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Machine Learning in Banking: How are Banks Best Capturing the AI Opportunity?
Major banks are ahead of the curve when it comes to adopting AI banking as a business strategy – an essential task for any major organization seeking an edge over their competitors. Banks have started to leverage AI and machine learning in banking for both front-end and back-end operations, already leading to more successful business outcomes overall.
With the intersection between machine learning and finance just getting started, take a look at how organizations are using AI in banking today, and how the adoption of AI banking strategies will impact key aspects of their operations – for the better.
Notable Applications of AI in Banking Today
While banks handle a wide variety of tasks and functions, there are several key areas where AI is proving to be a good fit in improving operations and profitability. Here are four major use cases of AI and machine learning in banking operations so far:
1. Customer Service
Customer service is an essential aspect of banking, and often makes the biggest difference in which bank a prospective customer chooses. It’s unsurprising then that this is an area where banks are experimenting the most with AI in banking to enhance customer relationships and improve the overall customer-bank interaction.
Conversational AI is already transforming banking customer service in the form of helpful chatbots, which provide a more personalized online and mobile banking experience for the customer. One of the biggest players on this side of the AI in banking scene is Bank of America’s Erica, the first widely-available virtual assistant for use in the bank’s mobile app. Chatbots like Erica can guide customers through standard banking operations, such as viewing balance information or transferring money. They can also push new products or services at appropriate times, leading to more successful customer adoption and providing more ROI for the bank.
Virtual assistants, backed by machine learning, use predictive analytics to determine the right pathways to direct customers and smooth the process of engaging with the bank. Customers can interact with these AI banking bots through texting or tapping through commands on their screens. A virtual assistant reduces the need to call or visit a bank directly, saving time for both parties. Banking hours can finally become a thing of the past with 24/7 access to virtual help.
2. Fraud Prevention and Security
Identity theft, fraud, and security breaches are common to the banking sector due to the sensitive personal data and money involved. Data security is critical to a successful bank operation and maintaining customer trust. Naturally, organizations use AI banking that is able to detect fraud quickly and more accurately, without the risk of human error overlooking any data or misunderstanding patterns.
AI in banking identifies fraud by referring to a pre-defined set of rules and by analyzing an individual’s past behavior. For example, if someone who has previously made only small purchases suddenly makes a very large one, the machine would flag that as fraud and contact the customer right away. AI is also being used to authenticate and identify customers when they engage with their bank.
With troves of personal data to protect, banks are likewise investing in AI as a cybersecurity tool to better prevent future cyberattacks.
3. Portfolio Management
Investment portfolios are typically managed by a financial advisor or by the customer themselves. AI in banking is moving away from that model by supplementing human decisions with more complete and accurate analyses and risk assessment, helping to ultimately maximize the value of the portfolio. Machine learning can help expand portfolios as well by scanning the global market for new investment opportunities, offering real-time data to inform decisions, and providing a quick sense of market sentiments around the globe.
All of this ties well into an enhanced level of customer service, as virtual assistants are increasingly able to provide personalized investment advice based on the risk level of the individual and current assets.
In all cases, humans make the final decisions but have a wider set of data and recommendations to inform their choices.
4. Credit and Loan Decisions
Credit and loan decisions have historically been made through human analysis of credit scores, credit history, and other past behaviors. This isn’t an exact science, and banks often lose money due to having incorrect or missing data in the database, or due to human error.
AI in banking is, of course, the next evolution to answering this problem. AI can quickly assess data from a prospective borrower and evaluate that against known behavior, patterns of others like him or her, and market trends to determine the probable risk and profitability in providing a loan or credit to that individual.
Using machine learning in this way, banks get a fuller picture of risk and potential return for each person, leading to safer decisions and fewer individuals defaulting on their loans.
Read more about the applications of AI in banking in this Enterprise Edges article by Ryan North and this Forbes article by Sani Abdul-Jabbar.
The Benefits of AI in Banking
AI in banking will permanently shape the way banks operate, inevitably helping both the bank and the customer have a more comprehensive, financially beneficial experience. Experts predict that AI and machine learning in banking will have several important impacts:
1. Reduction in operational costs and workload
By integrating AI banking into operations, banks will reduce the need for manual data entry and other human processes that can often lead to errors. This not only saves time for the individual and the bank but eliminates costly mistakes.
Moving to conversational AI options – such as virtual assistants – will free workers from answering standard questions and handling basic transactions. Instead, bank workers can focus on higher-value tasks, like deepening customer relationships and matching customers to the right services for their needs.
2. A new era of regulatory control
Banks are already one of the most highly regulated institutions in the world and must comply with strict government regulations in order to prevent defaulting or not catching financial crimes within their systems.
With AI’s ability to better detect fraud through behavioral analysis and integration with cybersecurity systems, banks can catch financial crimes much faster and with greater accuracy than humans, which puts them in increasingly greater compliance with regulations. It also reduces the bank’s risk. On top of auditing customer behavior, AI in banking can log key patterns and other information for reporting to regulatory systems, meaning less human data entry is required.
As machine learning in banking is used more frequently, expect to see financial regulations evolve with these changes.
3. Improved customer experience
AI in banking will be able to serve their customers faster, with more efficiency, and at all times of the day. Answers to questions and the ability to enact basic transactions will be at the customer’s fingertips. The trust between customers and their bank will likely increase over time thanks to this, securer data, and better regulatory compliance.
With AI able to provide personalized insights and connect customers to the right products and services for their needs, at the time that they need them, expect to see the relationship between banks and customers deepen and evolve.
Read more about the benefits of AI in banking in this TechTarget article by Ronald Schmelzer.
It’s a wonder what banks have already been able to accomplish with AI and machine learning, and other industries should start to take note.
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