying with the regulatory policies.

JP Morgan Chase & Co., one of the largest banks of the US has unveiled machine learning based software named COIN (Contract Intelligence) which automates document review of legal documents highlighting important clauses and summarizing the result in few paragraphs in a matter of seconds. Earlier, its legal and loan officers spend a total of 360,000 hours each year for interpreting commercial loan legal documents.

Automatic financial document summarization
Fig. Automatic financial document summarization

11. Detection of money laundering transactions

According to United Nations Office on drugs and Crime (UNODC), “The estimated amount of money laundered globally in one year is 2 – 5% of global GDP, or $800 billion – $2 trillion in current US dollars.

As it has a direct impact on the economy of the government, it is need of the hour to prevent money laundering transactions and reduce false positives which are the current challenging task in the banking industry.

Between 1% and 2% of all the Anti Money Laundering (AML) alerts are the actual suspicious transactions to be reported to the authorities.

So, machine learning can be used here to reduce 98% of the cases which are false positives. It will help banks in utilizing their resources to identify 2% potential AML cases.


So, in this article, we have discussed 11 potential use cases of machine learning in the banking sector. Banking is now very dynamic in nature with the increase in digitization of numerous banking operations. So the potential applications of machine learning will also increase in the banking industry at a rapid pace. Consequently, the requirement of skilled professionals in the machine learning domain will also increase at the same rate.

I hope I have discussed some of the interesting applications of machine learning in the banking sector.

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  1. Meet 11 of the Most Interesting Chatbots in Banking
  2. Nilson Report Card Fraud Worldwide
  3. Pre-processing Online Financial Text for Sentiment Classification: A Natural Language Processing Approach
  4. Financial Sentiment Analysis Using Machine Learning Techniques, International Journal of Investment Management and Financial Innovations
  5. Machine Learning: Challenges, Lessons, and Opportunities in Credit Risk Modeling
  6. Credit Risk Analysis & Modeling: A Case Study, IOSR Journal of Economics and Finance
  7. Predict customer churn in a bank using Neural Designer
  9. Sentiment Classification of Indian Banks’ Customer Complaints
  10. NLP For Topic Modeling Summarization Of Financial Documents (10-K/Q)
  11. 20 Applications of Automatic Summarization in the Enterprise
  12. Detecting money laundering transactions with machine learning


About the author

Manu Siddhartha

Hi!! I am Siddhartha, an aspiring blogger with an obsession to share my knowledge in Machine Learning & Data science domain. This blog is dedicated to demonstrate application of machine learning in different domains with real-time case studies.

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