The banking industry is primarily a world of computers and networks. It's boggling that the bulk of the world's wealth is stored in databases, and transactions are simply the exchanges of information over networks.
As impressive -- or scary -- as that might sound, artificial intelligence technologies aim to further revolutionize the way banking is done and the relationships between banks and their customers' experience.
Always-on chatbot sidesteps banking hours
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? Fortunately, AI in banking is one of the most impactful applications of artificial intelligence through the use of conversational assistants, or chatbots, to engage customers 24/7.
Customers are increasingly comfortable with chatbots handling many things, even private conversations regarding bank transactions, bank services and other tasks that don't necessarily require human intervention. For example, Bank of America introduced Erica as a virtual assistant to help with customer transactions, and that has shown significant positive ROI. Many banks have quickly followed suit, although some with mixed results.
In addition to fielding customer service inquiries and conversations about individual transactions, banks have been finding good results in 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. AI-based assistants aware of customer patterns can engage the customer at appropriate times, such as when they are on the bank site or mobile app for more successful conversions and customer experiences.
Furthermore, banks can now segment customers individually, rather than the traditional generalized customer buckets. By using AI-based pattern matching and behavioral analysis, banks are able to make the right offer or suggestion to the right customer, reward their best customers and respond to immediate finance-related customer service needs.
AI in banking aids 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 comply with a stack of additional regulations.
Banking regulatory compliance has significant cost and even higher liability if not followed. As a result, banks are looking to 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 is already making a significant impact on credit card and loan underwriting processes, and 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.
Improving 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 challenge with using AI-based systems for loan and credit decisions is they can suffer from bias-related issues similar to 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 mind. Explaining the decisions of neural networks is challenging and can negatively affect the customer experience.
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. AI in banking is being applied to these processes to eliminate much of the time-intensive and error-prone work involved in entering customer data from contracts, forms and other sources. Improved handwriting recognition, natural language processing and other technologies, combined with intelligent process automation tools, are being used more and more in back-office operations to handle a wide range of banking workflows.
In addition, by replacing these human processes with AI-based automation, banks can impose audit and regulatory control where it previously has been unable to do so. By replacing humans with intelligent, automated assistants, banks can focus their human resources on higher-value tasks, such as offering new services to their customers or improving customer satisfaction. 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.
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 customer better manage their money. 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.
In all these ways, AI in banking is continuing to transform the industry to provide greater levels of value to their customers, reduce risks and increase opportunities involved in being the financial engines of our modern economy.