Currently　existing　credit　risk　models,　e.g.,　Scoring　Card　and　Extreme　Gradient　Boosting　(XGBoost),　usually　have　requirements　for　the　capacity　of　modeling　samples.　The　small　sample　size　may　result　in　the　adverse　outcomes　for　the　trained　models　which　may　neither　achieve　the　expected　accuracy　nor　distinguish　risks　well.　On　the　other　hand,　data　acquisition　can　be　difficult　and　restricted　due　to　data　protection　regulations.　In　view　of　the　above　dilemma,　this　paper　applies　Generative　Adversarial　Nets　(GAN)　to　the　construction　of　small　and　micro　enterprises　(SMEs)　credit　risk　model,　and　proposes　a　novel　training　method,　namely　G-XGBoost,　based　on　the　XGBoost　model.　A　few　batches　of　real　data　are　selected　to　train　GAN.　When　the　generative　network　reaches　Nash　equilibrium,　the　network　is　used　to　generate　pseudo　data　with　the　same　distribution.　The　pseudo　data　is　then　combined　with　real　data　to　form　an　amplified　sample　set.　The　amplified　sample　set　is　used　to　train　XGBoost　for　credit　risk　prediction.　The　feasibility　and　advantages　of　the　G-XGBoost　model　are　demonstrated　by　comparing　with　the　XGBoost　model.