A processor-implemented method for generating a multi-dimensional risk profiling data structure includes identifying one or more dimensions common to a plurality of risks along which all of the plurality of risks may be aggregated, assigning the one or more common dimensions to a top level cube structure, identifying a set of dimensions and risk drivers specific to each of the plurality of risks, and assigning each set of dimensions and risk drivers to each of a plurality of second level cubes.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A processor-implemented method for generating a risk profiling data structure, comprising: identifying one or more parent dimensions along which a plurality of risks being profiled is aggregated; assigning the one or more common dimensions to a top level cube structure; identifying a set of dimensions and risk drivers specific to each of the plurality of risks; and assigning each set of the child dimensions and risk drivers to each of a plurality of second level cubes. 2. The processor-implemented method of claim 1, further comprising recursively identifying and assigning an Nth set of dimensions and risk drivers to each of Nth level cubes. 3. The processor-implemented method of claim 1, wherein the risk profiling data structure is generated using a neural network, by: creating a neural network computational model for data collected for risk profiling; training the neural network computational model using the data collected for risk profiling; and validating the neural network computation model for generating a risk profiling data structure. 4. The processor-implemented method of claim 1, wherein the dimension is at least one of geography, and organization. 5. A system, comprising: a tangible, non-transitory, computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform a method comprising: identifying one or more dimensions along which a plurality of risks being profiled is aggregated; assigning the one or more common dimensions to a top level cube structure; identifying a set of dimensions and risk drivers specific to each of the plurality of risks; and assigning each set of dimensions and risk drivers to each of a plurality of second level cubes. 6. The system, of claim 5, further comprising recursively identifying and assigning an Nth set of dimensions and risk drivers to each of Nth level cubes. 7. The system of claim 5, wherein the risk profiling data structure is generated using a neural network, by: creating a neural network computational model for data collected for risk profiling; training the neural network computational model using the data collected for risk profiling; and validating the neural network computation model for generating a risk profiling data structure. 8. The system of claim 5, wherein the dimension is at least one of geography, and organization.