2025 marked a turning point in digital identity risk. Fraud didn’t simply become more sophisticated – it became industrialized. What emerged across financial institutions was not a new fraud “type,” but a new production model: fraud operations shifted from human-led tactics to system-led pipelines capable of assembling identities, navigating onboarding flows, and adapting to defenses at machine speed.

Synthetic identities, account takeover attempts, and document fraud didn’t just rise in volume; they became more operationally consistent, more repeatable, and more automated. Fraud rings began functioning less like informal criminal networks and more like tech companies: deploying AI agents, modular tooling, continuous integration pipelines, and automated QA-style probing of institutional controls.

This is why so many identity controls failed in 2025. They were calibrated for adversaries who behave like people. 

Automation Became the Default Operating Mode

The most consequential development of 2025 was the normalization of autonomous or semi-autonomous fraud workflows. AI agents began executing tasks traditionally requiring human coordination: assembling identity components, navigating onboarding flows, probing rule thresholds, and iterating on failures in real time. Anthropic’s September findings – documenting agentic AI gaining access to confirmed high-value targets – validated what fraud teams were already observing: the attacker is no longer just an individual actor but a persistent, adaptive system.

According to Visa, activity across their ecosystem shows clear evidence of an AI shift. Mentions of “AI Agent” in underground forums have surged 477%, reflecting how quickly fraudsters are adopting autonomous systems for social engineering, data harvesting, and payment workflows.

Operational consequences were immediate:

• Attempt volumes exceeded human-constrained detection models
• Timing patterns became too consistent for human-based anomaly rules
• Retries and adjustments became systematic rather than opportunistic
• Session structures behaved more like software than people
• Attacks ran continuously, unaffected by time zones, fatigue, or manual bottlenecks

Controls calibrated for human irregularity struggled against machine-level consistency. The threat model had shifted, but the control model had not.

Synthetic Identity Production Reached Industrial Scale

2025 also saw the industrialization of synthetic identity creation – driven by both generative AI and the rapid expansion of fraud-as-a-service (FaaS) marketplaces. What previously required technical skill or bespoke manual work is now fully productized. Criminal marketplaces provide identity components, pre-validated templates, and automated tooling that mirror legitimate SaaS workflows.

These marketplaces supply:

• AI-generated facial images and liveness-passing videos
• Country-specific forged document packs
• Pre-scraped digital footprints from public and commercial sources
• Bulk synthetic identity templates with coherent PII
• Automated onboarding scripts designed to work across popular IDV vendors
• APIs capable of generating thousands of synthetic profiles at once
• And more…

This ecosystem eliminated traditional constraints on identity fabrication. In North America, synthetic document fraud rose 311% year-on-year. Globally, deepfake incidents surged 700%. And with access to consumer data platforms like BeenVerified, fraud actors needed little more than a name to construct a plausible identity footprint.

The critical challenge was not just volume, but coherence: synthetic identities were often too clean, too consistent, and too well-structured. Legacy controls interpret clean data as low risk. But today, the absence of noise is often the strongest indicator of machine-assembled identity.

Because FaaS marketplaces standardized production, institutions began seeing near-identical identity patterns across geographies, platforms, and product types – a hallmark of industrialized fraud. Controls validated what “existed,” not whether it reflected a real human identity. That gap widened every quarter in 2025.

Where Identity Controls Reached Their Limits

As fraud operations industrialized, several foundational identity controls reached structural limits. These were not tactical failures; they reflected the fact that the underlying assumptions behind these controls no longer matched the behavior of modern adversaries.

Device intelligence weakened as attackers shifted to hardware

For years, device fingerprinting was a strong differentiator between legitimate users and automated or high-risk actors. This vulnerability was exposed by Europol’s Operation SIMCARTEL in October 2025, one of many recent cases where criminals used genuine hardware and SIM box technology, specifically 40,000 physical SIM cards, to generate real, high-entropy device signals that bypassed checks. Fraud rings moved from spoofing devices to operating them at scale, eroding the effectiveness of fingerprinting models designed to catch software-based manipulation.

Knowledge-based authentication effectively collapsed

With PII volume at unprecedented levels and AI retrieval tools able to surface answers instantly, knowledge-based authentication no longer correlated with human identity ownership. Breaches like the TransUnion incident in late August 2025, which exposed 4.4 million sensitive records, flood the dark web with PII. These events provide bad actors with the exact answers needed to bypass security questions, and when paired with AI retrieval tools, render KBA controls defenseless. What was once a fallback escalated into a near-zero-value signal.

Rules were systematically reverse-engineered

High-volume, automated adversarial probing enabled fraud actors to map rule thresholds with precision. UK Finance and Cifas jointly reported 26,000 ATO attempts engineered to stay just under the £500 review limit. Rules didn’t fail because they were poorly designed. They failed because automation made them predictable.

Lifecycle gaps remained unprotected

Most controls still anchor identity validation to isolated events – onboarding, large transactions, or high-friction workflows. Fraud operations exploited the unmonitored spaces in between:

• contact detail changes
• dormant account reactivation
• incremental credential resets
• low-value testing

Legacy controls were built for linear journeys. Fraud in 2025 moved laterally.

What 2026 Fraud Strategy Now Requires

The institutions that performed best in 2025 were not the ones with the most tools – they were the ones that recalibrated how identity is evaluated and how fraud is expected to behave. The shift was operational, not philosophical: identity is no longer an event to verify, but a system to monitor continuously.

Three strategic adjustments separated resilient teams from those that saw the highest loss spikes.

1. Treat identity as a longitudinal signal, not a point-in-time check

Onboarding signals are now the weakest indicators of identity integrity. Fraud prevention improved when teams shifted focus to:

• behavioral drift over time
• sequence patterns across user journeys
• changes in device, channel, or footprint lineage
• reactivation profiles on dormant accounts
  ‍

Continuous identity monitoring is replacing traditional KYC cadence. The strongest institutions treated identity as something that must prove itself repeatedly, not once.

2. Incorporate external and open-web intelligence into identity decisions

Industrialized fraud exploits the gaps left by internal-only models. High-performing institutions widened their aperture and integrated signals from:

• digital footprint depth and entropy
• cross-platform identity reuse
• domain/phone/email lineage
• web presence maturity
• global device networks and associations

These signals exposed synthetics that passed internal checks flawlessly but could not replicate authentic, long-term human activity on the open web.

Identity integrity is now a multi-environment assessment, not an internal verification process.

3. Detect automation explicitly

Most fraud in 2025 exhibited machine-level regularity – predictable timing, optimized retries, stable sequences. Teams that succeeded treated automation as a primary signal, incorporating:

• micro-timing analysis
• session-structure profiling
• velocity and retry pattern detection
• non-human cadence modeling

Fraud no longer “looks suspicious”; it behaves systematically. Detection must reflect that.

4. Shift from tool stacks to orchestration

Fragmented fraud stacks produced fragmented intelligence. Institutions saw the strongest improvements when they unified:

• IDV
• behavioral analytics
• device and network intelligence
• OSINT and digital footprint context
• transaction and account-change data

into a single, coherent decision layer. Data orchestration provided two outcomes legacy stacks could not:

1. Contextual scoring – identities evaluated across signals, not in isolation
2. Consistent policy application – reducing false positives and operational drag

The shift isn’t toward more controls; it is toward coordination.

Closing Perspective

Identity controls didn’t fail in 2025 because institutions lacked capability. They failed because the models underpinning those controls were anchored to a world where identity was stable, fraud was manual, and behavioral irregularity differentiated good actors from bad.

In 2025, identity became dynamic and distributed. Fraud became industrialized and system-led.

Institutions that recalibrate their approach now – treating identity as a living system, integrating external context, and unifying decisioning layers – will be best positioned to defend against the operational realities of 2026.

At Heka Global, our platform delivers real-time, explainable intelligence from thousands of global data sources to help fraud teams spot non-human patterns, identity inconsistencies, and early lifecycle divergence long before losses occur.

‍

FOR IMMEDIATE RELEASE

‍

Heka Raises $14M to bring Real-Time Identity Intelligence to Financial Institutions

Windare Ventures, Barclays and other institutional investors back Heka’s AI engine as financial institutions seek stronger defenses against synthetic fraud and identity manipulation.

New York, 15 July 2025

‍

Consumer fraud is at an all-time high. Last year, losses hit $12.5 billion – a 38% jump year-over-year. The rise is fueled by burner behavior, synthetic profiles, and AI-generated content. But the tools meant to stop it – from credit bureau data to velocity models – miss what’s happening online. Heka was built to close that gap.

Inspired by the tradecraft of the intelligence community, Heka analyzes how a person actually behaves and appears across the open web. Its proprietary AI engine assembles digital profiles that surface alias use, reputational exposure, and behavioral anomalies. This helps financial institutions detect synthetic activity, connect with real customers, and act faster with confidence.

At the core of Heka’s web intelligence engine is an analyst-grade AI agent. Unlike legacy tools that rely on static files, scores, or blacklists, Heka’s AI processes large volumes of web data to produce structured outputs like fraud indicators, updated contact details, and contextual risk signals. In one recent deployment with a global payment processor, Heka’s AI engine caught 65% of account takeover losses without disrupting healthy user activity.

Heka is already generating millions in revenue through partnerships with banks, payment processors, and pension funds. Clients use Heka’s intelligence to support critical decisions from fraud mitigation to account management and recovery. The $14 million Series A round, led by Windare Ventures with participation by Barclays, Cornèr Banca, and other institutional investors, will accelerate Heka’s U.S. expansion and deepen its footprint across the UK and Europe.

“Heka’s offering stood out for its ability to address a critical need in financial services – helping institutions make faster, smarter decisions using trustworthy external data. We’re proud to support their continued growth as they scale in the U.S.” said Kester Keating, Head of US Principal Investments at Barclays.

Ori Ashkenazi, Managing Partner at Windare Ventures, added: “Identity isn’t a fixed file anymore. It’s a stream of behavior. Heka does what most AI can’t: it actually works in the wild, delivering signals banks can use seamlessly in workflows.”

Heka was founded by Rafael Berber, former Global Head of Equity Trading at Merrill Lynch; Ishay Horowitz, a senior officer in the Israeli intelligence community; and Idan Bar-Dov, a fintech and high-tech lawyer. The broader team includes intel analysts, data scientists, and domain experts in fraud, credit, and compliance.

“The credit bureaus were built for another era. Today, both consumers and risk live online. Heka’s mission is to be the default source of truth for this new digital reality – always-on, accurate, and explainable.” said Idan Bar-Dov, the Co-founder and CEO of Heka.

‍

About Heka
Heka delivers web intelligence to financial services. Its AI engine is used by banks, payment processors, and pension funds to fill critical blind spots in fraud mitigation, credit-decision, and account recovery. The company was founded in 2021 and is headquartered in New York and Tel Aviv. 

Press contact
Joy Phua Katsovich, VP Marketing | joy@hekaglobal.com