AI Articles and Analysis about Business intelligence and analytics

Automated loan processing and underwriting is not a new concept in the banking and financial services industry. Lenders have consistently faced pressure to reduce the costs and time associated with internal loans processing and turnaround.  

Insurers are looking to leverage all of the digital customer data that is now available to them, including one new data source that some of the largest insurance enterprises claim are actively collecting: real-time data streams from the Internet of Things (IoT).

McKesson is a large pharmaceutical distributor that delivers health information technology, medical tools and supplies, and care management tools and software to healthcare providers in the United States. The company has been working to incorporate artificial intelligence solutions internally since 2012, but in more recent years they have adopted a few important automation tools. 

Ten years into the longest economic expansion on record, auto lenders are looking for ways to leverage new opportunities for growth and risk reduction.

Our research indicates that AI applications for risk-related banking functions are more numerous than applications for other business areas. Fraud and Cybersecurity, Compliance, Loans and Lending, and Risk Management collectively made up 56% of the AI vendor products in the banking industry, as shown in the graph below:

Digitally-native eCommerce businesses are used to working with their customer data in order to write copy for marketing campaigns, run PPC ads, calculate customer lifetime value, and make decisions based on core metrics within CRM dashboards.

AI and machine learning have had successful applications in the financial sector even before the entry of the mobile banking ecosystem. AI is being used to leverage insights from data for financial investing and trading, wealth management, asset management, and risk management.

Artificial intelligence is changing the way healthcare networks do business and physicians perform their routine activities from medical transcription to robot-assisted surgery. Although the more mature use-cases for AI in healthcare are those built on algorithms that have applications in various other industries (namely white-collar automation), we believe that in the coming three to five years, AI solutions for healthcare will become increasingly specialized to individual use-cases.