The　financial　liberalization　and　globalization　have　increased　the　need　for　expert　and　intelligent　systems　to　deal　with　credit　risk　management　works.　Clustering　algorithms　are　widely　used　for　knowledge　acquisition　in　such　systems　to　assess　the　credit　risk　from　a　data　point　of　view.　Conventional　clustering　algorithms　fall　short　in　credit　risk　assessment　because　credit　datasets　are　typically　high-dimensional,　class-imbalanced,　and　with　large　sample　size.　To　address　these　problems,　this　paper　proposes　a　novel　evolutionary　multi-objective　soft　subspace　clustering　(EMOSSC)　algorithm　for　credit　risk　assessment.　Firstly,　we　develop　a　soft　subspace　clustering　validity　index　for　credit　risk　assessment,　by　which　we　can　detect　the　underlying　subspace　for　each　cluster　from　the　entire　high-dimensional　feature　space,　and　we　also　incorporate　the　weight　of　each　cluster　and　the　between-cluster　separation　into　the　clustering　validity　index　to　obtain　a　comprehensive　data　structure　in　the　clustering　process.　Secondly,　we　propose　to　optimize　the　clustering　criteria　of　the　new　clustering　validity　index　simultaneously　by　a　multi-objective　evolutionary　algorithm　without　any　predefined　weighting　coefficients,　which　guarantees　the　robustness　of　the　algorithm.　We　also　further　provide　a　local　search　strategy　which　significantly　accelerates　the　convergence　of　the　algorithm.　Thirdly,　we　design　a　GPU-based　parallel　computation　framework　for　updating　the　weights　of　features　in　our　proposed　algorithm　to　improve　the　computational　efficiency　on　credit　datasets　with　large　sample　size.　Finally,　we　conduct　a　comprehensive　experiment,　with　its　results　demonstrate　the　superiority　of　our　proposed　algorithm　in　credit　risk　assessment.　(C)　2019　Elsevier　Ltd.　All　rights　reserved.