1. A computer-implemented method of estimating risk in financial metrics of a business case, comprising: categorizing each financial metric into a plurality of risk factors; receiving likelihood of risk associated with each risk factor in a first categorical scale; receiving a severity associated with each risk factor in a second categorical scale; converting said first categorical scale and said second categorical scale into a numerical scale using a mapping; determining, by a processor, a maximal threshold parameter K that represents a maximum number of risk factors that simultaneously impact the business case with at least a threshold probability A; determining, by the processor, combinatorial subsets of the K or less risk factors; determining, by the processor, net severity impact for each of the combinatorial subsets based on the received severity associated with each risk factor in said each of the combinatorial subsets, wherein determining the net severity impact uses an aggregation function comprising Δ ⁡ ( i 1 , i 2 , … ⁢ , i I ) = 1 - ( ∏ k = 1 I ⁢ ( 1 - Δ k ) ) ⁢ ∀ 1 < I ≤ K , wherein K represents said maximum number, (i 1 , i 2 ,. . , i I ) represents one of the combinatorial subsets having cardinality I, and Δ k represents the severity associated with a respective risk factor, i 1 , i 2 ,. . , i 1 in the one of the combinatorial subsets; determining, by the processor, a likelihood of risk for each of the combinatorial subsets based on the received likelihood of risk associated with each risk factor in a respective one of the combinatorial subsets; and converting, by the processor, the net severity impact for each of the combinatorial subsets and the likelihood of risk for the combinatorial subsets into a probability distribution.