Byte-ing the Bullet with AI in Banking

From the recent Hollywood film Ex Machina to Facebook’s use of machine learning algorithms to map peoples’ homes, artificial intelligence (AI) is coming of age. AI is the science of developing computer systems to perform tasks that normally require human intelligence [2]. Today, AI involves machine learning algorithms, where data points are input into a program and the computer then uses that data to create classifications and identify trends. This form of supervised learning is often a substitute for manual work; with its creative problem solving , data analytics, and storage capabilities, AI is easily able to surpass the human brain to foster superior business results.

Today, it is very common for companies to develop algorithms that track a user’s online habits, creating personal online interactions. Google, a leader in the AI industry, displays search results based upon its relevancy algorithm. [3] While machine learning has been used in other industries, it was not until recently that the banking industry has decided to employ this technology. Just two years ago, USAA began using IBM’s Watson technology to assist military members in the financial planning process. [1] With this big push, banks are increasingly using AI to sort through big data to efficiently provide the correct services for their customers. According to a study by Cognizant [4], 26% of banking respondents stated they have enjoyed 15%-plus cost savings from automation in their front office and customer-facing functions compared with one year ago, and 55% expect those same levels of savings (15% or more savings) within the next three to five years. According to Cognizant, nearly half of the banks surveyed (45%) have also seen at least 10% revenue growth from analytics aligned with their front office and customer-facing functions, a number that is anticipated to rise to nearly three out of every four banks during the next three to five years. Despite these staggering statistics, the banking industry has been slow to adopt this powerful technology. [5]

Here at Micronotes, we’ve built an AI-based engagement utility. Catering to the need for customer personalization in the financial industry, we combine the classic method of asking what customers want with the modern technologies of artificial intelligence to follow up on leads and increase customer loyalty. We have the data to back it up too – traditional banner ads would take almost 30 years to reach the same number of customers we engage in six months with our engagement platform. To find out more, read our previous blog here.

The banking industry should not base its marketing strategies around playing catch-up. Take advantage of the powerful technology at hand and join us in making your life easier and your customers happier.