Data-driven insurance claims management given AI boost
Damco Solutions has partnered with AI as-a-service (AIaas) omni:us to help insurance companies with claims management.
The move is intended to help insurance companies with accelerating their digital transformation.
It is hoped the partnership between Damco’s two decades of insurance IT experience and omni:us’s AI expertise will allow insurers to deploy digitalisation services across their portfolios.
Omnius brings to the table four core strengths:
- Digital FNOL: Real-time claims management that converts information into actionable claims data by minimizing touchpoints with intelligent claim automation
- Claims Indexation: AI cognitive process that streamlines document extraction and classification to read, interpret, and understand data by eliminating the manual workflow
- Completeness Check: AI-powered claims handling process with AI-powered decisions and recommendations, providing insights for regulatory compliance and reducing manual claims touchpoints
- Coverage Check: Intelligent claims automation improving productivity in the claims process by eliminating time-intensive tasks, identifying noncovered claims, detecting fraud, claims leakage, etc.
Mohit Gupta, founder and CEO of Damco Solutions, said, "We look forward to collaborating with omni:us to help insurance companies with AI-based cognitive claims management solutions and digital technologies to simplify claims lifecycle.
“Together, we would be able to help a broader range of insurers industrialise innovation in claims management. With the addition of omni:us, we see tremendous opportunity to enlarge our approach and help more businesses supercharge their future readiness."
Sofie Quidenus-Wahlforss, founder and CEO of omni:us, said, "omni:us and Damco share a common aspiration: to drive insurance businesses towards a more efficient and customer-oriented experience.
“We at omni:us are excited to team up with such a veteran player in the field as Damco. Both sides will benefit from the other's strengths and specialisations and be further empowered to realize our ultimate goal of bringing about wide digital transformation to the insurance sector.”
Facebook Develops AI to Crackdown on Deepfakes
In light of the large tidal wave of increasingly believable deepfake images and videos that have been hitting the feeds of every major social media and news outlet in recent years, global organisations have started to consider the risk factor behind them. While the majority of deepfakes are created purely for amusement, their increasing sophistication is leading to a very simple question: What happens when a deepfake is produced not for amusement, but for malicious intent on a grander scale?
Yesterday, Facebook revealed that it was also concerned by that very question and that it had decided to take a stand against deepfakes. In partnership with Michigan State University, the social media giant presented “a research method of detecting and attributing deepfakes that relies on reverse engineering from a single AI-generated image to the generative model used to produce it.”
The promise is that Facebook’s method will facilitate deepfake detection and tracing in real-world settings, where the deepfake image itself is often the only information detectors have to work with.
Why Reverse Engineering?
Right now, researchers identify deepfakes through two primary methods: detection, which distinguishes between real and deepfake images, and image attribution, which identifies whether the image was generated using one of the AI’s training models. But generative photo techniques have advanced in scale and sophistication over the past few years, and the old strategies are no longer sufficient.
First, there are only so many images presented in AI training. If the deepfake was generated by an unknown, alternative model, even artificial intelligence won’t be able to spot it—at least, until now. Reverse engineering, common practice in machine learning (ML), can uncover unique patterns left by the generating model, regardless of whether it was included in the AI’s training set. This helps discover coordinated deepfake attacks or other instances in which multiple deepfakes come from the same source.
How It Works
Before we could use deep learning to generate images, criminals and other ill-intentioned actors had a limited amount of options. Cameras only had so many tools at their disposal, and most researchers could easily identify certain makes and models. But deep learning has ushered in an age of endless options, and as a result, it’s grown increasingly difficult to identify deepfakes.
To counteract this, Facebook ran deepfakes through a fingerprint estimation network (FEN) to estimate some of their details. Fingerprints are essentially patterns left on an image due to manufacturing imperfections, and they help identify where the image came from. By evaluating the fingerprint magnitude, repetition frequency, and symmetrical frequency, Facebook then applied those constraints to predict the model’s hyperparameters.
What are hyperparameters? If you imagine a generative model as a car, hyperparameters are similar to the engine components: certain properties that distinguish your fancy automobile from others on the market. ‘Our reverse engineering technique is somewhat like recognising [the engine] components of a car based on how it sounds’, Facebook explained, ‘even if this is a new car we’ve never heard of before’.
What Did They Find?
‘On standard benchmarks, we get state-of-the-art results’, said Facebook research lead Tal Hassner. Facebook added that the fingerprint estimation network (FEN) method can be used for not only model parsing, but detection and image attribution. While this research is the first of its kind, making it difficult to assess the results, the future looks promising.
Facebook’s AI will introduce model parsing for real-world applications, increasing our understanding of deepfake detection. As cybersecurity attacks proliferate, and generative AI falls into the hands of those who would do us harm, this method could help the ‘good guys’ stay one step ahead. As Hassner explained: ‘This is a cat-and-mouse game, and it continues to be a cat-and-mouse game’.