AN ANALYST
THAT COMPOUNDS.
An LLM that hallucinates a fraud explanation is worse than no explanation. NonaShield's agentic layer is built to refuse to answer rather than invent one — and every case it closes makes the next one sharper.
The Compounding Intelligence Loop.
Each fraud caught → labelled training data → better model → fewer false positives → analyst trusts the model → more autonomy → faster response.
From Day 1 to Data Asset.
- Root / hook detection
- Screen-share & overlay caught
- Digital-arrest signal
- 13 compliance frameworks
- Per-user tap cadence
- Session & posture history
- False-positive rate drops
- Anomaly threshold calibrates
- Attack-cluster detection
- Mule-account patterns
- RBI 2027 auto-evidence
- SOC analyst hours cut 60%+
- Zero-day pattern prediction
- Cross-tenant threat intel
- Proprietary fraud graph
- Sellable / capital-reserve evidence
// The Analyst Pipeline
Every case moves through Context Builder → RAG Retriever (similar historical cases) → LLM reasoning → Guardrail Validator → Insight Formatter, producing a structured payload: insight, confidence, risk level, recommended action, and evidence links back to the real graph entities cited.
// Hallucination Guardrails
- Every cited entity ID must exist in ground-truth graph context, or the response is rejected.
- Output schema is strictly validated before it reaches an analyst.
- Confidence is hard-capped below 1.0 — an LLM can never claim deterministic certainty.
Escalation, never
silent downgrade.
The Autonomous Decision Enhancer implements the RBI's autonomous-risk-decision mandate: when the async analyst's confidence exceeds a configurable threshold and the recommended action escalates risk, it pushes an enforcement command over a 4.5-second device poll cycle — with a full audit trail. It can never quietly downgrade a synchronous BLOCK, and it carries its own kill switch.
Why It Compounds.
Autonomous Threat Response
The kill switch blocks a device within one 4.5-second poll cycle, without waiting on a human. BLOCK / STEP_UP / FORCE_OTP execute before fraud completes.
Behavioural Biometrics Memory
Every touch, swipe, and keystroke cadence builds a permanent per-device model. A fraudster with the right OTP still fails the behavioural gate.
Compliance Auto-Mapping
Every signal auto-maps to RBI, NPCI, PCI-DSS, CERT-In, ISO 27001 and more — 13 frameworks in total. Audit evidence generated in real time.
Fraud Graph Network Effect
Mule accounts, SIM-swap rings, and coercion clusters are linked across sessions. One fraud caught trains the pattern for every future session.
Predictive Pre-Transaction Gate
Agentic advisory scores device risk before the transaction executes. Coercion and manipulation signals flag high-risk sessions before OTP entry.
Proprietary Data Asset
After 12 months, the customer owns a labelled fraud dataset across 47+ signal types, behavioural profiles, and attack timelines.
// Intelligence Compound Rate
Value grows non-linearly with data volume — the model gets meaningfully better, not just marginally.
What Customers See.
Threats blocked at the device layer — before any transaction reaches the network.
Agentic AI pre-triages every alert with signal attribution, evidence chain, and a recommended action.
Kill switch pushes BLOCK to device within one 4.5-second poll cycle — no human in the loop.
13 frameworks auto-mapped; RBI, NPCI and PCI-DSS evidence generated per signal, in real time.
Behavioural baseline plus config-driven trust registry eliminate noise as the model matures.
A labelled, device-level fraud dataset across 47+ signal types — no public dataset equivalent exists.
The LLM layer is explicitly async and analyst-only — it never sits on the sub-200ms real-time blocking path. The instant decision a user experiences comes from deterministic rules and ML scoring; the agentic layer investigates afterward, explains the case, maps it to regulatory clauses, and — only through the Autonomous Decision Enhancer's own guarded, escalation-only path, over a 4.5-second poll cycle — can subsequently tighten enforcement. We don't market instant LLM-driven blocking, because that isn't how it's built. The "10× accuracy" and outcome ranges above describe the expected trajectory as the behavioural and fraud-pattern models mature — treat them as a maturity curve, not a day-one guarantee.
The Verdict.
"A model that can't point to its evidence isn't an analyst. It's a guess with better grammar. One that compounds for two years is a moat."