Once regarded as expensive and risky to adopt, more and more banks are adopting Machine Learning (ML) technology to enhance their day-to-day operations. The global machine learning in banking market size was valued at $1.33 billion in 2021 and is projected to reach $21.27 billion by 2031, growing at a CAGR of 32.2% from 2022 to 2031. Approximately 40% of financial services companies rely on ML for fraud detection. By implementing machine learning technology in their operations banks get a competitive edge as well as access to state-of-the-art technology. ML has transformed the way modern banks function and operate making them safer, better and faster. 

We look at the different use cases of machine learning in banking today.

1. Risk Assessment

Machine learning technology reduces the time as well as risks for banks to approve loans. The former makes possible superior credit scoring. For your information, credit scoring tools analyze data from social media, telecommunication records, bank transactions and tax transactions to get a comprehensive overview of a client applying for a loan in a matter of minutes. Work experience, utility payments, rent history and annual income are other factors in determining the creditworthiness of applicants. Credit scoring tools are in great demand as there are many unbanked individuals worldwide and many are not considered creditworthy by traditional methods. ML algorithms can conduct highly accurate and customized credit score assessments. As a result, more individuals become eligible for credit. Humans may be biased and make unfair credit assessments. However, ML models make objective, fair and unbiased credit decisions. This bolsters the reputation of the bank among applicants. 

2. Investment Planning

Machine Learning algorithms process historical as well as real time financial data to identify patterns as well as trends better than earlier methods. Predictive analytics benefit from this and supply investors with superior insights into future market trends as well as stock values. Algorithmic trading has existed since the 80s. However, merging the same with ML has transformed it and made it more efficient as well as effective. Trading organizations leverage ML driven algorithmic trading that keeps track of finance related news, changing prices as well as trade results and other information. As a result, trades can be executed at the maximum prices utilizing that information.

3. Stock Market Forecasting

With the existence of vast historical data, machine learning tools can predict stock market trends. This includes identifying future risks as well as forecasting stock prices. Using machine learning the investors come to know of the slightest price fluctuations. Thanks to ML algorithms, informed decisions can be done in very less time.  

4. Customer Retention

Banks use machine learning to automate as well as minimize the time taken by customer support services. ML has the capability to analyze vast amounts of data to identify the needs and preferences of clients. Using these insights banks can come up with personalized recommendations for each client. ML tools learn from past experience as well as become better with time. As such the customer service offered by banks gets better and better. Better user experience leads to satisfied customers fostering customer retention. ML technology enables banks to obtain detailed insights about how their clients use their products and services. The data supplied by ML help banks track as well as predict client churn from changes in their behavior. The relevant clients are targeted with reminders and offers to encourage them to stay with the bank. The bank staff can reach out to them to understand the reasons for their dissatisfaction and take steps to address their grievances. 

5. Chatbots

Many banks make use of ML driven chatbots. They give real-time responses to client queries and issues. Note that their response becomes better with time. This leaves bank customer service staff free to focus on more strategic and innovative activities. Unlike human support staff, chatbots work round the clock. Chatbots leveraging advanced ML algorithms can resolve several daily queries which normally would be escalated to conventional customer support. If chatbots are unable to resolve a particular issue it would transfer it to a human support staff. However, before doing that the ML algorithm would classify the issue and extract relevant info so that the human staff can address the issue relatively quickly.

6. Adherence to Regulations

There exist many regulations that banks must adhere to. Also, the regulations keep evolving. Banks face a challenge to comply with the regulations. Human operators take a long time to understand the existing regulations and ensure that the banks adhere to them. The former are also prone to error. Machine learning solutions offer a better alternative. The former go through the regulations and documents swiftly enabling quick and full compliance. So, banks do not have to pay big fines for non-compliance.

7. Anti-Money Laundering

ML is very useful to detect and prevent money laundering activities. The ML algorithms can monitor vast numbers of transactions in real time and detect suspicious activity. The former flag such activities in real time for the bank to take appropriate action. Different patterns such as alterations in transaction frequency, abnormally large transactions, payments to high-risk jurisdictions are examples of suspicious financial activity. There exist layering processes, a frequent money laundering practice where multiple transactions are done to hide the origin of the funds. Smurfing is a popular money laundering tactic in which big amounts are broken down into smaller amounts that would attract a lesser amount of attention. ML can detect money laundering activities better than humans as well as traditional methods of identifying and preventing money laundering transactions.

8. Fraud Detection

By incorporating ML solutions, banks can detect fraudulent activities as well as identify users in real time. Credit card fraud can be detected as well as prevented in real time. ML tools alert banks in real time of cyberattacks making it possible for the banks to take mitigating measures and preventing any damage. The technology keeps banks a step ahead of criminals. ML makes the identity verification process simpler thus making clients happy.

9. Underwriting of Loans and Insurance

Humans consume tens of thousands of hours to do underwriting. There exists the possibility of human errors as well. Usually underwriting makes use of manual procedures as well as rule-based models which take a lot of time and are vulnerable to errors. ML technology can do underwriting much faster than humans slashing the time taken to approve or turn down loans. 

10. Managing False Positives

When banks wrongly reject requests for legitimate financial transactions it is called 'false positives'. The latter causes distress to clients. These lead to loss of revenue as well as client loyalty. Machine learning technology minimizes or eliminates false positives. These lead to happy and loyal clients as well as protect the banks' revenue and profits.

11. Onboarding

ML is extremely useful for onboarding. Clients get to open a bank account in a matter of minutes. ML tools verify their documents in real time. Thanks to the short and seamless onboarding process banks leave a good first impression on new clients. Regulatory compliance is done by ML by analyzing documents such as ID cards, income proof as well as address proof.

12. Processing of Documents

Classification of documents is an important task needing a lot of time as well as resources. ML greatly minimizes the time for the labeling, classification as well as organization of documents. By using OCR (Optical Character Recognition) technology, ML algorithms can digitize the text present on scanned documents. ML is utilized in document intensive procedures such as to extract relevant information from loan applications, identify fake documents as well as automate the process of claiming insurance.

After learning about machine learning use cases in the banking sector, we take a look at a step-by-step guide to implementing machine learning in banking.

Implementation of ML in banking

• Have Clear Goals

Initially define your goals. What solution are you providing? Are you providing a better product or service? The goals will determine the ML algorithm you choose. It will also result in clarity of vision.

• Obtain and Prepare Quality Data

The quality of the data matters. Source data from different avenues such as trends in the market, customer interactions and others. The data should be accurate as well as complete. Next clean the data. Manage missing data as well as lack of consistency.

• Shortlist ML Algorithms to Use

There exist supervised learning algorithms as well as unsupervised learning algorithms. The ML algorithm you utilize is based on your goals and data type.

• Do Training and Evaluation

After choosing the algorithm, feed it with data. Test for the performance of the model for parameters including accuracy as well as recall.

• Deployment

After the testing stage, deploy the model. Merge the latter into your systems. Track the performance model for reliability and accuracy. In the event of unsatisfactory results do retraining utilizing fresh data.

• Fix Ethical Issues

The model should be free of biases and discrimination. Check the model periodically and take action to eliminate biases. Make sure the model is fair as well.

• Constantly Improve

As needs change or there is fresh data, implement the necessary improvement in the ML models. Keep track of fresh developments in the ML space.

With extensive expertise and experience in the banking domain as well as ML technology, CoffeeBeans is well placed to help you achieve your specific and unique goals. We provide stellar quality at competitive rates. Our clients can vouch for our transparency, real-time communication as well as adherence to stipulated deadlines. Reach out to us at [email protected] to know we can help you achieve your objectives and preferences.