With　the　rapid　development　of　the　Internet,　a　variety　of　traditional　industries　use　information　technology　and　Internet　platform　closely　derived　from　a　variety　of　emerging　industries.　In　the　financial　field,　traditional　banks　cannot　meet　the　needs　of　small　and　medium-sized　enterprises　with　rapid　growth.　Thanks　to　the　mature　development　of　Internet　platforms,　various　P2P(Peer　to　Peer　Lending)　platforms　have　emerged　as　the　times　require.　They　provide　a　simple　reception　approval　process　and　provide　investors　and　borrowers　with　convenient　lending　channels.　However,　with　the　rapid　development　of　the　P2P　industry,　P2P　has　frequent　security　problems.　The　main　reason　for　this　phenomenon　is　that　the　personal　credit　evaluation　of　the　borrower　is　inaccurate,　which　leads　to　a　chain　reaction.　Therefore,　the　effective　estimation　of　the　credit　data　of　the　borrower　is　the　key　to　solving　the　credit　risk.　With　the　rapid　development　of　machine　learning,　various　risk　prevention　and　control　models　are　more　and　more　accurate　in　predicting　personal　credit.　However,　default　data　accounts　for　only　a　small　part　of　the　massive　credit　data,　so　it　is　particularly　important　to　process　the　data　set,　especially　to　balance　the　sample　data.　Traditionally,　SMOTE(Synthetic　Minority　Oversampling　Technique)　algorithm　is　generally　used　for　oversampling　of　data.　However,　when　there　are　noise　samples　in　the　auxiliary　samples　of　the　samples　synthesized　by　this　algorithm,　the　new　samples　may　become　the　noise　sample　interference　model.　In　this　paper,　the　Borderline-SMOTE　algorithm　is　used　to　oversample　the　data　features.　The　results　show　that　the　model　trained　by　the　processed　data　set　is　better　than　the　control　model,　both　in　accuracy　and　recall　rate.　©　2022　IEEE.