Accounting　procedures　have　historically　been　the　fundamental　framework　for　managing　data　environments.　In　this　study,　we　aim　to　enhance　the　Accounting　Information　System　(AIS)　efficiency　through　optimization　techniques.　Our　primary　objective　is　to　propose　and　evaluate　hybrid　optimization　methods　to　improve　the　performance　of　AIS.　Furthermore,　the　Hybrid　Capsule　Network　with　XGBoost　(CNGB)　model　and　the　Hybrid　Honey　Badger　Particle　Swarm　Optimization　(HBPSO)　method　are　used　for　search　optimization　while　utilizing　the　capabilities　of　the　PSO　optimization　algorithm.　The　data　pre-processing　is　performed　and　extensive　data　analysis　is　conducted　for　auditing　the　risk　assessment　with　predictive　models.　Binary　classification　is　performed　in　our　proposed　model　while　utilizing　the　properties　of　the　capsule　network　and　XGBoost.　Furthermore,　parallel　search　optimization　and　parameter　tuning　are　accomplished　with　HBPSO,　which　ultimately　helps　in　efficient　and　reliable　search　results.　We　also　conduct　some　experiments　to　evaluate　the　performance　of　our　proposed　models.　The　experimental　results　show　that　our　proposed　hybrid　optimization　model　is　efficient　and　outperforms　all　benchmark　schemes.　Our　proposed　model　HBPSO　achieves　an　accuracy　of　95%.　Besides　this,　the　logistics　regression　also　achieves　75%　accuracy　while　the　proposed　CNGB　model　achieves　the　highest　accuracy　of　97.9%　for　auditing　the　information　system　data,　which　shows　the　effectiveness　and　reliability　of　our　proposed　optimization　algorithm.