Decision-makers in the banking sector have a unique set of business intelligence needs, and artificial intelligence has been on the radar of banking executives for several years now. It follows that AI and machine learning would find their way into business intelligence applications for the banking sector.
In this report, we discuss AI-driven business intelligence software for the banking sector and their evidence of success. As it turns out, there isn’t much. The companies covered in this report all lack banking case studies for their business intelligence software, which may be indicative of the nascency of AI for this particular application in banking. That said, these companies do offer case studies for other sectors.
- Report Generation – creating reports with a variety of visualizations that suit the needs of employees at different departments
- Predictive Analytics – correlating enterprise data to find patterns on which executives can take action
In this report, we’ll discuss the uses cases of AI-driven business intelligence applications in the banking sector by taking a look at four vendors that offer AI software to banks. We’ll begin by examining the vendors offering report generation-related AI solutions:
SAS offers software called SAS Visual Analytics, which it claims can help banks provide their lead staff with self-service analytics and interactive reports using what appears to be a combination of predictive analytics and natural language processing. For banks, this analysis and reporting may be related to customer buying patterns, loan payments, or customer experience.
This could be for strategies on gaining customers or to finding customers less likely to default on loans. SAS claims that in addition to the data analytics capabilities of its solution, it can also discern the sentiment behind text data, such as social media posts, and mark it as positive or negative. This is likely accomplished through natural language processing.
Below is an 8-minute video demonstrating how SAS Visual Analytics can segment customers. In this video, the demonstrator is able to segment the section of his customer base by 4:40:
We can infer the machine learning model behind the predictive analytics portion of the software needs to be trained on the client’s data related to banking transactions, customer profiles, and geolocation. For example, if a customer made an ATM withdrawal, the relevant data from that transaction would be the customer’s age, what gender they are, and where the ATM is relative to their bank. Customer segments could become more specific than this, however. The data would then be run through the software’s machine learning algorithm.