Risk-Averse Simheuristic Optimisation Under Explanation-Stability Constraints: An Application to Firm-Level Credit Allocation

Credit allocation is usually left to a threshold rule applied to machine-learning estimates of default risk, and the stability of the explanations that justify it is rarely measured and never required. We formulate credit allocation as a stochastic combinatorial problem that minimises the conditional value-at-risk of portfolio loss subject to a return floor, a minimum ticket, an origination cost and a cap on the exposure-weighted instability of the Shapley attributions of the funded book. We solve it with a two-level simheuristic: a variable neighbourhood search over which applicants to fund, started from a rounded linear relaxation, and a simulation-driven cutting-plane method that optimises the amounts to a certified tolerance. On Polish bankruptcy data, with a Taiwanese cardholder dataset as control, the tail criterion gives up 9.2 per cent of expected profit out of sample and multiplies the profit retained in the worst 5 per cent of scenarios by 4.6. Stable explanations are costly on the primary data and free on the control. The exact solver proves optimality up to 75 applicants; at 100, where it proves none within an hour, the simheuristic matches its best incumbent in 45 seconds, within a certified 2.6 per cent.