How Machine Learning is Used in Finance and Banking

Sophisticated, intelligent security systems and streamlined customer services are keys to business success. Financial institutions, in particular, need to stay ahead of the curve using cutting-edge technology to optimize their IT and meet the latest market demands. The banking landscape is constantly changing, and the application of machine learning in banking is arguably still in its early stages. However, banks using AI and ML are quickly going to overtake their competitors.

Machine learning solutions are already rooted in the finance and banking industry. 85% of respondents use some form of ML and AI, according to a 2020 survey by the Cambridge Centre for Alternative Finance, with fintech companies being slightly ahead of incumbents in the adoption of AI. For example, many financial organizations have already adopted machine learning in risk management (56%) and revenue generation. Machine learning offers countless opportunities to deliver personalized banking solutions leveraging customer data.

In this article, we’ll examine a number of use cases and highlight the benefits of ML in banking, presenting you with suggestions for implementing ML into your business.

Machine Learning in Banking Statistics

To substantiate, let’s look at some recent statistics about ML adoption in the fintech market.

• Financial companies employ 60% of all professionals who have the skills to create AI systems
• Banks around the world will be able to reduce costs by 22% by 2030 using artificial intelligence technologies, saving up to $1 trillion, according to a forecast by the research company Autonomous Next.
• It is expected that face recognition technology will increase its annual revenue growth rate by over 20% in 2020, and will be implemented in the banking sector to help prevent credit card fraud.
• Implementing artificial intelligence and machine learning in banking will be critical for financial institutions to stay competitive and thrive in the market by 2024; both web and mobile banking adoption among US consumers will increase, reaching 72.8% and 58.1%, respectively, according to Insider Intelligence
• Aggregated cost saving for banks from AI applications is expected to be $447 billion by 2023, according to Insider Intelligence’s AI in Banking report.

As the world moves towards ML, it’s crucial to consider digital transformation in financial organizations to reinforce business, making sure to select targeted technologies that best suit your business model.

Here are some of the most impactful ML and AI trends in the fintech industry that will shortly become industry standards:

ML Trends in the Fintech Field

1. The Metaverse Impact

Everyone is talking about the Metaverse! This hyper-connected virtual ‘world’ of interactions and transactions will have a profound impact on the fintech industry. The new frontier of cryptocurrency, digital tokens, and NFTs, is restructuring online finance. The metaverse gives birth to a host of exciting opportunities, including:

2. Conversational AI and Client Engagement

Customers today expect 24/7 communication with businesses and rapid responses. AI-powered interactions with financial institutions can meet such expectations. With the help of data analytics, ML chatbots can create natural interactive experiences with real-time problem-solving and a high level of personalization.

3. Low code and no-code AI solutions

It’s becoming more and more popular to develop highly automated AI and ML solutions for finance tailored to your business needs with the help of low code or no-code AI tools. 65% of organizations are planning to use low-code or no-code solutions to reduce software development costs and time-to-market, enabling them to rapidly embrace industry changes, according to Gartner’s research. With low code or no-code AI, even those without extensive coding experience can create, edit and update apps that can deliver a seamless customer experience.

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Top Use Cases of Machine Learning in Banking and Finance

Banks are constantly expanding their use of ML to enhance customer experience and back-office operations.

Machine learning tools allow banks to transform their data streams into actionable insights, from operations to business development and marketing. Usually, businesses turn to machine learning use cases in fintech for faster support, more robust security, and smooth, sleek processes. This section will illustrate the most popular machine learning use cases in banking.

1. Onboarding and Document Processing

Machine learning in banking goes far beyond fraud detection and transaction processing. Document processing is traditionally a labor-intensive process requiring effort and time. Machine learning can ultimately reduce time spent organizing, classifying, labeling and processing documents. First, you need to run copies through the Optical Character Recognition (OCR) process, and then machine learning algorithms can process the text on scanned documents to analyze the context. With the help of this information, the machine learning model classifies and indexes everything for future reference.

Machine learning-based document processing is also helpful for traditional banks that still rely on paper forms during the new client onboarding process. Whether it’s a scan of an ID or an invoice, machine learning is a highly scalable and powerful tool for onboarding. Customers can open a bank account in just a few minutes, completing necessary checks in real-time. Such machine learning use cases help businesses build healthy and valuable relationships with their customers.

The client onboarding process is significantly streamlined by machine learning. It can determine how small changes in the consumer’s decision journey influence conversion rates. By analyzing thousands of user actions, machine learning will help financial organizations enhance the way consumers interact with their systems. In addition ML can offer new employees access to corporate information, email accounts, and other company knowledge resources. Machine learning algorithms can remove access from past employees, and perform other essential system security operations.

2. Fraud Detection

Fraud in the fintech sector is becoming a common problem for many companies, regardless of the number of customers and size. Machine learning in finance can evaluate substantial data sets of simultaneous transactions in real-time. At the same time ML can minimize human input by learning from results and updating models. With the help of machine learning, financial organizations can label historical data as fraudulent or not fraudulent and continue to enhance their ability to detect possible potential fraud by learning from previous patterns of behavior. ML can help banks quickly identify user activity, verify it, and respond to cyber-attacks quickly and effectively.

In addition to rule-based fraud detection, machine learning allows for skimming through large amounts of data in real-time and minimizing human input. Additionally, it improves user experience by simplifying the identity verification measures. We’ve illustrated a few differences between rule-based and ML-based fraud detection in the picture below.

3. Regulatory Compliance

Dealing with regulatory issues in the fintech field and banking is resource intense, especially in terms of time and money. Even if companies invest billions of dollars in regulatory compliance, this doesn’t necessarily guarantee effective compliance. That’s why machine learning among the top machine learning use cases in finance are regulatory technology applications (RegTech). Machine learning algorithms can scan and learn from regulatory documents, and they can easily recognize correlations between guidelines. They can track and monitor regulatory changes when they occur. With the help of machine learning systems, financial organizations can automatically identify anomalies, monitor transaction data, and ensure that all transactions comply with regulatory requirements. To summarize, the benefits of ML in banking are the following:

• Regulatory work can be carried out faster with minimized risks of non-compliance, reducing manual task resources
• Machines can replace humans in complex, computational, time-consuming tasks
• Financial organizations can comply with laws, regulations, and other expectations more closely

4. Credit Scoring

Credit scoring is probably the most promising use case of ML in banking. It evaluates how well the bank’s customer can pay and is likely to plan to pay off debt. As there are billions of unbanked people around the world and less than one-half of the population eligible for credit, there’s a strong need for credit scoring solutions. Machine learning scoring decisions are based on various data, including work experience, total income, transaction analysis, and credit history. It’s a mathematical model which is based on accounting and statistical methodologies. As a result, machine learning models can provide more personalized and sensitive and reliable credit score assessments and give access to credit to more people. Unlike human scorers, machine learning systems can evaluate borrowers without emotional bias. Additionally, with the help of machine learning in banking, companies can remove gender, racial and other conscious or unconscious bias and serve a wider audience more equitably. As you can see, ML in credit scoring brings a whole range of benefits, with customers receiving loans in a few clicks without leaving their homes.

5. Сustomer Retention

Practical customer support is an essential part of a successful financial business. Machine learning in the financial industry helps companies meet their customers’ needs with personalized offers and services by analyzing customer behavior in using products. The most crucial aspect of ML in customer retention is that it helps companies monitor and forecast customer churn by monitoring changes in their behavior. Acquiring new customers is much more expensive than retaining existing ones. In this case, machine learning helps businesses identify customers who are willing to leave and act quickly to retain them. Whether it is somebody who forgot about the service or a customer that had a negative experience, machine learning helps increase trust among customers and retain their interest for longer.

Machine learning is a powerful tool that helps banks with customer support. Financial organizations turn to machine learning systems to fasten the support process and determine what a particular customer needs. What’s more, ML-powered systems learn from their experience and improve over time, and are capable of processing increasingly more complex information.

One of the best examples of a ML use case in banking is chatbots. They provide real-time and human-like help that increases user experience and saves the company’s organizational resources. One huge advantage of chatbots is that they learn along the way, making conversations between companies and customers more natural, customized, and ultimately more helpful. As a result, a whole range of service-oriented organizations can benefit from intelligent chatbots.

6. Payments

The payment industry also benefits from incorporating machine learning in payment processes. The technology allows payment providers to reduce transaction costs and therefore attract more business. Among other advantages of machine learning in payments is optimizing payment routing based on pricing, functionality, performance, and much more. By processing various data sources, machine learning systems can smoothly allocate traffic to the best performing combination of variables. This feature allows financial organizations to deliver the best results to merchants based on their specific objectives. Today, there are numerous machine learning applications for finance on the market, which serve as an excellent tool for companies to generate deep value by solving widespread problems. With the help of machine learning in payment processing, payment providers can identify whether a transaction should go ahead or first be routed to a two-step verification page.

7. Investing

Machine learning technology can also be used to optimize how investment companies operate. For example, trading companies apply machine learning-powered algorithms to simultaneously monitor trade results, financial news, prices, and other data resources to detect patterns that change the prices of financial documents. The algorithms can execute trades at reasonable prices, reducing human errors that otherwise might result in the losses of millions of dollars.

Machine learning also makes financial markets more accessible with automated robo-advisors that make investment propositions automatically based on a customer’s preferences. Such advisors can create personalized portfolios and help clients accomplish their financial goals, including retirement funds, savings, or protection from inflation.

8. Anomaly Detection

Anomaly detection is one of the most challenging areas in the asset-serving sector of financial organizations. Anomalies can occur because of accidents, incompetence, or system errors in day-to-day processes. For the fintech industry, it’s critical to detect anomalies as they may be related to such illegal activities as account takeover, fraud, network intrusion, or money laundering, which may cause unexpected outcomes. There’re different ways to address the challenge of anomaly detection, and machine learning is one of them. Machine learning anti-fraud systems for finance can find subtle events and correlations in user behavior. It compares many variables in real-time and can process large datasets to identify the likelihood of fraudulent transactions.

Real-time anomaly detection helps financial institutions quickly respond to deviations, saving billions of dollars. Manual anomaly detection is not scalable to millions of consumer transactions every day. In this sense, anomaly detection systems fuelled by machine learning can maintain real-time responsiveness and comb through millions of data points every second helping finance companies increase their efficiency.

Benefits of Machine Learning in Banking

Banks turn to machine learning systems for multiple reasons. Here, we list the most common benefits financial organizations get from ML.

1. Personalized offers

A large variety of information about user behavior allows banks to find out what customers want at any given moment and what they are willing to pay for. For example, based on the ads the client was looking at, banks can offer personalized loans after analyzing all possible risks and their solvency. Optimizing the customer footprint helps banks discover subtle tendencies in customer behavior and create a more customized experience for each client.

2. Reduced operational costs and risks

Also, human interaction brings many benefits, and there’s one significant disadvantage. Errors are still common, and they can result in severe losses. Even experienced employees can make wrong decisions which influence the company’s liability. That’s why banks and other financial institutions actively embed Ml and AI systems for finance in their operations. For example, robotic process automation (RPA) software is used to mimic digital tasks performed by humans and reduce many of the error-prone processes (for example, with entering customer data from forms or contacts). With the help of natural language processing and other ML technologies, such RPA bots, a wide range of banking workflows can be handled.

3. Improved loan and credit decisioning

Another benefit of machine learning and artificial intelligence services in banking is that they help banks make safer and more informed loan and credit decisions. Many banks are currently efficient at using credit history, banking transactions, and customer references to determine if a company or an individual is creditworthy. Additionally, ML-based systems can look at patterns and behaviors to see whether a customer with limited credit history makes a good credit customer. The only problem with ML-powered systems is that they can have bias-related issues, and this is all due to the way ML models are trained. However, many institutions are eager to use machine learning systems in banking to weed out bias and take ethics into their ML training processes.

4. Improved investment evaluation

The investment valuation process consists of multiple complex calculations. The method includes collaborating with different teams responsible for various aspects of investment asset management, product specialists, and portfolio managers. These teams should consider different approaches to investing. The ML solution for this is an application that can process large amounts of data from other sources in real-time while learning biases and preferences regarding risk tolerance, investments, and time horizon.

5. Fraud detection and compliance

We’ve already talked about fraud detection in machine learning use cases in the banking section. The main idea is that with the help of machine learning systems can sift through large amounts of data by applying different algorithms and identifying fraud. When it comes to regulatory compliance, ML virtual assistant support banks monitor transactions, watch for customer behavior, and log information to additional compliance and regulatory systems minimizing overall risk.

The list of ML benefits in banking is seemingly endless, and here are only a few most common reasons banks apply ML in their workflows. Now you know a lot about the power ML may bring to any financial organization, but how do you implement it into your organizations with minimal effort? Our experts from the AI Practice put together a number of practical tips on smooth ML implementation.

Things To Consider When Implementing ML in Banking

We’ve lined up four implementation steps for ML in banking that will give you an understanding of which efforts and risks it will require.

1. Identify your business case

Applications of machine learning in financial software development are numerous, and each of them has its unique contributions improving efficiency and contributing to greater business success. Begin by choosing a business case that helps you determine the project scope and that will impact your datasets the most.

2. Gather and prepare data

When banks prepare data for machine learning algorithms, they should consider data quality and compliance with regulations. Low data quality can zero out efforts spent on developing machine learning tools. And as banks possess a vast amount of data about customers, losing it is a frivolous mistake. If financial organizations can provide data privacy, customers will have less hesitation in allowing them access to their personal data.

3. Select the right algorithm

To reduce error frequency, it’s better to select the most suitable machine learning algorithm and methodology and understand where bias may come from and how to root it out. When chosen correctly, machine learning algorithms bring great value to finance, and understanding them properly helps you to identify which have the most positive or negative impacts on business.

4. Help your people adapt to change

Employees might feel uncomfortable or confused with the upcoming change implementation when hearing about ML in their workforce. They can be scared of potential job cuts or other perceived threats that machine learning may pose. That’s why it is vital for any business to assess employee willingness to accept and adapt to change. Your employees should understand how machine learning in banking will impact their work lives and make the relationship between work and life more manageable. In this sense, human-machine collaboration is one of the top-performing principles that companies must keep up-to-date with to maintain competitive advantage. Therefore,a transformative change management strategy and approach is key to facilitating changes with low levels of resistance and higher levels of employee acceptance.

Final Thoughts

AI and machine learning in banking is truly revolutionary, and will ensure financial services are safer, more innovative, and more efficient. Leveraging AI and ML technologies in banking will drive sales through meaningful engagement if initially targeted at high-value cases and then leveraged and scaled across organizations. Adopting AI solutions has become the foundation of new enterprise value propositions, and has become not simply an addition to current processes, but a critical strategic necessity for success in the global finance industry. Join the revolution now, and stay ahead of the curve!