连接与治理
接通你的系统、政策与知识，让 AI 在你的规则之内工作，而不是绕过它们。
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Stop fraud before it clears. Not after.
Real-time AI that detects anomalous patterns, flags suspicious transactions and reduces false positives — so your risk team acts on signal, not noise.
The problem
Rule-based controls can't catch adaptive fraud
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Rule-based systems miss adaptive fraud
Static rules work until fraudsters adapt. New attack patterns bypass existing controls until someone notices and updates the rules.
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False positives that damage the customer relationship
Overly aggressive detection blocks legitimate customers. Every false positive is a friction event — and a potential churn event.
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Investigation backlog that never clears
Analysts review flagged cases manually. The queue grows faster than the team can work through it.
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Models that drift unnoticed
Fraud patterns evolve faster than rule and model updates. Detection quality decays until losses or false positives spike.
How it works
从信号到成果——治理内置
自动化与辅助
让工作流上线：分流、起草、解决或升级，全程带完整上下文与审计追溯。
度量与改进
跟踪运营 KPI、质量与风险——再和你的团队一起调优剧本。
流程序列会适配你的工具、渠道与风险态势。
Stop fraud before it clears. Not after.
What's included
What you get when you run this with Thinkia
A governed layer across data, workflows, and handoffs—so teams ship safely and scale with metrics.
Real-time transaction scoring
Assesses every transaction for fraud probability at the moment it occurs.
Behavioural anomaly detection
Learns normal patterns per user, account or entity and flags deviations.
Adaptive model updating
Retrains on new fraud patterns continuously without requiring manual rule updates.
False positive reduction
Contextual scoring that distinguishes suspicious from legitimate unusual behaviour.
Investigation prioritisation
Ranks flagged cases by risk score and evidence strength so analysts focus on what matters.
Audit and regulatory reporting
Full decision log for every flagged transaction, formatted for regulatory submission.
Powered by Thinkia Sentinel
Results
What changes when this runs in production
Results vary by transaction volume, fraud typology and existing detection infrastructure.
+40%
Improvement vs. rule-based baseline on adaptive fraud patterns
Orientative — confirmed in discovery; depends on the starting point.
–35%
Reduction in legitimate transactions incorrectly flagged
Orientative — confirmed in discovery; depends on the starting point.
–50%
With AI-prioritised queue and pre-assembled evidence
Orientative — confirmed in discovery; depends on the starting point.
How we work
From rules fatigue to adaptive signals analysts can explain
Signal inventory
Signal inventory
Week 1–2
Fraud typologies, data feeds, and investigation workflows are baselined with your SOC/FIU.
Model & thresholds
Model & thresholds
Week 3–5
Scores, tiers, and override paths are tuned for precision/recall and regulatory expectations.
Champion/challenger
Champion/challenger
Week 6–9
Shadow scoring on live traffic; investigators validate alerts and narrative quality.
Operate & refresh
Operate & refresh
Week 10+
Feedback loops, drift monitoring, and periodic model reviews enter BAU governance.
Latency and explainability requirements vary by product line; scope follows highest-loss flows first.
Go deeper
Risk, detection, and explainable AI
Ideas, trends, and tools to stay ahead
Get started
Ready to scope this for your context?
We start with a focused session—no commitment—to map constraints and a sensible path.