Recently,　Graph　Neural　Networks　(GNNs)　have　been　widely　used　for　fraud　detection.　GNNs　first　generate　node　embedding　by　aggregating　neighboring　information　under　different　relations,　and　then　use　the　final　node　embedding　to　detect　the　node＇s　suspiciousness.　However,　traditional　GNNs　employing　only　a　single　type　of　aggregator　fail　to　capture　neighbor　information　from　multiple　perspectives　and　treating　different　relations　equally　inevitably　weakens　the　semantic　information　of　heterogeneous　graphs.　Meanwhile,　expressive　ability　of　GNNs　is　limited　by　using　conventional　concatenating　or　averaging　operations　to　update　the　center　node.　Also,　camouflaged　entities　could　damage　GNN-based　models.　To　handle　these　problems,　a　novel　heterogeneous　GNN　model　called　Multiple　Aggregators　and　Feature　Interactions　Network　(MAFI)　is　proposed　in　this　paper　to　conduct　fraud　detection　tasks.　Concretely,　multiple　types　of　aggregators　are　applied　on　different　relations　to　aggregate　neighbor　information　and　aggregator-level　attention　is　utilized　to　learn　the　importance　of　different　aggregators.　Also,　relation-level　attention　is　leveraged　to　learn　the　importance　of　each　relation.　Besides,　conventional　update　operations　are　replaced　with　vector-wise　implicit　and　explicit　feature　interactions.　Moreover,　a　trainable　neighbor　sampler　is　employed　to　filter　camouflaged　fraudsters.　Comprehensive　experiments　on　two　real-world　fraud　datasets　indicate　that　the　proposed　MAFI　outperforms　existing　GNN-based　fraud　detectors.