AI Fraud Detection for SAP

I need an end-to-end AI automation that scans every incoming invoice in my AWS-hosted environment, analyses it for fraud indicators, and then—when something looks suspicious—flags the invoice for a human reviewer inside SAP instead of blocking payment automatically. Core checks the model must perform: • Spot duplicate invoices or repeated invoice numbers. • Catch unusual amounts or unexpected variances from a supplier’s historical pattern. • Detect any supplier bank-account or payment-detail changes that could indicate tampering. The solution has to plug straight into our SAP finance ERP, exchange data securely through approved APIs or BAPIs, and run within our existing AWS stack. I’m open to the exact AWS services you choose—SageMaker, Lambda, Step Functions, DynamoDB, or others—as long as the pipeline is serverless or containerised, scalable, and easy to maintain. Deliverables 1. A trained, explainable ML model (or ensemble) optimised for invoice fraud signals. 2. Integration code that posts the model’s confidence score and “Flag for review” status back into SAP with traceable audit logs. 3. A lightweight dashboard or CloudWatch/Grafana board showing detection rates, false positives, and system health. 4. Deployment scripts (Terraform or CloudFormation preferred) and concise runbook/documentation so my in-house DevOps team can reproduce the environment. Acceptance criteria will be a working demo against a masked sample data set, <3 % false-positive rate on known clean invoices, and clean hand-over of IaC plus source code in Git. --- 來源：Freelancer.com | 出價次數：36 | 剩餘時間：6 日 | 專案類型：固定預算