AI Fraud Intelligence
for Indonesia's Digital Financial Ecosystem
Helping financial institutions detect suspicious transactions, reduce fraud losses and improve investigation efficiency through explainable AI and graph intelligence.
Rule-based fraud systems can't keep up with Indonesia's transaction velocity
Static rules generate noise, miss new patterns, and force analysts into reactive investigation. Modern fraud is adaptive — defense needs to be too.
High false positives
Legacy rule engines flag 30–60% legitimate traffic, eroding customer trust and overwhelming ops teams.
Blind to coordinated attacks
Mule networks, device farms and QRIS abuse stay invisible without behavioral graph context.
Long investigation cycles
Manual triage across siloed logs delays decisions and increases recovery cost per incident.
A complete fraud intelligence stack — not a black box
SAFER combines deterministic risk scoring, ML behavioral models and graph analytics into one operational platform.
Explainable AI scoring
Every decision returns weighted indicators in plain language — auditable for analysts and regulators.
Real-time monitoring
Sub-150ms scoring for QRIS, BI-FAST, e-wallet and card rails at national-scale throughput.
Fraud graph intelligence
Connect accounts, devices and merchants to surface mule rings before losses accumulate.
Compliance-by-design
PDP-aligned data handling, RBAC, immutable audit logs and anonymized model inputs.
API integration layer
Drop-in REST and streaming endpoints — production integration in under two weeks.
Sovereign deployment
Run in shared SaaS, private VPC or fully on-premise to meet OJK and internal risk policies.
Built for fraud analysts, not for dashboards
Triage queues, investigation timelines and graph drilldowns designed around how SOC and fraud ops teams actually work.
Unified queue across rails — QRIS, BI-FAST, e-wallet, card.
Per-transaction explanation: indicators, weights, peer comparison.
One-click escalation with full case packet for compliance review.
Configurable risk policies with shadow-mode evaluation.
%3C%2Ftext%3E%0A%20%20%3Cpolyline%20fill%3D'none'%20stroke%3D'%233b82f6'%20stroke-width%3D'2'%20points%3D'40%2C330%2080%2C300%20120%2C310%20160%2C270%20200%2C290%20240%2C240%20280%2C250%20320%2C210%20360%2C230%20400%2C200%20440%2C180%20480%2C210%20520%2C170%20560%2C200'%2F%3E%0A%20%20%3Cpolyline%20fill%3D'none'%20stroke%3D'%23ef4444'%20stroke-width%3D'2'%20points%3D'40%2C340%2080%2C335%20120%2C330%20160%2C320%20200%2C322%20240%2C315%20280%2C310%20320%2C300%20360%2C305%20400%2C295%20440%2C290%20480%2C288%20520%2C280%20560%2C275'%2F%3E%0A%3C%2Fsvg%3E)
Drop-in API integration — production-ready in under two weeks
A single REST call returns risk score, explainability, severity and suggested action. Streaming webhooks for real-time monitoring.
Aligned with OJK, Bank Indonesia and UU PDP
Security and governance controls designed for regulated financial institutions, not bolted on after the fact.
Built for Indonesia's tiered financial market
From tier-2 fintechs to enterprise banks and regulator consortia — SAFER scales pricing and deployment with you.
API-first SaaS, usage-based pricing.
Private deployment with managed ops.
Consortium intelligence sharing.
Building Indonesia's fraud intelligence ecosystem
SAFER's long-term goal is a collaborative, regulator-supported fraud intelligence network that protects the entire digital financial ecosystem.
Shared intelligence
Anonymized fraud signals shared across participating institutions — attacks detected once, defended everywhere.
Regulator dashboards
OJK and BI gain real-time sector-wide fraud visibility without accessing individual customer data.
Cross-institution graph
Mule networks that span multiple banks become visible through federated graph queries.
National fraud baseline
Benchmarking fraud rates, typologies and response times across the Indonesian financial sector.
See SAFER work on your transaction patterns.
Explore the live console, simulate fraud scenarios and walk through the fraud graph — no signup required.