Vision Verification & Fraud Detection
Computer vision for signature, document and identity verification at operational scale.
What it does
Banks, lenders and insurers still rely on manual checks for signatures, instruments and identity documents. These checks are high-volume, repetitive and subject to regulatory requirements on data residency and auditability.
Rabbit Cloud's Vision Verification solution applies computer vision on Amazon SageMaker to these tasks: signature matching against reference specimens, tamper and forgery detection, and identity document authenticity checks. Scores within a defined uncertainty band are routed to a reviewer console. The solution is deployed in the AWS Region required by the client's regulator, with model versioning and access logging so that every decision can be traced.
Built for
- Signature verification for banking and lending operations
- Cheque and instrument tamper detection
- Identity document authenticity checks
- Damage, defect and condition assessment
From input to auditable output
Capture
Instruments and documents are captured at the branch, on mobile or by scanner and pre-processed: deskew, denoise and segmentation.
Reference
Reference specimens are stored in Amazon S3 with versioning and access controls.
Score
Amazon SageMaker inference endpoints score the capture against reference specimens and authenticity models.
Decide
Scores above the acceptance threshold pass automatically. Scores in the uncertainty band are routed to a reviewer.
Audit
Every decision, score, model version and reviewer action is recorded for regulatory review.
AWS services
Controls built in
- In-Region deployment, including ap-south-1, for data residency
- Thresholds calibrated with the client's risk and compliance function
- Model versioning with rollback and per-decision traceability
- Encryption at rest and in transit with customer managed keys
- Access logging and reviewer accountability
How the solution is delivered
Each stage defines scope, cost and outcomes before the next commitment. Qualifying engagements are structured to AWS partner programme requirements.
| Stage | Duration | Outcome |
|---|---|---|
| Assessment | 2 to 4 weeks | Use-case scoring, data readiness review, evaluation set design and a cost-per-transaction model. |
| Proof of concept | 4 weeks | Working pipeline on the client's data against agreed success criteria, with measured accuracy and cost. |
| Production build | 8 to 16 weeks | Hardened deployment in the client's AWS account: infrastructure as code, guardrails, monitoring and integration. |
| Operate | Ongoing | Evaluation on every change, model and cost review, and managed operations under SLOs. |
Frequently asked
What accuracy can we expect?
Accuracy depends on capture quality and specimen coverage. A baseline is established on the client's own data during the proof of concept, and thresholds are set against the client's risk appetite.
Can it be integrated into our core banking platform?
Yes. The solution exposes an API that is integrated with the core banking, cheque processing or onboarding workflow.
Is customer data used to train models?
Client data is used only to evaluate and, where agreed, fine-tune models for that client. It is not shared with or used for any other client.
Discuss Vision Verification & Fraud Detection with a solutions architect
A structured assessment determines feasibility, data readiness and the appropriate delivery path before any commitment to build.