AI Fraud Detection for Vietnamese Banks
Real-time detection across card, transfer, account-takeover and application fraud — adaptive models with analyst-friendly case management and risk-based step-up.
Overview
BancoOS scores every authorisation and transfer in real time with adaptive ML, and gives fraud analysts a unified case workspace with Copilot.
Industry challenges
- Card, transfer and ATO fraud escalating in real time.
- Rules-only systems lag novel patterns.
- Blanket blocks create customer friction.
- Chargeback learning loops are manual and slow.
The BancoOS solution
Rules + ML run together, chargeback outcomes retrain models automatically, and risk-based step-up minimises customer friction on low-risk activity.
Workflow diagram
Business outcomes
- 40–60% higher true-positive rate at same false-positive budget.
- Faster analyst response and case resolution.
- Fewer customer step-ups on low-risk activity.
- Closed-loop learning from chargebacks.
Integrations
Authorisation switches, core banking, card processors, device intelligence and CRM.
Security & compliance
Designed to operate within PCI-DSS scope with tokenisation, encryption, tenant isolation and in-region hosting.
Frequently asked questions
›Which fraud types does BancoOS detect?
Card-not-present, ATM/POS, wire and instant-transfer fraud, account takeover, mule accounts, application fraud and internal fraud.
›Is detection real-time?
Yes. Sub-100ms scoring for authorisation flows; asynchronous scoring for downstream monitoring.
›Do you replace our existing rules?
No — rules and ML run together. Fraud teams keep control of policy while ML picks up novel patterns.
›How are models kept current?
Models retrain on labelled outcomes; drift monitoring alerts the platform team automatically.
›How is chargeback data incorporated?
Chargebacks and confirmed fraud flow back into training and rules-tuning to close the loop.
›Can analysts investigate cases inside BancoOS?
Yes. A case workspace shows scores, contributing features, related entities and prior activity, with Copilot for natural-language questions.
›How does BancoOS handle customer disruption from false blocks?
Risk-based step-up: only borderline transactions trigger customer verification; low-risk flow through.
›What data sources are needed?
Authorisation streams, transaction history, device and session signals, and customer profile data.
›Is BancoOS PCI-compliant?
The platform is designed to operate within a PCI-DSS scope; card data can be tokenised so BancoOS never handles PANs directly.
›What ROI do banks see?
Typical results: 40–60% higher true-positive rate at the same false-positive budget, and faster analyst response.
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