Automating Report Writing: How Financial Services is Embracing AI
When you think of Artificial Intelligence you no doubt conjure up ideas of self-driving cars, robots, and science fiction. With this in mind, AI and finance may seem strange bedfellows, but the truth about AI is less Hollywood and more Wall Street. Today, Artificial Intelligence is employed across the financial services and banking sector in use cases ranging from customer service to performance reporting, compliance reporting, and more. While the majority of these use cases are based on automating report writing, there has been a notable growth in use cases focusing on customer experience.
Automating Report Writing: Natural Language Generation
I am often asked what is the most common way that Natural Language Generation (NLG) technology is used. NLG is software that automatically summarizes data, explains analysis, and writes the data-driven sections of reports. Whether it is automating the writing of fund reports, P&L Report Explanations, Environment Social & Governance Reports (ESG), or management reports, good NLG use cases are reports that are costly to write, in terms of time and money, and they are written often (and are reports you wish you could write even more often).
Based on our experience working with some of the largest banks and financial institutions in the world, we put together a white paper to help in the process of choosing your first use case. Ultimately, the use case and business problem must always be the driving factor in choosing the right software for your needs. For example, do you need content in languages other than English? Do you need the flexibility of self-service and does security mean you need on-premise?
Automating for Better Customer Experience
One of the fastest growing use cases for Natural Language Generation technology is in the customer experience space. Smart Chatbots that are able to offer advice and guidance are a growing use case. But the most common use case today is as a CRM plug in. Essentially, NLG can write a memo before a salesperson takes a meeting. The software explains who you are meeting, the history with the client, what you need to know, what you need to sell, and with what sales pitch: all of this automatically. Some clients take this to the next step, using a software like Alexa to “read” the report written by NLG.
Just picture it, you are on a way to a meeting and you ask “Alexa what do I need to know” and the NLG software explains everything just like a personal assistant but using all the data you have on the customer. This is not science fiction, it’s the reality in many of the largest banks and insurance companies today.
Many customers begin by automating report writing before moving on to customer experience projects. Regardless, the key with Natural Language Generation (and really any software) is do your homework on the company, market, and on your use case.
Learn how to choose your first NLG use case in this free white paper and:
- Find your best, first use case in 3 simple steps
- See current examples of popular use cases
- Learn how clients are getting the highest ROI with their NLG investment