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AI in banking industry brings operational improvements

Banks using AI tools are finding that the technology can help them overcome traditional customer service challenges, but barriers to widespread use remain.

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 exchange of information over networks.

As impressive -- or scary -- as that might sound, artificial intelligence technologies are aiming to further revolutionize the way banking is done and to improve the relationships between banks and their customers.

Always-on chatbots sidestep 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 handling many things using chatbots, 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, an AI assistant to help with customer transactions, and the bot has shown significant positive ROI. Many banks have quickly followed suit, although some saw mixed results.

In addition to fielding customer service inquiries and conversations about individual transactions, banks have been finding 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. AI-based assistants that are aware of customer patterns can engage the customer at appropriate times, such as when they are on the bank site or mobile app.

Furthermore, banks can now segment customers individually rather than sorting them into 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 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 that banks have acceptable risk profiles to avoid large-scale defaults, as well as to make sure that banking customers are not using banks to perpetrate financial crimes, like fraud and money laundering.

As such, banks have to comply with a myriad of 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 a significant cost and even higher liability. As a result, banks are looking to smart, always-on AI assistants to monitor transactions, keep an eye on customer behaviors, and audit and log information for various compliance and regulatory systems.

Big data-enhanced fraud prevention is already having a significant impact on credit card and loan underwriting processes, and the addition of machine learning 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 can help banks stay on top of 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 more profitable loan and credit decisions. Currently, many banks are still 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, are missing real-world transaction history and are misclassifying creditors. In addition to using data that's available, AI-based loan decision systems can look at behaviors and patterns to determine if a customer with limited credit history might, in fact, make a good credit customer, or to find customers whose patterns might increase the likelihood of default.

The challenge with using AI-based systems for loan and credit decisions is that 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 explain 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 a challenge.

Reducing bank operating costs and risk

The bank industry is largely digital in operation, but it is still riddled with human-based processes that are sometimes paperwork-heavy. With 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 couldn't.

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 20-25% savings in their operations by implementing intelligent assistants and AI-based systems into 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 Swiss-based UBS and Netherlands-based ING are having AI systems scour the markets for untapped investment opportunities to inform their algorithmic trading systems. While humans are still in the loop with all these investment decisions, the AI systems are uncovering additional opportunities via better modeling and discovery.

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

In all these ways, AI in banking is continuing to transform the industry to provide a greater level of value to their customers, reduce risks, and increase opportunities as the financial engines of our modern economy.

This was last published in July 2018

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