Flag Suspicious Transactions with Hybrid SQL

Combine structured financial data with semantic context. JOIN_VECTOR links transaction tables to agent memory for intelligent fraud detection.

The Problem

Blind Spot Fraud

Rule-based systems miss novel patterns. Fraudsters adapt faster than rules.

Slow Investigations

Analysts manually cross-reference dozens of systems to build context.

Compliance Gaps

Regulators demand explainable reasoning. Black-box ML can't provide it.

The HatiData Fix

Hybrid SQL

JOIN_VECTOR merges transaction data with semantic intelligence in one query.

Chain-of-Thought

Every decision is hash-chained and replayable for regulators.

Real-Time Detection

Semantic triggers fire when new patterns match known fraud signatures.

See It in Action

-- Hybrid query: structured data + semantic context
SELECT t.id, t.amount, t.counterparty,
       semantic_rank(m.embedding, 'suspicious wire transfer') AS risk_score
FROM transactions t
JOIN_VECTOR agent_memories m
  ON semantic_match(m.embedding, 'suspicious wire transfers', 0.8)
WHERE t.amount > 10000
ORDER BY risk_score DESC
LIMIT 20;

94%

fraud detection rate

<200ms

query latency

100%

decision auditability

Related Use Cases

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Legal & Contract Auditing

Immutable chain-of-thought ledger means every agent decision is hash-chained, replayable, and court-admissible.

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Cybersecurity Threat Hunting

Security agents correlate alerts, IOCs, and threat intel using semantic triggers. Novel threats surface before signatures exist.

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