We propose a new matching problem for combinatorial optimization in financial markets. The problem studied here has arisen from the financial regulators that collect transaction data across regulated assets classes. Unlike previous matching problems, our focus is to identify any unhedged/unmatched derivative, Contract for Difference (CFD) with its corresponding underlying asset that has been reported to the corresponding component authorities. The underlying asset and CFD transaction contain variables like volume and price. Therefore, we are looking for a combination of underlying asset variables that may hedge/match the equivalent CFD variables. Our aim is to identify unhedged /unmatched CFD?s. This problem closely relates to the goal programming problem with variable parameters. In this paper, we construct and implement a variant of BasicVariable Neighborhood Search (BVNS) with our newly constructed local search techniques that performs efficient neighbourhood search to solve these types of problems. Computational results show that the proposed approach achieve good solutions.