Artificial intelligence and other technologies hold great promise for the insurance industry but are not without issues, such as adoption and security, according to a panel of insurance and technology industry executives speaking at the Insurance Information Institute's Joint Industry Forum in New York on Thursday. Andrew Robinson, co-CEO for Groundspeed Analytics Inc. in Atlanta, left his life in the insurance industry "to join an early-stage insurtech company because I saw the great potential technology offered. I'm a full believer in the enablement of our industry and the role that technology is now playing, particularly AI and big data." Technology by itself, however, is not an answer to all challenges faced by the insurance industry, according to Sean Ringsted, executive vice president, chief digital officer and chief risk officer for Chubb Ltd. "For me and Chubb, we couldn't be more excited about the possibility of AI and big data and how it can be a force for good," with a couple of caveats, he said. "It's not going to do everything by itself; you've got to bring other technologies and capabilities to bear on that," Mr. Ringsted said, adding "there's always going to be a place for a human touch."

The smart use of technologies such as artificial intelligence is expected to revolutionize the management of baggage over the next decade, promising to make mishandled bags an increasingly rare event for passengers globally. This is according SITA's Intelligent Tracking: A Baggage Management Revolution paper published today. The paper notes that more than 4.5 billion bags1 are handled by industry baggage systems each year but airlines and airports will have to cope with twice that number with passenger numbers set to double over the next 20 years. Already, through improvements to technology and processes, the air transport industry has halved its annual mishandling cost over the past decade from US$4.22bn to US$2.1bn. However, every mishandled bag is one too many and the industry continues to seek ways to reduce the number further.

Radiologists can scrutinize hundreds of images before identifying an area of concern in a patient's body. But a type of artificial intelligence known as deep learning could soon help medical experts pinpoint problems faster and more accurately, says Dr. Michalski, executive director of the Boston-based Center for Clinical Data Science at Massachusetts General Hospital and Brigham and Women's Hospital. Deep learning includes algorithms, or computer programs, that search for, identify and analyze problems without direction from people, though many humans still guide the algorithms today. For months, Dr. Michalski has used hundreds of thousands of medical images to train a deep-learning system to detect pulmonary nodules and strokes, measure tumors and look for traumatic injury and fractures, among other tasks. The early results "are promising," he says. "The technology really does work."