Our trademark signature product is the BACG insurance risk tensor or BIRTTM. BIRT consists of a multi-dimensional matrix (tensor) where each row consists of an insurance coverage type or category, each column represents an insurance coverage amount (and/or range), each third-dimensional pivot represents the client’s risk potential within this coverage type of insurance coverage, and each fourth-dimensional pivot is the client’s profile requirement within that triple of (coverage type, coverage amount, coverage risk). This 4-dimensional tensor can be visualized as a tesseract or a 4-D cube (hypercube). Subregions within this tensor will optimize insurance coverage, risk minimization, and client profile satisficing. The client will then be able to decide on the appropriate subregions that will contain their insurance acquisition.
Tesseracts and hypercubes, in general, are difficult to visualize in lower dimensional presentations because only their projections onto these surfaces can be seen. For example, tesseract can be projected upon a sold cube which can then be further projected on a 2-D presentation screen. For analytical purposes, a tensor is easier to deal with. When presenting a subregion of a tesseract that presents our insurance portfolio space, we simply unravel the 4-D tuples that show what combinations of coverages work best for the client. These subregions are subgraph-subnetworks that look like something called a rhombic dodecahedron projected as a graph-network onto a 2-D surface. Each node or point in these subgraphs represents an entry from the insurance portfolio that is a unique combination of coverage, risk, satisficing, and offering insurers.
Let us assume that T represents the insurance portfolio tensor (tesseract) that is applicable to the client (represented as a tesseract-graph-network). There exists a subregion in T, say R, where the group of points in R cover all your risk requirements optimally within T. That subregion, R, will then represent your optimized BIRTTM insurance portfolio. The coverages (entry points) as a group within that subregion will optimize your risk aversion and minimize your risk exposure while satisfying your preference profile.
In other words, R represents the client’s optimized insurance portfolio in the sense of risk minimization and profile satisficing. As time progresses, the client’s satisficing profile will change dynamically (even in real-time). So too must its optimized insurance portfolio R. In fact, Z (the potential space of coverages, risks, and satisficing) will also change with time. It is, therefore, optimal when R (from Z) changes with the client’s real-time profile dynamics, up to within a workable (practical) interval of time, tINT, to minimize further risk exposure to the client during that interval of time. To that effect, we then represent this time dynamic optimal portfolio by RtINT(t).
For most insurance services, tINT is usually a week, given that any financial compensation and the insurance policies are registered timely. However, what if tINT could be reduced to a few nanoseconds ala stock market computerized high-speed transactional trading? If the client’s satisficing profile is updated at these speeds, then the computation of RtINT(t) could be undertaken and implemented at those speed boundaries. This is where blockchained technologies within a blockchained entity can execute this scenario. The blockchain would consist of high-speed synchronized transactions starting with the client satisficing profile updates, the calculation of the optimized insurance portfolio, RtINT(t), the high-speed payment (or credit) of the updated premium balance, and finally, the high-speed registration of the insurance portfolio policies.