Financial crime hides in relationships, not records.
Rule-based AML systems evaluate transactions in isolation. Traditional ML models score individual accounts. Neither approach can see what actually matters: the network. Money laundering, fraud rings, and tax evasion schemes are structurally invisible to point-in-time, record-level analysis. Odin's graph intelligence layer is built to expose exactly what those systems miss.
Every dimension of financial crime, covered.
Anti-Money Laundering (AML)
Trace layering and integration stages through graph paths too complex for rule-based systems. Surface laundering networks across institutions without sharing raw data.
Know Your Customer (KYC)
Resolve entity identity across fragmented records. Link beneficial ownership, sanctions exposure, and risk signals into a single, continuously updated entity view.
Financial Crime Networks
Map multi-layered criminal networks spanning accounts, shell companies, and jurisdictions. Expose coordinated fraud rings before they breach the perimeter.
Tax Fraud Detection
Identify synthetic entities, circular fund flows, and offshore structures that evade line-item detection. Graph traversal reveals what transaction monitoring cannot.
Customer Intelligence
Construct a unified entity view across every touchpoint, account, and relationship. See the customer — not the record — for deeper risk and opportunity signals.
Insurance Claims Fraud
Connect claimants, providers, and brokers across claims histories. Expose coordinated fraud rings and inflated claim networks in real time.
Anti-Money Laundering
AML compliance requires detecting layering and integration stages across transaction flows that span multiple accounts, jurisdictions, and beneficial owners. Rule-based systems fail because they cannot see the graph — they only see individual transactions. Odin builds and continuously updates a live knowledge graph across your institution's data, enabling detection of laundering networks that appear unrelated when evaluated per-transaction.
Know Your Customer
KYC entity resolution is the process of constructing a complete, accurate picture of who a customer is — across fragmented identity records, corporate registries, sanctions lists, and PEP databases. Manual KYC reviews are slow, inconsistent, and point-in-time. Odin automates continuous entity resolution, maintaining a live view of each customer's identity, ownership structure, and risk exposure as new information surfaces.
Fraud Detection
Fraud rings coordinate activity across many accounts and claimants to stay below individual detection thresholds. Graph intelligence detects this coordination by mapping relationships — shared devices, addresses, phone numbers, and transaction counterparties — across your entire institutional dataset. What looks like isolated fraud events becomes a visible network when viewed through the graph.
Frequently Asked Questions
How does AI improve financial crime detection?
AI — specifically graph intelligence — models the relationships between entities, accounts, and transactions rather than evaluating each in isolation. This surfaces connections across large networks that rule-based systems and simple ML models cannot detect, making it significantly more effective for financial crime, AML, and fraud ring detection.
What is graph AI for anti-money laundering?
Graph AI for AML uses graph data structures to model how money flows between entities — individuals, accounts, shell companies, and jurisdictions. By traversing these graphs, the system identifies layering and integration patterns indicative of money laundering that appear unrelated when viewed in isolation.
Can AI be used for KYC compliance?
Yes. AI for KYC automates entity resolution — linking fragmented identity records, PEP and sanctions lists, corporate registries, and beneficial ownership data into a single, continuously updated customer view. This reduces manual review time and improves accuracy compared to point-in-time static checks.
How does Prescott Data handle cross-institutional financial crime detection?
Odin is designed for zero-trust environments where raw data cannot be shared across institutional boundaries. It performs graph intelligence and entity resolution within each institution's data perimeter, enabling consortium-level financial crime detection without violating data sovereignty.
What industries use AI for fraud detection?
Financial services, insurance, healthcare, and government agencies all deploy AI for fraud detection. Use cases include insurance claims fraud, tax fraud, identity fraud, synthetic account creation, and coordinated account takeover attacks.