Most fraud vendors are focused on the detection layer, competing on model accuracy.

I've been tracking a pattern in my research that reveals a growing gap: while detection systems get attention, the real expertise lies with the investigation and operations teams. They are tasked with untangling sophisticated schemes and finding nuanced patterns, a job for which they are increasingly unsupported, despite being the function's most critical asset.

However, these experts spend the majority of their day on manual data collection and administrative work, leaving little time for the complex analysis and adjudication where they create the most value. Furthermore, translating the unique patterns and intuitions these teams develop into the detection layer is a slow and difficult process. This creates a severe operational drag that makes it impossible to scale effectively.

Enter Fravity, a 2024 Austin startup that is changing this paradigm.

Instead of focusing on the detection layer, Fravity is transforming the investigations and operations layer. Their approach streamlines and automates investigative workflows to enable better, faster human decisions. By supercharging experts with AI agents, they empower teams to handle more complex cases with significantly greater speed and depth.

Based on my research into their approach and early customer results, they might be onto something significant.

From my research across fraud teams at banks, fintechs, and marketplaces, a clear pattern emerges.

Walk into any financial institution's fraud operations center and you'll see the same scene - talented analysts clicking through case management systems, copying data between screens, and spending 80% of their time on administrative tasks instead of actual investigation work. Meanwhile, sophisticated financial criminals are scaling their attacks faster than your team can respond.

Traditionally people will just tell you, "Hire more analysts."

But there's a fundamental problem with this approach. Operations analysts are reviewers, not builders. They're experts at investigating suspicious activity, not at building the automated workflows that could amplify their capabilities.

This creates a devastating productivity trap.

The more sophisticated your fraud prevention becomes, the more complex cases you generate. The more complex cases you have, the more analyst time you need. It's a linear scaling problem in a world where threats grow exponentially.

The analyst workflow reality:

70-80% data gathering and administrative work, 20-30% actual investigation and decision-making.

Every improvement in fraud detection accuracy generates more nuanced cases that require human review. Success in detection creates failure in operations.

As there are many teams racing for complete automation "Let's automate fraud detection completely with better AI/ML models." Fravity believes, "FRAML shouldn’t or rather couldn't be 100% automated at detection layer, given the dynamic nature of an adversarial setup . But we can transform how analysts work within that reality."

Complex financial crime requires contextual human judgment that pure automation can't replicate - understanding the nuances of customer behavior, interpreting regulatory requirements in edge cases, and connecting subtle patterns across seemingly unrelated activities.

But current workflows waste that valuable human judgment on data gathering and administrative tasks rather than the high-value analysis where domain expertise actually matters. The result is that your most skilled investigators spend their time copying information between systems instead of investigating sophisticated schemes.

Instead of replacing analysts, transform how they work.

Build AI copilots that handle data correlation and workflow automation so analysts can focus on investigation and decision-making.

“Operations analysts are reviewers, not builders." Stop asking them to create complex workflows. Give them pre-built AI agents that understand fraud operations and automate the repetitive components.

A significant value of Fravity's approach is its solution architecture. With their browser use agent, their copilot that can see and do everything a human investigator can on their existing tool kit, case manager. Teams can layer the power of Agentic AI on their current workflows and start using it right away. No historical data needed, not long development cycles needed for backend integrations.

Ops teams dont need to learn a new tool or change any of their existing workflow. Fravity deliver this "instant modernization" by giving you an agentic ai workflow whether you are using Nice Actimize, SAS, ComplyAdvantage, a homegrown system, or any combination of these tools.

Traditional fraud operations flow:

Fravity-enhanced flow:

Platform components from vendor submission:

Data sources - User/transaction/business 360, third-party intelligence data, OSINT

Workflows: Standard operating procedure, Policy documentation,

Learning: Agents come pretuned but learn from customers historical data and on job human in the loop decisions

Implementation - API-based deployment, 2-week time to value

Integration targets - Most case management tools, enterprise event streams/APIs and data warehouses

Recent implementation data from their customer base shows operational transformation.

A cross-border payments company with complex manual workflows across AML compliance, transaction monitoring, and dispute resolution. Analysts spending majority of time on data gathering rather than investigation.

Fravity deployed and operationalized 3 complex workflows with multi-agentic AI systems handling data correlation and preliminary analysis.

Per vendor, "massive wins on their top KPIs" including reduced fraud losses through faster, more thorough investigations, lower customer friction with reduced time in review bins, and decreased operational expenses through analyst productivity gains.

Analysts went from spending 80% of time on administrative tasks to 80% of time on actual investigation and decision-making.

Customer profile from my research - Banks, credit unions, fintechs, merchants, and marketplaces. Common characteristics - operational scaling challenges rather than detection accuracy problems. Decision makers include Fraud, Payments, Trust & Safety, and Product teams.

Fravity's emergence reveals several critical strategic considerations for fraud and AML leaders. Your detection accuracy might be excellent, but if your operations can't scale with case complexity, you're building a house of cards.

The most sophisticated fraud prevention in the world is useless if analysts can't act on it efficiently.

The human factor compounds this challenge. Burned-out analysts make poor decisions. Analysts spending time on data entry instead of investigation miss important patterns. Investing in analyst productivity isn't just an HR issue.

It's a risk management imperative.

Most financial institutions are investing heavily in AI for fraud detection. Few are investing in AI for fraud operations. This creates a massive opportunity for organizations that prioritize analyst productivity alongside detection accuracy.

Traditional case management vendors (Nice Actimize, BAE Systems, SAS) focus on workflow management but lack AI-powered analyst augmentation.

Detection vendors (DataVisor, Feedzai, etc.) optimize for accuracy metrics but don't address operational scalability.

Fravity's positioning - The intersection of operational AI and fraud domain expertise. They're not competing on detection sophistication. They're competing on operational effectiveness.

Market timing factors:

Short-term market evolution - Based on current vendor development patterns and customer feedback, operational AI may become a standard evaluation criterion for fraud technology procurement.

Medium-term transformation - AI copilots may become standard in fraud operations, similar to how automated detection became widespread.

Strategic question for fraud leaders - Consider whether detection accuracy or operational efficiency is the primary constraint on your team's effectiveness.

Fravity represents a strategic market shift from detection-focused to operations-focused fraud technology. Their positioning challenges the assumption that better algorithms automatically translate to better outcomes.

For fraud and AML leaders - If your analysts are spending more time gathering data than analyzing it, you have an operations problem that detection accuracy improvements won't solve.

Investment thesis - Organizations that prioritize operational efficiency alongside detection accuracy will have significant competitive advantages in both cost structure and response capability.

Market timing - Early adoption window for operational AI is open now. The organizations that figure this out first will set new performance baselines that others will struggle to match.

Based on my research, Fravity is addressing a real operational constraint that most fraud technology vendors are ignoring. Whether their specific approach becomes the standard or gets acquired by a larger platform, the operational efficiency problem they're solving isn't going away.

What's the biggest operational bottleneck your fraud or AML team is facing? Hit reply and let me know where your analysts spend most of their time.

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