You know your bank, but how well does your bank know you? Banks want better relationships with their customers in order to provide more personalised and bespoke services. Artificial intelligence (AI) can help banks understand their customers in powerful new ways.

Why AI?

You may have heard the terms analytics, advanced analytics, machine learning and AI. Let’s clarify:

  • Analytics is the ability to record and play back information. You can record customer transactions and report the number of banking services that a customer uses.
  • Analytics becomes advanced analytics when you write algorithms to search for hidden patterns. You can cluster customers based on which banking services they use.
  • Machine learning is when the algorithm gets better with experience. The algorithm learns from examples to predict the banking services that a customer will use.
  • AI is when a machine performs a task that humans find interesting, useful and difficult to do. Your system is artificially intelligent if, for example, machine-learning algorithms infer a customer’s need and recommend a solution.

If you’re in banking, here’s how to make sense of the terms analytics, advanced analytics, machine learning and AI. Click the image to expand.

AI is often built from machine-learning algorithms, which owe their effectiveness to training data. The more high-quality data available for training, the smarter the machine will be. The amount of data available for training intelligent machines has exploded. According to an article on, by 2020 every human being on the planet will create about 1.7 megabytes of new information every second. According to IDC, information in enterprise data centres will grow 14-fold between 2012 and 2020.

For banks used to traditional sources of customer information like transaction history, income and age, the importance of this new data may not be obvious. But using this data to get to know customers is key to creating value. Here’s what it looks like when banks use AI to understand the customer journey and help along the way.

Here’s what it looks like when you apply industrialised AI in banking. Click the image to expand.

Be more trustworthy

Protecting customers from loss and fraud is a good idea — and the law. AI can detect anomalies in streams of financial data. It can monitor financial transactions and customer connections, and make it easier to comply with anti-fraud regulation. Cognizant found that applied AI could lower regulatory compliance costs by up to 30 percent. By getting better at spotting fraud, you increase compliance and strengthen your customer’s trust.

Be more efficient

Good operations planning makes for efficient banks. According to the McKinsey Global Institute, 39 percent of administrative activities can be automated by machine learning.  For banks, AI can forecast demand. It can augment operations by narrowing choices to options that will optimise tasks like staff and branch location planning. You become more efficient by eliminating wasteful practices from consideration.

Be more understanding

Banks succeed due to their ability to form relationships with customers. But according to a study by Capgemini Consulting, only 37 percent of retail banking customers say that banks understand their needs and preferences. AI can learn to hyper-customise the customer interaction. It can improve risk calculation based on personal behaviour. It can infer a customer’s need and recommend the next best offer. Your system learns to understand the customer and offer exactly what he or she needs.

Be more helpful

AI allows banks to provide personalised services in real time. Imagine learning that the customer is planning to take an international holiday and reminding him or her to pay the bills before leaving, increase bank account security services, or issue traveller’s checks. Customers showing interest in high-end purchases online may be candidates for services like pre-approved Internet banking offers with loan deposits available within seconds of completing the transaction. According to Capgemini, banks using AI to reduce customer attrition can increase market share by as much as 12 percent. You respond quickly. Your customers stay happy.

Applied AI is a differentiator

If we see AI as just technology, it makes sense to adopt it according to standard systems engineering practices: Build an enterprise data infrastructure; ingest, clean and integrate all available data; implement basic analytics; build advanced analytics and AI solutions. This approach takes a while to get to ROI.

But AI can mean competitive advantage. When AI is seen as a differentiator, the attitude toward AI changes: Run if you can, walk if you must, crawl if you have to. Find an area of the business that you can make as smart as possible as quickly as possible. Identify the data stories (like detecting fraud or the next best service offer) that you think might make a real difference. Test your ideas using utilities and small experiments. Learn and adjust as you go.

It helps immensely to have a strong Analytics IQ — a sense for how to put smart machine technology to good public use. We’ve built a short assessment designed to show where you are and practical steps for improving. If you’re interested in applying AI to banking and are looking for a place to start, take the Analytics IQ assessment.

See more of Jerry Overton’s thoughts in Wired Magazine: Welcome to the Age of AI-Based Super Assistants.