As consumers, we use AI platforms every day. Google uses AI to auto-complete your searches. Amazon builds personalized product recommendations based on the things you’ve bought in the past. Did you enjoy House of Cards on Netflix? Give it a 4 star rating and AI will suggest similar content that you’re bound to enjoy.
These platforms work because we supply them with the fuel they need to learn and adapt to our changing preferences and habits. A massive volume of data is created each year, and it’s growing in a hurry as we do more online. This is true for businesses too, as they turn to digital channels to transact and operate. It’s an area that’s experiencing explosive growth, and as technologies improve, we’re developing innovative ideas to apply to AI to grow business in compelling new ways.
So, what does this mean for banks? Well, let’s assume that you are looking for new ways to apply AI to your business; in fact, you know this is essential to your competitive advantage. But historically, this has been expensive, and it’s been difficult for you to deliver on that analytics initiative when you can’t make sense of the data assets that you have access to.
Fortunately, advancements in the key technologies underlying AI are rapidly changing the economics of your digital initiatives. Now, we have banking solutions being built on four AI technologies that drive insight discovery, outcome predictions, and task automation. They are: Big Data, Machine Learning, Natural Language Processing, and Predictive Analytics.
AI Technologies – A Primer for the Non-Technical
Big Data is the foundation of your AI solution. Big Data provides the raw building materials –structured or unstructured information – that are needed to identify patterns, surface insights, and make predictions. Your Big Data is found in the disconnected CRM solutions, core banking, transactional, third party, and social data streams that your firm maintains or has access to. Chances are that you’ve already implemented a Big Data Solution or Data Lake on a platform like Apache Hadoop or Amazon S3.
Next, we have Machine Learning, which involves training computers to achieve a result by describing the desired outcome, and then feeding it the data needed to produce that result. Machine Learning is being used to enable cognitive services that enhance customer management at the point of service. Platforms you might be familiar with include IBM Watson, Google Cloud, and Microsoft Azure.
Third, we have Natural Language Processing, which is used to find patterns within large volumes of unstructured data, such as the diary entries and call reports about client contacts in your customer set. An example of applied Natural Language Processing is to perform sentiment analysis against social media posts, or on the notes kept in your CRM systems. This will help you understand how a customer feels about a particular brand or product before you visit or call them.
Finally, Predictive Analytics will forecast unknown future events based on patterns that are identified in your data. Predictive Analytics will surface your next best offer or action based on the client insights that your AI platform has enabled. This is powerful stuff!
With this in mind, we’ll look at how banks are using AI to drive digital transformation and grow revenue by optimizing the customer journey across channels. Stay tuned for the next post: “AI & Your Competitive Edge in a Changing Marketplace.”
