This paper proposes an artificial immune network approach combined with feature selection techniques for bank term deposit recommendation. The method utilizes immune system principles to identify optimal feature subsets and improve prediction accuracy in banking marketing campaigns. The proposed algorithm demonstrates superior performance compared to traditional machine learning methods in terms of both accuracy and computational efficiency. Experimental results on real banking datasets show significant improvements in customer targeting and conversion rates.
