Eric Sibony is Co-Founder and Chief Product and Science Officer of Shift Technology, a provider of decision automation and optimization solutions for the global insurance industry. Since the establishment of the company, Eric has supervised the design of the solution and its evolution, as well as the R&D on the algorithms that it uses. He holds a PhD in machine learning.
1. Could you tell us in concrete terms how Shift Technology's AI solutions help detect fraud in the insurance industry? Can you give us some examples?
Since the creation of Shift, our first objective has been to offer insurers a SaaS solution for fraud detection based on artificial intelligence. Fraud is a major problem for the insurance industry, representing more than two and a half billion euros in losses per year in France alone, not including health insurance and personal protection. A burden that is reflected in the premiums paid by policyholders.
Once this goal was achieved, we turned our attention to the issue of automating the policyholder journey, and gradually developed Shift Insurance Decisioning Platform, a software platform that uses AI to help insurers make better decisions throughout the underwriting and claims processes.
The core business of Shift's solutions is to apply Artificial Intelligence to better serve the insurance industry, in particular by helping them make the best decisions. AI can be used to identify anomalies, suspicious behavior, fraud patterns, etc. in all critical processes related to the various stages of an insurance policy. Properly applied, it makes sense of large volumes of data, and above all, transforms them into actionable information.
In concrete terms, our Claims Fraud Detection solution detects suspicious claims in real time, and provides clear information to managers to help them make decisions quickly and efficiently. The AI we use cross-references claims and policy data with external data and analyzes it in a secure environment. A suspicion score is then assigned to each claim, and we have indicators in place that explain why the claim is suspicious, as well as investigative leads. This allows the anti-fraud teams to focus on the really suspicious cases, and helps them to process them until the fraud is proven (request for additional information, verification of file documents, on-site investigation, etc.)
Here are a few examples of large-scale or networked frauds that our solutions have recently detected:
2. Does Chat GPT, the new technology developed by OpenAI, pose any challenges for insurers and are you working on safeguards at Shift Technology?
This new technology, in its current form, does not really impact insurers. Chat GPT is fed by information that is essentially available online, and therefore 'public', whereas most of the data processed by insurers is private data. Therefore, there is no real interference between the two. Moreover, when an insured declares a claim, it is a priori in his interest to tell things as they really happened; as for the fraudster, he will try to lie in a very precise way. In both cases, the use of Chat GPT does not seem to be a particularly interesting solution.
If this changes in the future, we will probably adapt our solutions to cover all eventualities. Our goal at Shift Technology has always been to achieve the highest level of security and compliance.
3. From your point of view, what will be the next big revolution in NLP?
Considering the latest innovations, including DALL-E and Chat GPT, I would say that the next step could be the use of NPL to generate reports of all kinds. From there, one could imagine this technology being deployed on a predefined scope, with a precise data system, like that of a company. The work of all the actors producing this type of reports in our current economy would be greatly facilitated. Perhaps to the point of leading to a major revolution in certain professions.
Another possible extension would be the enumeration of all the sources used by the NPL - even if this seems statistically complicated. To reach this point, we would have to be able to trust the AI absolutely, but we are not there yet.