Jim Hulett ARM, FCLS
Uncovering digital media fraud with advanced forensics
Straight-through processing is considered the holy grail of digital claims—where insurers can intake, process, and pay claims automatically
Image forensics: How photo data can reveal claims fraud
Image forensics: How photo data can reveal claims fraud
Is that loss photo legit? Fighting digital image fraud
Photo-based claims estimates have been a growing trend the past few years in the insurance industry, and once the pandemic hit, that trend accelerated significantly.
Artificial intelligence in claims fraud: How AI is automating suspicious behavior detection
Insurers are turning to AI to detect claims fraud automatically – but to get the most from their analytics, they need to understand an anti-fraud system.
Insurance fraud detection in action: Identifying suspicious medical billing during the COVID-19 crisis
Taking a closer look at medical billing helps identify pandemic-related issues that can put patients at risk and negatively impact insurers’ bottom lines.
The critical role fraud scenarios play in advanced analytics
Neural networks, machine learning, multivariate random forest models, and various derivatives of the same are being used to create modern fraud detection models.
Medical-related fraud and theft increase during COVID-19 crisis
Unfortunately, crisis is fertile ground for fraud, whether it be the opportunistic kind or organized criminal activity.
Estimating claims remotely? Uncover potential fraud in digital loss images
Claims adjusters are now working at home, relying on customer-submitted images and videos to inspect and appraise damages for the foreseeable future.
Five data-driven strategies for fighting insurance claims fraud
As we enter 2020, the insurance industry looks much different than it did ten years ago.
How to find fraud in insurance claims
For insurers looking to stem the $30 billion-a-year insurance claims fraud problem, early detection is key.