Top 4 cases for AI in banking

Artificial intelligence (AI) is driving immense changes in the financial services industry, and banks that get on board now stand to gain a major competitive advantage. Experts estimate AI-driven technology will bolster FI revenues by 34% and reduce operating expenses by 22% within the next 10 to 15 years – but how, exactly, it can do that isn’t always clear. Here, we illustrate the top four use cases for AI in banking to clarify how those extraordinary results can be achieved.

1. Superior risk management, compliance and decision-making 

Analysts predict risk, compliance and authentication projects could save banks up to $217 billion over the next decade. Such initiatives employ powerful algorithms to analyze data, detect fraud and fuel strong decisions. Here’s how real-world banks are applying the technology. 

Fraud detection 

Banks rely on automated fraud detection to avert risk. For years, credit card processors have employed sophisticated algorithms to scan millions of transactions and detect potentially fraudulent purchases. The new breed of technology promises to take fraud detection to the next level. 

KYC and AML compliance 

For example, some banks are testing applications that automate Know Your Customer (KYC) and Anti-Money Laundering (AML) compliance. As regulations tighten, manual processes and outdated systems will become increasingly inefficient, escalating risk and potential liability. AI-driven applications can instantly cross-check millions of documents and transactions to authenticate customer identities and immediately flag suspicious activity to minimize risk.

Credit and underwriting decisions 

Artificial intelligence can also help financial institutions make smarter credit and underwriting decisions. Consider Upstart, a company that has pioneered the first AI-driven lending platform. The software analyzes non-traditional data points such as borrower education and job history, then factors them into consumer credit decisions. 

Imagine using computers to instantly know whether a borrower is likely to pay loans on time, or whether an applicant poses an otherwise unforeseen risk. Technology removes human error and intuition from the equation and empowers sound decision-making. In doing so, it lessens risk and increases revenue.  

2. Enhanced customer experience

AI technologies such as Natural Language Processing (NLP) remove “friction points” when customers interact with banks and leverage data to influence revenues. Here are some real-world applications.  

Account self-service

Interfaces can automate routine and redundant customer service tasks such as password resets and balance checks, or instantly recall customer data to answer basic questions that help customers get what they need fast.  

The result is an enhanced customer experience aided not only by machine interaction, but also by the ability for bank staff to develop deeper, stronger relationships with customers. For example, a bank staff member can spend their time helping a customer diversify their investments rather than helping a customer find a copy of their April 2019 statement. 

Chatbots and virtual assistants 

Chatbots and virtual assistants take account self-service to the next level. They rely on Natural Language Processing, deep learning and a neural network to assist customers with key tasks and even influence revenue. 

For example, customers on your banking platform can be greeted with a chatbot that asks if there is anything it can do to help. A customer might type a question such as “how much did I spend at Walmart in March 2019?” and the chatbot can instantly retrieve the answer.  

Virtual assistants humanize the technology by responding to voice commands. They can even be connected to existing devices such as Alexa and iPhones (Siri) to allow customers instant access to banking. For example, a customer can verbally request an account transfer, and the system can confirm the transaction via voice. Customers can use virtual assistants to check account balances, find the nearest ATM, determine their credit card limits and much more. 

Chatbots and virtual assistants might seem like little more than cool technology, but the conveniences they offer customers coupled with their ability to deliver personalized offers based on deep machine learning have an enormous impact on banks. Consider this: organizations that implement AI for customer service or sales report up to 70% fewer call or email queries and a 33% savings compared to a call with a live agent. 

Biometrics and facial recognition

Image recognition, also referred to as computer vision, enables computers to identify objects, places, people and even handwriting. Such technology eliminates the need for passwords, which enhances the customer experience and improves security. That, in turn, minimizes risk for your bank.  

With biometrics and facial recognition, devices can scan customers’ faces, fingerprints and even irises to grant account access. It eliminates the need for security questions and other measures that can frustrate customers and divert staff from revenue-building opportunities. Handwriting recognition technology can help banks authenticate signatures on important documents – another way to prevent or detect fraud. 

3. Improved operational efficiency

Streamlining operations represents one of the greatest opportunities for AI, as analysts predict banks can save up to $200 billion through back office efficiencies alone over the next 10 to 15 years. Technologies such as robotic process automation (RPA) and intelligent automation (IA) promise to reduce operating expenses by up to 22%, which would have a major impact on any bank’s bottom line. Here’s how artificial intelligence can improve operational efficiency for your bank. 

Contract reviews and reporting

You might already employ robotic process automation for contract reviews and reporting. This specialized software handles routine business processes such as data collection, spreadsheet updates or merging data between applications. The benefit is it quickly and accurately handles repetitive tasks, eliminating staffing expenses and human error. Ultimately, banks see the return through cost savings.  

Straight-through processing and workflow automation

Intelligent automation takes RPAs to the next level by “training” themselves to improve efficiency over time. The technology can analyze unstructured data that resides in PDFs, emails or other formats to automize workflows and eliminate the need for staff to spend tedious hours matching records. 

For example, NACHA estimates more than 60% of ACH payments arrive separately from remittance information. These “stranded” receivables force staff to track down email remittances, then manually enter data. By delaying posting and lengthening DSO, manual reconciliation negatively impacts cash flow. Sophisticated AI technology, however, can automatically match incoming electronic and paper payments to open invoice remittance details from accounts receivables processing systems, with no need for human intervention. 

4. Revenue growth

Banks that implement AI solutions stand to increase revenue by 34% through a variety of intelligent, self-learning applications that influence customer behavior. Here are some real-world examples. 

Personalized offers, upsells and cross-sell recommendations

As stated, chatbots can interact directly with customers on your bank’s portal. But they can do much more than answer simple questions.  

For example, you likely deploy banner ads or pop-ups on your banking platform to motivate customers to consider refinances, auto loans, CDs and other products. Banner ads are hit-or-miss; they might be relevant to one customer but not another. Chatbots, on the other hand, can analyze customer data and personalize offers, cross-sells and upsells. That, in turn, boosts revenue. 

Here’s a real-world scenario. Let’s say John Doe routinely sends international wires. When John logs into your bank’s portal he usually sees a banner advertising car loans or mortgages – ads that aren’t relevant to him. What would happen, however, if he were instead greeted with a chatbot that said: 

“Hi John! I noticed you sent five international wires last week. Did you know other electronic payment options are available at less cost?”

Naturally, John is far more likely to respond to the chatbot’s prompt, and the software can make an instant upsell or transfer the warm lead to a banker for further discussion.  

By analyzing customer spending habits, behaviors, travel locations and other data points, offers can be tailored to each specific customer based on the likelihood they’ll act.

Robo-advisors

Robo-advisors take personalization to the next level. They analyze and process information through multiple layers of the neural network to draw data-backed conclusions. 

For example, a customer might wonder whether they should refinance their home, borrow on equity or take out a personal loan. Another customer might be interested in the best type of loan for their small business. Yet another might be weighing the potential outcomes of different investments. In each case, a robo-advisor can analyze market data against the customer’s needs, then recommend the course of action that will best help the customer achieve their goals.  

By catering to specific customer needs with reliable, accurate advice, offers, and upsells, banks that employ AI-driven applications report a whopping 30% higher sales conversion rate with prospects!

Alerts for at-risk customers

Keeping current customers is perhaps more important than gaining new customers, and AI can help. Computers can continually monitor numerous variables to identify when high-value customers are at risk. They can then alert banking staff to act. 

For example, let’s say a high-value customer suddenly has fluctuating transaction levels and stops logging in to your bank’s portal. Software can automatically alert bank staff, who can then reach out to the customer, save the account and stem attrition.  

Integrated receivables

Next-generation Integrated Receivables (IR) solutions employ sophisticated algorithms and machine learning technologies to match customer invoices with electronic remittances. It eliminates the need for tedious, costly manual matching – a common problem for businesses that have high volumes of ACH receivables. This creates an opportunity for banks, which can offer a compelling, value-added product to corporate customers. 

It works like this: Algorithms scan thousands of remittance documents such as PDFs and emails, then automatically extract details such as vendor names, payment amounts, invoices numbers and dates. The program compares the data with the treasury customer’s open file of invoices to create a three-way match: payment, remittance and open invoice. After a one-time customer confirmation, the self-learning program automatically reassociates all future payments for each vendor account. 

The result? IR solutions can increase straight-through processing rates by up to 95%, representing a significant savings to businesses that presents an opportunity for banks to sell a valuable product.  

With artificial intelligence, banks can deepen customer relationships, offer greater support to sales teams, execute stronger marketing efforts and launch new products – all powerful ways to increase FI revenue. 

Why AI is critical to your FI's future

These use cases illustrate the incredible benefits banks can realize through AI implementation. In fact, by 2030 analysts predict AI solutions will result in $1 trillion in savings for financial institutions. Despite that, many mid-sized banks and credit unions are ignoring the need for artificial intelligence. 

While more than 70% of large banks have already implemented AI, just 2% of mid-sized banks have dipped their toes in the water. Only 13% plan to invest in AI in the near future, and nearly half say it’s not even on the radar. 

Rather than view AI as a challenge, forward-thinking banks should view it as an opportunity. If your competitors fail to implement solutions that can cut costs by 22% and grow revenues by 34%.

Next steps

By now, you should have a good idea of how your bank can implement AI to cut costs and boost revenues. The next step is to ready your organization. Start with your business needs to identify ways artificially intelligent applications can improve efficiencies and bolster sales. Work with leadership to develop an AI Center of Excellence, which works across all departments to ensure successful implementation. 

Plan for quick wins: what initiatives will have immediate, positive impacts? Grow from those. Ultimately, you want to establish a digital culture that embraces artificial intelligence and the role it plays in your bank’s success. 

When you’re ready to take the leap, consult with outside resources. Find a partner who understands the needs of banks and has experience integrating AI solutions for banking. The right partnership is key to success; in fact, a good partner can even guide you through readying your organization and help you make your case to bank leadership, who will need to greenlight the investment.  

As the next disruptor for FI products and services, AI cannot be ignored. Intelligent applications become more efficient and improve bottom lines over time. Early adopters position themselves for success now and in the future, while those who fail to implement AI solutions may be left behind.