AI Articles and Analysis about Process automation

Several key insurance carriers began to experiment with AI in the last decade, including Progressive, All-State, and State Farm. Although not as large as the banking and retail industries, the AI vendor landscape in insurance is growing.

HSBC Holdings is a multinational banking and financial services holding company and is ranked 99th on the Fortune 500 list. The bank has worked with multiple AI vendors and provided evidence of success that other top banks lack. According to our AI Opportunity Landscape research on how the top global banks are using AI, besides Deutsche Bank, HSBC is the European bank with the most AI initiatives. HSBC's AI initiatives account for 12.5% of the AI initiatives at the European banks in our analysis. 

AI may have a role to play in digitizing the paper-heavy mortgage process, facilitating more streamlined search and discovery for entities across a variety of digital and scanned PDF documents. We spoke with Dan Cortright, Senior Director of Product Management at Iron Mountain, about just that. Courtright discusses how AI could help approve loans quicker, better assess risk, and allow employees to pull up documents they need to respond to customer requests.

Many of the largest US banks, including Bank of America, are starting to automate many of their business processes with AI. These include ACH payments and more specific processes such as order to cash. Bank of America has been investing seriously in AI and machine learning since at least 2017 and continues to research ways they can take advantage of AI in the future.

Much of the discussion surrounding AI in banking is focused on retail banks, with applications such as chatbots and payment fraud detection. But there exist AI capabilities that stand to benefit investment banking clients specifically as wealth management departments begin to adopt them.

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).