According to McKinsey, there is “a second wave of automation and AI emerging in the next few years, in which machines will do up to 10 to 25 percent of work across bank functions, increasing capacity and freeing employees to focus on higher-value tasks and projects.” Some might think that 25% is quite a high number, but the banking industry in particular is finding lots of success with automation. There are a wide variety of solutions that not only automate particular business processes, but also can incorporate and apply industry rules and regulations as part of the analysis.
Here’s a brief description of some popular banking automation solutions:
- 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.
- Natural Language Processing (NLP): NLP uses language to help facilitate communication between the end user and the computer. NLP is made up of Natural Language Understanding (NLU) and Natural Language Generation (NLG). NLU takes human text and converts it into data, a language the computer can understand. A common example would be Siri or Google’s assistant. Similarly, NLG takes information from the computer (data) and turns it into written text. Yseop Compose is an example of NLG. Confused by all the language acronyms? Download this infographic.
- Smart Chatbots: By augmenting current chatbot technology with AI, chatbots can take on more complex questions with accuracy. If done correctly, customers can get answers to basic questions 24/7. This also helps free up time for the call center and sales desk to focus on the more detailed and unique client requests.
By automating some of these areas in banking, companies can start to capitalize on the benefits of these technologies while increasing productivity, customer satisfaction, and company margins.
See the chart below from McKinsey for more examples of automation in banking or read more about other finance use cases and a more in-depth account for the history of automation finance:
Here’s some other interesting reads from last week:
Artificial Intelligence Puts The “AI” In Personalization
Adobe takes to Twitter to find answers to the question (and others): What makes a marketing experience “personal”? (Source: Adobe Blog)
More Evidence That Humans and Machines Are Better When They Team Up
How is AI helping augment the workforce? MIT’s director of Computer Science and Artificial Intelligence Lab (CSAIL), Daniela Rus, provides some unique use cases in law, manufacturing, and distribution. (Source: MIT Tech Review)