Using Intelligent Automation to Bank Smarter, Not Harder

By this point in 2019, most consumers and companies are somewhat familiar with the concept of artificial intelligence. Executives and consultants have discussed its application in financial services for years; lately, the conversations have been brisk and some organizations are doing more than just talking. Many tangible AI use cases have emerged at financial institutions of all sizes over the last 12 months, and intelligent technology is beginning to make an impact on banks’ productivity and bottom lines.

Still, AI remains a largely abstract concept for many institutions. Some of the biggest challenges these banks face in preparing and executing an AI strategy starts with having a too-narrow definition of these technologies.

Technically, AI is the ability of machines to use complex algorithms to learn to do tasks that are traditionally performed by humans. It is often misrepresented or misunderstood in broader explanations as a wider range of automation technologies — technologies that would be more appropriately characterized as robotics or voice recognition, for example.

Banks interested in using intelligent automation, which includes AI, robotic process automation, and other smart technologies, should target areas that could benefit the most through operational efficiencies or speed up their digital transformation.

Banks are more likely to achieve their automation goals if executives shift their mindsets toward thinking about ways they can apply smart technologies throughout the institution. Intelligent automation leverages multiple technologies to achieve efficiency. Some examples include:

• Using imaging technology to extract data from electronic images. For example, banks can use optical character recognition, or OCR, technology to extract information from invoices or loan applications, shortening the completion time and minimizing errors.
• Robotic process automation, or RPA, to handle high-volume, repeatable manual tasks. Many institutions, including community banks with $180 million in assets up to the largest institutions in the world have leveraged RPA to reduce merger costs, bundle loans for sale and close inactive credit and debit cards.
• Machine learning or AI to simulate human cognition and expedite problem solving. These applications can be used in areas ranging from customer service interactions to sophisticated back-office processes. Some industry reports estimate that financial institutions can save $1 trillion within the next few years through AI optimization. Several large banks have debuted their own virtual assistants or chatbots; other financial institutions are following suit by making it easier and more convenient for customers to transact on the go.

What are next steps for banks interested in using AI? Banks first need to identify the right use cases for their organization, evaluating and prioritizing them by feasibility and business need. It’s more effective to start with small projects and learn from them. Conduct due diligence to fully assess each project’s complexity, and plan to build interactively. Start moving away from thinking about robots replacing employees, and start considering how banking smarter – not harder – can play out in phases.