Evaluate Weigh the pros and cons of technologies, products and projects you are considering.

5 benefits of AI in banking

Despite risks, AI tools are helping banks overcome traditional customer service challenges and streamline back-end processes. Here are five benefits of AI in banking.

The banking industry is primarily a world of computers and networks: It boggles the mind that the bulk of the world's wealth is stored in databases, and transactions are simply an exchange of information over networks.

As impressive -- or scary -- as this digital financial world might sound, it's a realm that artificial intelligence will revolutionize, changing how people do their banking and how banks operate. While AI's use in banking operations carries risks, it is already driving significant efficiencies, reducing fraud and improving the customer experience.  

Here is a look at five benefits of AI in banking, with caveats.

1. Always-on chatbots

There's a reason why people deride banking hours. Banks never seem to be open when you need them most, such as later in the day or on holidays and weekends. Our money doesn't sleep, so why should the banks? One of the big benefits of AI in banking is the use of conversational assistants or chatbots that, unlike their human counterparts, can engage customers 24/7.

Customers are increasingly comfortable using chatbots to handle many standard banking tasks, even private conversations regarding bank transactions, and other bank services tasks that previously required person-to-person interaction. For example, Bank of America introduced virtual assistant Erica on its mobile app in 2018 to help with customer transactions. Three months later, the virtual financial assistant had 1 million users. Many banks quickly followed suit, debuting chatbots on mobile banking apps and websites, although some with mixed results.

In addition to fielding customer service inquiries and conversations about individual transactions, banks are getting good results using chatbots to make their customers aware of additional services and offerings. For example, business customers might not be aware of merchant services and loan offerings that can help resolve payment or credit issues.

Furthermore, banks can now segment customers individually rather than by the traditional generalized customer buckets, allowing for highly personalized service. Supported by predictive analytics and AI tools like and machine learning, virtual assistants can boost sales and improve the customer experience by making the right offer on the right device in real time.

2. Proactive regulatory compliance

Banking is one of the most highly regulated sectors of the economy, both in the United States and worldwide. Governments use their regulatory authority to make sure banks have acceptable risk profiles to avoid large-scale defaults, as well as to make sure banking customers are not using banks to perpetrate financial crimes like fraud and money laundering. As such, banks have to comply with myriad regulations requiring them to know their customers, prevent money laundering, uphold customer privacy, monitor wire transfers and more.

Banking regulatory compliance has significant cost and even higher liability if not followed. As a result, banks are using smart, AI virtual assistants to monitor transactions, keep an eye on customer behaviors, and audit and log information to various compliance and regulatory systems.

Big-data-enhanced fraud prevention has already made a significant impact on credit card and loan underwriting processes. The addition of machine learning algorithms and cognitive technologies is helping those systems stay ahead of the game, as the nature of fraud continues to evolve. By looking at customer behaviors and patterns instead of specific rules, AI-based systems help banks stay on top of their regulatory compliance, while minimizing overall risk.

3. Better decision-making for loans and credit

Similarly, banks are using AI-based systems to help make more informed, safer and profitable loan and credit decisions. Currently, many banks are still too confined to the use of credit scores, credit history, customer references and banking transactions to determine whether or not an individual or company is creditworthy.

However, as many will attest, these credit reporting systems are far from perfect and are often riddled with errors, missing real-world transaction history and misclassifying creditors. In addition to using data that's available, AI-based loan decision systems and machine learning algorithms can look at behaviors and patterns to determine if a customer with limited credit history might in fact make a good credit customer or find customers whose patterns might increase the likelihood of default.

The big challenge with using AI-based systems for loan and credit decisions is they can suffer from bias-related issues similar to those made by their human counterparts. This is due to how loan decision-making AI models are trained. Banks looking to use machine learning as part of real-world, in-production systems need to make sure to factor bias and ethics into their AI training processes to avoid these potential problems. This is especially the case when using AI algorithms, such as deep learning approaches, that are inherently unexplainable.

The issue of explainability is another potential stumbling block. Financial institutions operate under regulations that require them to issue explanations for their credit-issuing decisions to potential customers. This makes it difficult to implement tools built around neural networks, which operate by teasing out subtle correlations between thousands of variables that are typically incomprehensible to the human brain. Explaining the decisions of neural networks is challenging and can negatively affect the customer experience.

4. Reducing bank operating costs and risk

The bank industry is largely digital in operation, but it is still riddled with human-based processes that sometimes are paperwork-heavy. In these processes, banks face significant operational cost and risk issues due to the potential for human error. Robotic process automation (RPA), software that mimics rules-based digital tasks performed by humans, is being applied in banking to eliminate much of the time-intensive and error-prone work involved in entering customer data from contracts, forms and other sources. Coupled with improved handwriting recognition, natural language processing and other AI technologies, RPA bots become intelligent process automation tools that can handle an increasingly wide range of banking workflows.

In addition, by replacing these human processes with AI-based automation, banks can impose audit and regulatory control where they previously have been unable to do so. By replacing humans with intelligent, automated assistants, banks not only improve their risk management but they can also focus their human resources on higher-value tasks, such as client relationship management and customer service. According to Accenture, banks are seeing between 20% to 25% savings in their operations through implementation of intelligent assistants and AI-based systems in their back-office workflows.

5. AI assistants for investing

Finally, some banks are delving deeper into the world of AI by using their smart systems to help make investment decisions and support their investment banking research. Firms like Switzerland-based UBS and Netherlands-based ING are having AI systems scour the markets for untapped investment opportunities and inform their algorithmic trading systems. While humans are still in the loop with all these investment decisions, the AI systems are uncovering additional opportunities through better modeling and discovery.

In addition, many financial services companies are offering robo-advisers that can help their customers with portfolio management. Through personalization, chatbots and customer-specific models, these robo-advisers can provide high-quality guidance on investment decisions and be available whenever the customer needs their assistance.

Dig Deeper on AI business strategies