It　is　crucial　to　predict　the　credit　risk　of　small　and　medium-sized　enterprises　(SMEs)　accurately　for　the　success　of　supply　chain　finance　(SCF).　However,　most　of　the　existing　research　ignore　the　fact　that　the　data　distribution　is　usually　imbalanced,　that　is,　the　proportion　of　default　SMEs　is　much　smaller　than　that　of　non-default　SMEs.　To　fill　this　research　gap,　we　propose　a　novel　approach　called　DRL-Risk　to　deal　with　the　imbalanced　credit　risk　prediction　(ICRP)　of　SMEs　in　SCF　with　deep　reinforcement　learning　(DRL).　Specifically,　we　formulate　the　ICRP　problem　as　a　Markov　decision　process　and　suggest　an　instance-based　reward　function　to　incorporate　financial　loss　into　the　reward　function　with　consideration　of　the　actual　loss　caused　by　misclassification　in　the　ICRP　of　SMEs.　Then,　we　recommend　a　deep　dueling　neural　network　for　decision　policy　to　predict　the　credit　risk　of　SMEs.　With　deep　reinforcement　learning,　the　DRL-Risk　approach　can　prioritize　the　learning　on　the　SMEs　that　would　lead　to　great　financial　losses.　Experimental　results　demonstrate　that　the　DRL-Risk　approach　can　significantly　improve　the　performance　of　credit　risk　prediction　of　SMEs　in　SCF　compared　with　the　baseline　methods　in　recall,　G-mean,　and　financial　loss.　We　have　also　identified　management　implications　for　the　decision-makers　participating　in　SCF.