ML and AI in banking: 6 ways to use smart technology
Feb 23, 2021
Machine Learning and Artificial Intelligence in banking represent versatile, affordable, and customizable technologies that allow financial institutions to solve key tasks as efficiently as possible.
Particularly, they can help reduce costs, improve security, minimize errors, optimize most processes, boost customer service, gain valuable insights, and more.
Let’s take a closer look at the ways of using AI and ML in banking for delivering more value to the customers while increasing company profits.
Fascinating use cases of ML and AI in financial services
Before proceeding to real-world examples of AI in banking, and the benefits it may promise, let’s briefly discuss how this system works.
To put it simply, an AI-powered algorithm is able to analyze huge data arrays and make smart and accurate predictions and suggestions depending on the core issue it’s used to solve.
In fact, AI and ML work solely according to the rules of logic and mathematics, and if the algorithm is programmed correctly, the likelihood of a mistake is critically low.
Due to these features, Artificial Intelligence and Machine Learning found the following application in finance:
#1 Use case — process control and optimization
Financial institutions are complex structures that deal with general and specific tasks: for example, human resource management (general) and mortgage approval (specific).
Each of these processes generates large amounts of data that can be analyzed with the help of Artificial Intelligence and Machine Learning. However, data analysis isn’t helpful without the extraction of actionable insights that can be used for each process control, improvement, and optimization.
For instance, an analysis of the maximum workload of a bank branch can suggest how many employees will be needed on the floor so that customers can avoid waiting in line. Plus, robotic solutions can take over some of the work involved.
Artificial Intelligence in banking can also be successfully used to work with documents. Let’s say that JP Morgan Chase has developed a smart contract system (a solution on the verge of Artificial Intelligence and blockchain) that helps find the necessary document and the information in it quickly.
This system then analyzes the signed contracts, extracting essential information that is used for marketing. It’s estimated that manual review of all these documents would take several years.
#2 Use case — enhanced customer service
The use of chatbots, which have already become common practice for many industries, is the simplest example of AI in banking. These simple but smart helpers are effective enough both to improve the user experience and to intelligently reduce the burden on bank customer support. What’s more, their capabilities have long since gone beyond automated answers to simple questions.
In this example, Wells Fargo has created a chatbot that allows clients to interact with the bank's website more smoothly and manage their finances more effectively. This chatbot may offer to deposit a certain amount after receiving payment, as well as notify the client if their expenses for a certain period exceed the average bill.
#3 Use case — fraud protection and prevention
Fraud prevention is one of the most useful capabilities of ML and AI in finance, simultaneously improving customer service, preventing financial losses for both the bank and its clients, and making financial transactions transparent and safe.
Financial fraud can take on many different forms, from standard credit card fraud to the newly emerging COVID-19 scams, and banks are forced to not only detect fraudulent attempts but also to prevent them.
Machine Learning in banking allows financial institutions to successfully cope with both tasks. For comparison, the methods of anti-fraudulent protection that were used previously determined the act of fraud when it was already committed, that is, after the onset of harmful consequences. ML in banking allows banks to prevent these cases by analyzing user behavior and recognizing both behavioral patterns and anomalies.
Consider the case of a client using a banking application from an out-of-country IP address, without forewarning the bank about their movement. Machine Learning will flag the unusual activity for additional security measures.
#4 Use case — credit scoring and risks assessment
Previously, lending was a rather risky initiative for banks, accompanied by bureaucratic red tape not always leading to correct and potentially profitable decisions.
Today, the use of Artificial Intelligence in banking allows financial institutions to make smarter decisions through almost instant analysis of data on the client's solvency, credit history, and the likelihood of loan repayment.
Machine Learning algorithms can also be used to assess investment attractiveness and successful stock trading. Smart systems analyze a stream of historical and current information, take into account current and future trends, and make accurate predictions about the potential profitability of a transaction on the exchange.
Thus, ML and AI in banking allow companies to reduce risks to almost zero, making each operation as safe and profitable as possible.
#5 Use case — robotic assistance
In general, each of the above examples of ML and AI in finance implies a certain amount of robotic assistance. However, the capabilities of AI-powered robots aren’t limited to just these use-cases.
Robotic solutions equipped with computer vision can be used to improve security. Robots with facial recognition capabilities can be used to identify customers, as well as to detect wanted persons, or those potentially involved in terrorism financing or money laundering. The same algorithm can be used to recognize customers' intentions at the stage of applying for a loan.
In addition, robotic solutions are used to automate routine operations, like closing or opening accounts, drawing up reports, or working with other daily banking tasks and customer requests. Bank of America has deployed an entire infrastructure of 22 robots that cover almost all the essential tasks of this financial institution.
#6 Use case — data-driven marketing
For efficient promotion, financial services require competent, personalized, omnichannel marketing. Fortunately, Artificial Intelligence and Machine Learning in banking can help with this task as well.
A thorough analysis of user information, including online search history, social media interactions, and other behavioral factors, can suggest a customer’s current need (for example, to buy a car on a loan), while analysis of their creditworthiness and credit history can help in developing a personalized offer.
In addition, a thorough analysis of user behavior can not only shorten the sales cycle but also help to retain the customer. For example, an AI system can analyze a combination of behavioral factors and conclude that the user intends to close the account or use the services of another bank. In turn, your financial institution can take appropriate retention measures and offer the client even more beneficial conditions.
Leveraging the power of your financial company with AI and ML
There are a lot of value-promising ways to use Artificial Intelligence in financial markets. That’s how the use of anti-fraud AI solutions could be a worthy priority for many banks around the world, especially taking into account the growing number of fraud attempts due to the pandemic and the rise of e-commerce.
To make the most out of this innovative technology, you have to prepare a well-thought-out strategy for employing AI and ML in banking, based on the specifics of the financial institution’s current tasks. Partnership with a reliable development vendor who has experience in developing fintech tools is the pledge of the success of the venture.
We at Mentalstack understand both the technical and business side of these issues, and are ready to implement highly-efficient custom AI and ML solutions for your financial business!