How Oscilar's Next-Gen Platform Aims to Close the AI Fraud Gap Worth Billions

The financial services industry faces a mounting crisis. As artificial intelligence tools become increasingly accessible, fraud losses driven by AI technology are projected to skyrocket from $12.3 billion in 2023 to $40 billion by 2027 in the United States alone—representing a staggering 32% compound annual growth rate. Traditional defenses are crumbling against this onslaught, prompting innovators like Oscilar to rethink digital identity verification from the ground up.

The Problem: Legacy Solutions Can’t Keep Pace

Conventional device fingerprinting and behavioral biometrics were designed for a different era. Today’s fraudsters, armed with democratized AI tools and sophisticated attack frameworks, can systematically circumvent these older approaches. The vulnerability isn’t a minor flaw—it’s structural. Traditional systems expose their detection logic, making them predictable targets for automated attacks and reverse engineering attempts.

Financial institutions and fintechs find themselves trapped between two impossible choices: either accept higher fraud losses or implement friction-heavy security that drives legitimate customers away. The status quo isn’t sustainable.

Oscilar’s Answer: Cognitive Signatures That Can’t Be Faked

Under the direction of CEO Neha Narkhede—the entrepreneur who co-created Apache Kafka and built Confluent into a $10 billion real-time data streaming powerhouse serving over 80% of Fortune 500 companies—Oscilar has developed a fundamentally different approach. The company’s Cognitive Identity Intelligence Platform leverages proprietary Digital & Behavior Identification technology to analyze thousands of unique signals across network, device, and behavioral dimensions.

Rather than relying on a fixed set of detection rules, Oscilar’s system generates dynamic “cognitive signatures” for each user interaction. These signatures emerge from polymorphic code and execution paths that shift across sessions, making the system’s patterns virtually impossible for automated tools to learn or replicate. The architecture processes this complex signal network in real-time across a distributed infrastructure capable of handling over 100,000 transactions per second while continuously adapting ML models to emerging fraud patterns.

Building on Deep Expertise

Chief Product Officer Saurabh Bajaj, who previously led fraud prevention initiatives protecting Fortune 500 enterprises, top-tier banks, government agencies, and healthcare organizations, spearheaded the platform’s development. This combination of cybersecurity rigor and fraud prevention depth created something the team describes as “security-first”—meaning the architecture itself prevents fraudsters from reverse-engineering detection methods.

The result eliminates the traditional trade-off between security and user experience. Legitimate users encounter minimal friction while sophisticated synthetic identity attacks and coordinated fraud schemes become exponentially harder to execute successfully.

Core Technical Innovations

The platform introduces several breakthrough capabilities: advanced cognitive signature technology creates unique digital fingerprints that persist across devices and sessions, making synthetic identity creation nearly impossible. A security-first architecture employs military-grade protections to keep detection methodologies hidden from adversaries. End-to-end journey protection provides continuous authentication across all touchpoints with real-time risk assessment. The system leverages generative AI to dynamically update risk strategies, and integrates seamlessly with existing enterprise risk infrastructure.

Real-World Validation

Early adoption tells the story. Over a dozen major financial institutions, including Happy Money and Curve, are already deploying the platform. At Happy Money, which serves over 300,000 members, the system passively monitors cognitive signatures during loan applications and account management without adding friction for legitimate applicants. The platform catches sophisticated synthetic identities and fraud attempts that would slip past conventional defenses, enabling the institution to maintain trust while protecting capital deployed to borrowers.

This combination of measurable fraud reduction and improved user experience suggests Oscilar has solved the central problem that plagued earlier generations of fraud prevention technology.