Many large banks and financial institutions are beginning to digitize parts of their business processes to prepare for future initiatives in automation and machine learning. This is particularly true with loan processing. These functions could become faster and more accurate if they use digitized data that is more easily accessible than paper documents.
Financial institutions have challenges around data accessibility. They want to leverage their large amounts of data so their employees, such as customer service agents, can find the information they need quickly.
In this brief overview, we run through several use-cases for voice recognition software in the healthcare industry. Voice recognition software, built on natural language processing (NLP) algorithms, primarily finds a home in the doctor's office. Physicians use it to dictate their notes into their healthcare network's system or update patient electronic medical records (EMR).
It can be difficult for financial institutions to keep up with the rapid changes in the digital marketing and advertising landscape. There are numerous factors which are susceptible to change, and they all have an effect on how useful certain marketing strategies are. As the internet and advertising evolve, some companies may find it important to consider an automated solution to driving efficiency in marketing.
In this article, I showcase 6 examples of nontechnical professionals who used their business and subject-matter expertise (not their coding ability) to have more exciting careers in AI, and I respond directly a number of questions and comments from Emerj subscribers about AI knowledge for career advancement
Many of the key processes in industries such as banking and insurance are still done on paper. That said, many large enterprises seem to be in the process of digitizing parts of these processes in order to prepare for forays into automation and artificial intelligence.