Machine Learning in the Banking Industry: 4 Best Practices

Any financial institution should be able to stay up-to-date with the latest customer demands, especially when it entails security and customer services. Scalability and on-demand customization are small challenges that have to be resolved with new technologies. Particularly, small institutions are under tremendous pressure to modernize their IT systems. Customers demand an excellent online experience, as everything is always changing in the banking industry. Even with so much technology, the application of machine learning in banking is occurring at a slow pace. However, the banks that are using ML have more success, including the fact that the banking industry is highly competitive.

Banks are always trying to find the best way to analyze customer data in an effort to better understand their needs. To achieve this, banks need access to necessary communication as quickly as possible in order to provide products that will be of use to their clients. Personality analysis using machine learning provides an efficient way for banks to assess the needs of customers accurately.

How else can machine learning help banks? Read this article as we discuss the benefits of this technology. We will also talk about the best use cases for banking machine learning.

Top Benefits of Machine Learning in Banking

Banks can benefit from ML since it helps adopt great management in an organization, enhance customer satisfaction, get simplified deployment and operations — let’s make a fair balance by describing the benefits.

1. Improved decision making

One of the benefits of machine learning in banking is improved decision making. As compared to traditional methods, artificial intelligence helps banks to calculate credit scores accurately. The main reason ML can do this is that it can provide an objective evaluation without any bias. The huge amount of data collected from the potential borrower assists banks in making better decisions.

2. Better risk management

AI and ML reduce risks for both customers and banks through accurate reporting. Artificial intelligence can also make predictions based on transaction history after giving credit to customers. Employees have more insights into credit risk testing. Early detection of errors and the availability of potential future risks helps the banking industry to prepare in advance.

3. Prevention of fraud

Credit card fraud is a huge problem in the banking industry. Machine learning for banking can significantly lower the number of fraudulent activities. The majority of fraud occurs when customers pay for products, whether online or offline. Machine learning in banking prevents this from happening in several ways. For example, facial recognition can be used to confirm the person using a credit card is the owner.

4. Improved customer experience

With technology changing almost every aspect of life, consumers are looking for better services and are eager to get the same from banking institutions. At the same time, banks that can provide more security and a personalized experience would attract more clients. Customers want digital banking products that are easy to use. One way in which ML improves the overall experience and services is by reducing the time it takes to make credit decisions and banking operations. Loan applications, which used to take weeks, can now be made within days. Machine learning can make an unbiased analysis based on several credit factors.

5. Internal operational solutions

Machine learning in the banking sector has greatly changed internal operations for the better. Automation reduces the time staff spends on redundant tasks. Therefore, resources can be allocated towards improving the overall experience. Robots perform routine tasks with minimal risk of errors. So a bank can provide efficient solutions while automation gives employees the chance to pay more attention to the most important tasks.
Using ML has so many advantages, with the most important one being internal operational solutions today. Robots can go through a customer database at record time, thus reducing the need for employees to do this manually.

6. Marketing and lending solutions

ML and AI in fintech collect data and also search for specific patterns that help banks make better marketing predictions. Examples of predictions that ML can make include:

• Changes in currencies,
• The best investment ideas,
• Credit risks,
• The optimum loan agreement for a client.

This data assists a bank in deciding where to invest, thus increasing their revenue. It also provides more accurate information on how to attract new clients.

7. More personalization

Banks can benefit from ML since it helps adopt great management in an organization, enhancing customer satisfaction, and providing more personalized and simplified operations and support.
Big data can give their clients and potential consumers a personalized experience in the banking field. ML in the banking industry is all about creating safe yet accessible financial services and data.

Access to the client’s predictive behavior and purchasing algorithms puts banking experts and institutions in a better position of understanding the market, which services to invest, and what relevant messages to drive. Financial institutions can use AI to create an individual experience for each user by tracking their transaction history and making corresponding changes in the operations.

Application of Machine Learning in the Banking Industry: 4 Best Practices
Some of the top banks in the world are able to meet customer expectations by using ML. Check out some applications for machine learning in banking.

8. Customer service

Just like other fields, the main driving force towards machine learning applications in banking is consumer demand. Customers want a secure and personalized approach to banking. Since banks rely heavily on customer loyalty, they have no other choice but to adopt technologies that will meet the ever-changing demands of customers and to use that technical competence for development further products.

Machine learning and artificial intelligence improve customer service in so many ways. One of these is customer support. The only way a bank can secure a future with current clients is by offering top-notch customer support. The main problems banks face when it comes to customer service include:

• Lack of a personalized approach to customer complaints;
• Slow services;
• Inability to resolve issues;
• Limited channels.

All these lead to low retention rates. Banks need to respond to client queries fast. ML and AI, through automation of major tasks in a bank, make this possible. Banks can thus create more accurate, cheaper, and productive ways to address customer complaints. Chatbots and AI assistants reduce the time customers have to wait before being served, thus reducing the workload of bank employees. Some of the latest chatbots are capable of performing simple tasks like sending notifications.

For example, a bank using data science to improve customer support is the Bank of America. The Bank of America has incorporated data science with its mobile banking to reduce the burden of customer support centers. Because of tech competence in machine learning, it is feasible to deal with many customer transactions, including the most complex tasks. Through machine learning for online banking, the Bank of America has become one of the top banks.

9. Fraud detection

Being used in banking, machine learning is helpful in detecting fraud. It is also applied to prevent fraudulent activities involving credit cards and insurance. Customers want to only work with banks that provide the very best cybersecurity. The level of credit card fraud is at its highest, with £112 million in banking losses in 2019 in the United Kingdom. So banks have to work extra hard to make sure that both customers and staff are protected from fraud.

The best way to prevent fraud is early detection. This gives the bank ample time to block any activity on the affected account, thus preventing losses. Machine learning and AI are effective tools to create schemes that can detect and prevent fraud. Because every customer’s data is unique, banks need experts who can provide practical knowledge on how to forecast, analyze, and classify information. For example, Citibank has invested in machine learning algorithms that provide real-time data that can alert the bank about imminent fraud.

10. Risk assessment

ML in the banking sector reduces errors. So financial firms have more accurate reporting. Automation of credit risk testing limits the risks of losses for both banks and clients. By looking at history, ML and AI can provide more accurate foresting before banks can finance loan applications. This, in the long run, helps banks take the necessary steps to curb any potential problem.

Algorithms can scan huge amounts of data within minutes. This is way faster as compared to humans. What’s more, is that they do this with lower chances of making errors. This, combined with big data, helps banks to make more informed decisions about credit.

Banks need to analyze investment risks, as well. Financial firms rely on making successful investments in order to make profits since investing in stocks is a very risky step. For investment banking, machine learning provides risk modeling that not only provides suggestions but also predictions for investments in stocks.

11. Marketing

Success in trading is all about meeting the customer’s needs. Any offer that a bank makes should meet the particular needs and concerns of a client. With ML in banking, it is possible to create personalized schemes to market a bank. A great way to identify customers for a new product is through data mining.

Data analytics and machine learning projects require a training data set. This is what makes algorithm training possible. By looking at demographics, purchase behavior, and history, data scientists can determine the chances a customer will buy a new product. Sales prediction machine learning solutions analyses external and internal business data to provide accurate demand and sales forecasts of a bank product.

Banks are more likely to make a more personalized scheme on attracting new customers with banking deposit prediction. This also allows banks to improve relationships with each customer.

Our Experience

Fayrix is a software development company whose goal is to provide personalized solutions for each company no matter the size. We make it our responsibility to ensure that each client gets quality software on time and our case studies speak for themselves.

Our experts are trained in machine learning, big data, mobile application development and custom software development services. We’ve proved our expertise by creating an ML-based product for one of the biggest US banks – Santander Bank. We’ve solved the issue of customer choice forecasting by building a predictive model. It can predict the customer’s choice of banking products based on their social-demographic data if they used the bank services over the past 18 months.

Final thoughts

In order to remain competitive, financial institutions need to switch to machine learning. Machine learning in the banking industry helps provide more customer-based solutions, increases customer retention, and saves money on acquiring new ones. Processing data quickly, automation of internal processes and accurate predictions help financial institutions make better decisions and profit from reasonable investments. Additionally, chatbots can process customer requests faster, freeing valuable employee time for more important tasks and increasing their productivity.

If you’re interested in making your bank more profitable and seeing instant results, contact the Fayrix team to get consulted on your ML-based solution. Not sure which technology you will need in your future project, check Fayrix’s technological competencies and choose the best solution that will solve all your business goals.