Trusted by institutions building next-gen finance

Chargeback Fraud

Loan Stacking

Application Fraud

Bust-Out Fraud

Ghost Funding

De-Shopping and Wardrobing
workflow assistance
Create powerful account opening fraud detection workflows with Oscilar AI.
AI-Powered Workflow Creation
Describe your workflow and rules logic in natural language and watch them come to life, tailored for account opening fraud scenarios.
Leverage Pre-Built Rules and ML Models
Easily implement rules and ML models for various account opening fraud scenarios, including loan stacking, chargeback fraud, and application fraud.
Powerful Testing Suite
Run backtests, A/B tests, and unit tests in one click to validate existing and new account opening fraud detection workflows.

comprehensive security
Obtain a true 360° view of your users and their activity with device intelligence, behavioral signals, and more.
Security-Aware Device and Behavioral Intelligence
Oscilar’s advanced D+B product provides state-of-the-art encryption and obfuscation to ensure security and prevent spoofing and reverse engineering.
Advanced Device and Behavioral Signals
Enhance customer identification and prevent account takeovers with device intelligence, behavior biometrics, and data enrichments.
Effortless 1st Party Data and Marketplace Integration
Connect your databases with one click using our automated ingestion system and access over 80 third-party data sources via the Oscilar Marketplace.
comprehensive detection
Reduce user friction with adaptive risk-based authentication.
Take advantage of Oscilar's technology that goes beyond traditional fraud detection methods. Analyze users' historical patterns of transactions, device usage, and behavioral biometrics to detect risky actions with minimal false positives.
Detect unusual transaction patterns
Identify suspicious changes in user behavior
Leverage device fingerprints and advanced behavioral profiles
Analyze cross-account linkages to uncover fraud rings


efficiency gains
Streamline investigations with AI-driven Case Management.
Intelligent Prioritization
Review cases prioritized based on urgency and severity. Easily assign cases to specialized account opening fraud investigation queues.
Holistic User View
Access all historical and real-time data about the user and their activity at your fingertips.
AI Case Insights
Receive natural language explanations of why an account opening fraud case was created, along with proactive insights to speed up reviews.
Customers see results with Oscilar.
FAQ
Glad you asked.
What is first-party fraud?
First-party fraud occurs when a legitimate applicant or account holder intentionally misrepresents information or misuses a product for financial gain. Common examples include application misrepresentation, loan stacking, first-payment default, bust-out schemes, and chargeback or refund abuse. It is difficult to detect because the person may use a real identity and behave like a good customer before the harmful activity appears.
How is first-party fraud different from third-party fraud and credit risk?
In first-party fraud, the legitimate customer or applicant is the actor; third-party fraud involves an external actor using stolen accounts, credentials, or identities. Credit risk usually reflects an inability to repay, while first-party fraud involves deliberate deception or misuse. The outcomes can look similar, so institutions need longitudinal behavior, application and portfolio data, linkages, and review processes rather than a single fraud rule.
What are common types of first-party fraud in financial services?
Common types include falsifying application information, opening multiple loans before bureaus update, making no first payment, building credit before a bust-out, disputing legitimate purchases, abusing refunds or promotions, and moving funds through accounts under the customer's control. The relevant typologies vary by lending, cards, deposits, payments, and commerce, so detection should be tailored to the product and customer journey.
How do financial institutions distinguish deliberate misuse from genuine financial distress?
No single signal proves intent. Institutions should compare repayment and delinquency patterns, utilization, income and balance changes, account activity, application representations, linked accounts, devices, behavioral signals, and prior disputes. Contradictory evidence and plausible hardship explanations should be considered. When confidence is limited, use proportionate actions and human review rather than automatically labeling a customer as fraudulent.
Which behavioral and portfolio signals can reveal first-party fraud before losses materialize?
Useful indicators can include rapid utilization, sudden spending or transaction velocity relative to history, multiple applications or accounts, funding-source changes, first-payment default, repeated chargebacks, unusual limit usage, shared devices or identities, and coordinated activity across linked accounts. These signals are stronger in combination and over time. Oscilar can combine first-party, device, behavioral, transaction, and network data in configurable decision workflows.
How should first-party fraud decisions be documented when intent is uncertain?
Record the evidence and its source, the behavioral timeline, relevant rule or model reason codes, the policy applied, alternative explanations considered, the analyst's rationale, any override, and the action taken. Documentation should distinguish observed facts from an inference about intent. Proportionate interventions, quality review, appeal or reconsideration paths, and outcome feedback help teams act consistently without overstating what the evidence proves.







