Type of DLT structured as a chain of blocks containing multiple transactions and securely linked together via cryptography. This technology enables to store permanently updated information in multiple places and with all copies synchronized. The Bitcoin (with a capital B) Blockchain is the most popular, but there are many others from Ethereum to IOTA, each with unique types and functions.
Systems based on a distributed digital ledger, i.e. systems in which all the nodes of a network have the same copy of a database which can be read and modified independently by each node. In these systems, changes to the ledger are regulated through consensus algorithms. These algorithms make it possible to achieve consensus between the various versions of the ledger, even though they are updated independently by the network participants.
Policies that provide for the use of specific IoT objects and sensors available in the physical world and/or digital twins of the virtual representation of the object when uniquely identified with NTFs (Non-Fungible Token) and DIDs (Distributed Digital Identity) that can be used with secure and protected access protocols according to the new paradigms of Web 3.0 and/or the Productive Metaverse
This includes, but is not limited to, a policy for a scooter or an electric bicycle that also has its own representation on the blockchain and a non-removable IoT device that identifies the vehicle in a certain and unambiguous way and makes it possible to locate it.
At third stage, the Start-up focuses on the launch on the market to acquire the first customers (a process called “market traction”), promote its image (brand awareness) and get the first revenues. It is about investment rounds that are necessary to finance product development, marketing, and staff recruitment activities.
“Integrated” policies sold in combination with other products or services, or, in the specific case of technological innovation, included in IoT devices or SW systems linked to physically identifiable devices and which enable to take out policies in a transparent and automatic manner upon sale of the identifiable asset.
A network of connected objects (simple domestic objects or sophisticated industrial devices) provided with unique identifiers and the ability to communicate with each other and towards nodal points of a system, but above all with the ability to create a network of things where each of them can be traced by name and possibly by reference to the position. At insurance level, it is important to combine IoT with methods or tools (RF tags, QR codes, NFTs or DIDs) that allow the object to be uniquely identified, thus making it insurable.
The third part that enables to objectively determine and ascertain an event. For example, in parametric insurance policies it can be used to ascertain and determine the settlement of a claim, thanks to its objective and automatic recording of the event. In these cases, when signing the insurance contract, the parties agree that through the reports of the oracle the accident will be deemed to have occurred, without the need for a complaint.
Concept based on indices, or calculated average values, relating to the occurrence of events. Parametric insurance policies therefore provide for the payment of a predefined indemnity upon the occurrence of a harmful event. In fact, the amount of the indemnity is set in the insurance contract when the contract is signed and is set regardless of the value of the actual damage that may occur.
Policies in which the premium (the cost of the policy) is established by the detection or measurement carried out by third parties (so-called oracles) and is therefore variable; or, as a result of virtuous conduct, they give rise to forms of return of the amounts spent by the policyholder in the policy activation (“money back”).
This includes, but is not limited to, a new health policy that would allow the policyholder to take advantage of a form of money back in case of virtuous behaviours such as carrying out wellness activities, which can be monitored through health platforms.
Policies that provide for risk-taking on a selection of specific events and are issued with digital contracts in the form of algorithms that automatically calculate the probability of occurrence, both with simple algorithms such as IFTTT (“if this then that”) and advanced algorithms using Machine Learning. They provide for certain compensation when certain circumstances occur and do not require evaluations or expert reports.
This includes, but is not limited to, third-party liability policy for the house, where a reduction is applied to the policy premium by detecting authorized persons in the house (through smart speaker systems). The presence in the house is essential for the prevention of third-party liability, theft and fire risks.
A branch of advanced analytics used to make predictions about future events, behaviours, and outcomes. It uses data, statistical algorithms, and machine learning techniques to figure out the likelihood of future outcomes based on historical data. The goal is to go beyond the understanding of what happened to get to a better assessment of what will happen in the future.
A Red Carpet is for Vhub an innovative Startup that deals with Insurtech and is already on the market with revenues, available to evaluate a PoC (Proof of Concept) to be integrated into Vittoria Assicurazioni/Vhub ecosystems.
The Red Carpet startups with the highest degree of maturity will be subject to an ad-hoc selection by Vittoria hub team of experts. In these cases, there may be two possible outcomes:
At this stage, the Startup enters the development of the idea through the elaboration of its own BM (Business Model) and BP (Business Plan) and the development of a demonstrative MVP (Minimum Viable Product). At this stage, the first lenders are involved: incubators/accelerators, business angels, crowdfunding companies, lenders.
Startups with a scalable business model and which at a technological level demonstrate a highly innovative direction towards Foundation Models for the Insurtech sector will be given priority. Startups that use (for example and not exclusively) Large Language Models (LLM) and Large Vision Models (LVM), also in combination with each other, for the generation of intelligent chatbots, for synthetic datasets and for innovative forms of profiling support, for the personalization and automation of the offer of insurance products.
