Three Ways Artificial Intelligence is Transforming Banking

[Editor’s note: This is a blog post from Nathan Snell, Chief Innovation Officer at nCino. nCino is a silver sponsor at LendIt Fintech Europe 2018, which will take place on November 19-20, 2018 in London.]

The financial services sector is ripe for the benefits of artificial intelligence (AI) and machine learning. According to a Narrative Science research brief, widespread adoption of cognitive systems across a broad range of industries will drive worldwide revenues from nearly $8 billion in 2016 to more than $47 billion in 2020, with banking named as one of the top two industries to lead the charge. Not only that, but a recent Accenture survey of 1,300 nonexecutive bank employees reports that 67 percent believe AI will improve their work-life balance, and 57 percent predicted increased career opportunities based on this trend.

By now, most bankers are aware of AI. Most understand its potential to transform the industry. And a majority recognise they will fall behind if they don’t embrace AI within the next two years.

Yet, banks have been slow to adopt this technology, lagging many other industries. Globally, only 15 percent of financial institutions have deployed an AI solution to date, while another 22 percent expect to go live in the next 18 months. Larger financial institutions are deploying AI solutions more quickly than smaller banks and credit unions, with security and risk applications leading the pack, followed by personalisation and communication applications.

So, how are banks currently using AI, and how can they leverage this technology more effectively?

The Power of Automation

The first question we must ask when evaluating tasks is whether a human will add value to the process, or if it is mundane and repetitive enough to benefit from automation? In addition, the onus falls on creators and developers to make this experience as seamless and intuitive as possible, making any so-called “retraining” of humans to work with machines unnecessary. Some financial technology (FinTech) firms have already introduced time-saving functions such as optical character recognition (OCR) for reading tax returns and other financial documentation, eliminating the need for humans to input data into the system. This relatively simple application of AI is already saving workers hours of manual data entry and rekeying per week, while also reducing costly errors. Most importantly, it allows them to focus on more human-centred tasks, such as building stronger customer relationships.

The Intelligence of Machine Learning

Machine learning, a complementary technology to artificial intelligence, greatly favours early adopters. Because it represents the ability of a system to compare observed reality with practiced results and apply these learnings to foster continuous improvement, it has a compounding effect. As the database grows and the machine increasingly learns about your institution’s customer base, the algorithm becomes ever-more valuable. One example of this is the chatbot, a common application of AI. Not all chatbots utilise machine learning; some are simply programmed to answer a wide range of common questions and remain in a constant, static state without learning from new queries posed by customers. When machine learning is added to the algorithm, however, it creates a far more sophisticated and powerful chatbot, one that can learn to anticipate unusual questions or requests, evolving to serve customers comprehensively and accurately. The more the service—in this case, the chatbot—is used, the better it gets, differentiating your institution in a way that is difficult for your competitors to match.

The Single Platform Solution

Particularly for mid and large-sized institutions that grew over time through mergers and acquisitions, the problem of organizational and systemic siloes is significant. If a bank or credit union is housing and running processes across multiple, disparate systems, it can be difficult to implement cognitive technologies that provide a complete and accurate picture of the customer. Users are forced to jump out of the systems they are currently using to try and implement AI solutions, which is cumbersome and inconvenient. Moreover, without a true 360-degree view, the goals of using AI to enable deep customer insights, efficient customer onboarding and customized next product offers based on need will never be realized. This challenge can be counteracted by deploying a single, end-to-end bank operating system across the organization. Via a truly holistic customer engagement platform, every employee is given access to the same information, allowing the right insights to be delivered to the right person at the right time in the right place.

As you can see, there are many different ways to leverage AI at your financial institution, from creating custom-built models to using newer embedded AI solutions which will allow you to get started without making a huge investment. No matter which route your institution chooses, the most important thing is to start now so that you can reap the benefits for years to come. Those institutions that can successfully harness the enormous promise of using AI to automate back-office processes, gain valuable customer insights, and create a better, faster customer experience today will win and retain the loyalty of their customers tomorrow.

To learn more, please download nCino’s complimentary white paper, “The Future is Now: How Artificial Intelligence is Transforming Banking.”

References:

  • The Rise of AI in Financial Services, Research Brief, Narrative Science, 2016.
  • “How artificial intelligence is reshaping jobs in banking,” by Penny Crosman, American Banker, May 7, 2018.
  • AI in Banking: The Next Frontier in Customer Experience, Digital Banking Report, Issue 250, September 2017.

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