This exploration is to design an optimal sliding mode controller for the chaotic finance system. In this controller design, back stepping and sliding mode control techniques are combined together to get the chaotic finance system globally, asymptotically stabilized at the equilibrium point. Furthermore, sliding surface parameters are optimized using a hybrid Genetic Particle Swarm Optimization (GPSO) to improve the reaching phase characteristics of the sliding mode controller. Numerical simulation results demonstrate the effectiveness of the proposed scheme in successfully tuning the parameters of the sliding mode controller. The comparative study with other techniques shows the efficacy of the hybrid Genetic Particle Swarm tuned sliding mode controller in improving the reaching phase characteristics and settling time required for the chaotic finance system to reach a stable equilibrium point.