AI Articles and Analysis in Finance

Banks are starting to deploy natural language processing (NLP) to make use of enterprise and customer data in text mining applications ranging from gauging customer sentiments to enterprise search.

Robotic process automation, or RPA, has dominated much of the automation conversation in the insurance industry for several years. RPA is able to capture manual steps that employees take to log into software, search documents, and enter data and replicate them.

The advent of machine learning in finance ushered in a keen interest in using AI to automate processes from fraud detection to customer service. While some use-cases aren’t nearly as established as others, our research leads us to believe that in the coming five years, banks will continue to invest in machine learning for risk-related processes, including underwriting.

Business process management (BPM) in banking involves the automation of operations management by identifying, modeling, analyzing, and improving business processes. Many banks already have some form of BPM for various process. For example, compliance processes at most banks tend to have some form of software automation in their workflows.

Retailers and financial institutions are adopting artificial intelligence and machine learning in their business to solve various business problems such as cybersecurity and document digitization. However, many of these companies are also using AI to improve their payment processes for their clients and customers. These types of applications are usually layered into an existing payments technology stack, which could include straight-through processing (STP) or robotic process automation (RPA).

Automated loan processing and underwriting is not a new concept in the banking and financial services industry. Lenders have consistently faced pressure to reduce the costs and time associated with internal loans processing and turnaround.  

Since the advent of online banking services, customers have had several different ways of communicating with their banks. Banks need to monitor all of these incoming customer requests and respond to them in the most efficient way possible. Further, each of the various channels of communication represents a valuable way to segment customers to not only improve how they perceive a bank’s brand but also to market banking products to them better.

Insurers are looking to leverage all of the digital customer data that is now available to them, including one new data source that some of the largest insurance enterprises claim are actively collecting: real-time data streams from the Internet of Things (IoT).