Stop Fraud
Before
It Happens

Fraud is not a niche problem confined to a single industry. It is a systemic failure operating across every sector of the economy — government programs, insurance, financial services, and commerce. The losses are not abstractions. They represent real money paid to criminals, benefits denied to legitimate recipients, and public trust eroded at scale.

The dominant model for fraud prevention is reactive: monitor transactions, analyze patterns, flag anomalies — and investigate after the fact. This model has a fundamental structural flaw. By the time fraud is identified, the consequences are already locked in.

1. 01
   The Damage Is Done First

   By the time fraud is detected — typically days, weeks, or months after the transaction — money has been paid, benefits have been disbursed, identities have been exploited. Detection without prevention is damage control, not fraud prevention.
2. 02
   Recovery Is Rare and Expensive

   Less than 3% of fraud losses are ever recovered. Pursuing recovery requires legal action, cooperation from financial institutions, and enforcement resources that frequently exceed the value of what might be recouped. The economics of after-the-fact recovery are deeply unfavorable.
3. 03
   Conventional Technology Cannot Reconcile Fast Enough

   Stopping fraud at transaction time requires querying multiple disparate data sources — Social Security Administration, IRS, medical records, DMV, claims history, financial records — simultaneously, in milliseconds. General-purpose database infrastructure is too slow and too expensive to do this at the scale of real production systems.

A fraud attempt is not random noise — it carries specific, verifiable attributes. A fraudulent Medicaid claim has a provider ID, a patient identity, a procedure code, a location, and a date. A fraudulent insurance claim has a policy number, a claimant identity, an incident description, and a repair estimate. A fraudulent PPP loan application has a business identity, a payroll record, and a bank account.

Each of these attributes can be reconciled against independent data sources. When the fraud is real, some attributes will fail to reconcile. That failure is detectable — if you can query fast enough.

• Identity Reconciles against SSA, credit bureaus, voter registration, DMV records
• Location Reconciles against address databases, IP geolocation, prior claim history
• Credentials Reconciles against provider registries, professional license databases, employer records
• Financial Reconciles against IRS records, bank account ownership, payroll filings
• Behavioral Reconciles against historical patterns and peer cohort analysis

Real-time reconciliation applies across every fraud domain. The data sources differ; the mechanism is the same — expose the attributes of the fraud attempt to independent data, and the inconsistencies surface immediately.

Real-time reconciliation is not pattern matching or anomaly scoring after the fact. It is a deterministic process that validates the specific claims made by a transaction against independent, authoritative data sources at the moment of the attempt.

1. 1
   Attempt Arrives

   A claim, transaction, or application enters the system in real time. The attempt contains asserted attributes — identity, location, credentials, financial details.
2. 2
   Attributes Are Extracted

   The reconciliation engine parses the attempt and identifies the set of verifiable attributes: identity claims, location assertions, credential references, financial figures.
3. 3
   Parallel Queries Dispatched

   Fractal simultaneously queries all configured data sources — no sequential waiting. Each source receives only the attributes it is authoritative for.
4. 4
   Each Source Scores Its Attributes

   Each data source independently confirms or rejects the attributes it received. A mismatch — an identity that doesn't reconcile with SSA records, a provider not in the CMS registry — is flagged immediately.
5. 5
   Reconciliation Engine Aggregates

   A weighted reconciliation score is computed across all source responses. The weights reflect the authoritative confidence of each source for each attribute type.
6. 6
   Decision in Milliseconds

   The attempt is approved, flagged for review, or blocked — with a full, immutable audit trail. No human in the loop required for clear-cut cases.

Conventional database systems fail at real-time multi-source reconciliation for two structural reasons: speed and cost. Querying 10 disparate data sources sequentially at transaction time adds hundreds of milliseconds of latency per abstraction boundary crossed — a 10⁷ penalty relative to raw hardware capability. Doing this at scale — millions of transactions per day — is economically infeasible on general-purpose infrastructure.

Fractal Computing was designed from first principles to eliminate these two constraints simultaneously. Its proprietary Locality Optimization™ technology ensures that each Fractal instance processes only data it holds locally — eliminating network I/O from the reconciliation hot path entirely.

Because each Fractal instance manages a discrete, pre-partitioned data partition and carries a complete copy of the application logic, the system scales horizontally with no single point of failure and no shared-memory bottleneck. A 10-million-customer reconciliation application runs on 10 Intel NUC servers — no cloud, no data center.

For organizations evaluating a real-time fraud reconciliation deployment, Fractal provides full technical documentation: