Automation is transforming the finance industry and it’s easy to see why. With growing databases and customers demanding personalized service, automation helps companies meet the current market demands. To help meet these demands, companies are adopting expert systems. The goal of an expert system is to help automate repetitive, time-consuming aspects of a job, freeing up the employee’s time to focus on more creative, non-routine tasks. To give you an idea of what is popular tech for automation in finance, here are 3 examples of expert systems that are helping companies automate to scale.
1. Virtual Customer Assistant (VCA) and Virtual Personal Assistant (VPA)
Different types of tech, VPA’s and VCA’s, help provide support to both employees and customers, respectively, to leverage your current knowledge base. VPA’s use a company’s best practices to guide call center employees and give the best possible compliant expert advice. Whereas VPA’s are guides for your existing employees, VCA’s interact directly with the customer. By leveraging your company’s current knowledge base, you can start to provide a more personalized customer experience for potential and existing customers.
2. Robotic Process Automation (RPA)
RPA is a set of tools that automate repetitive and routine-based tasks. It’s considered popular tech for automation in finance because it automates business processes involving a high degree of knowledge and expertise. Through automation, bankers can leverage the thousands of data points stored at the company (in the CRM or elsewhere) and generate advice, questions, or recommendations that are fully compliant following relevant regulation, risk profiles, et cetera. This ensures that the customer is paired with the product that serves him or her best, and not just which one the banker remembers.
3. Natural Language Generation (NLG)
The next generation of enterprise NLG unifies two divergent aspects of AI tech: automation of repetitive tasks and language generation, taking the company’s best practices and applying them at exceptional speeds. The marriage between an inference engine and NLG technology brings about a new age in NLG, what is called Next-Generation NLG. For the first time in history, machines can summarize data, explain insights, and articulate what these insights mean — in plain language — so that anyone can understand. This new software can go even deeper, explaining the drivers behind a dataset as well as next steps and additional data to collect. These types of tech are providing a simple way to help leverage your current knowledge base all while automating repetitive, time-consuming tasks to scale.