Fake accounts are proliferating at an unprecedented rate. With AI, bad actors can generate thousands of synthetic identities, bot-driven profiles, and coordinated fake accounts in minutes—exploiting promotions, distorting engagement metrics, and overwhelming platforms before detection can respond. Traditional detection tools often lag behind, allowing these malicious accounts to slip through unnoticed. The result? Increased hosting and marketing costs, fraudulent payouts, and financial losses from chargebacks.

Chalk understood our problem from day one. Our first call was with a sales engineer who had deep technical knowledge—it felt clear and technically sound. Their docs were excellent and completely open. And they even got us up and running on GCP that weekend.

Niel Ketkar Co-Founder, Verisoul

Verisoul specializes in stopping the most advanced fake account bots before they cause harm. Serving as a centralized view for all fake account problems, Verisoul provides businesses with immediate, high-confidence decisions. Its platform analyzes device signals, behavioral patterns, biometric anomalies, and network-level risks in real time—stopping take-downs before they cause damage.

But fake accounts are a moving target. Today’s threats include large-scale fake account creation, identity farming, and sophisticated multi-accounting techniques that evade traditional risk tools. Staying ahead requires more than accuracy—it demands speed. To meet this challenge, Verisoul turned to Chalk’s real-time feature platform.

With Chalk, Verisoul can:

• Ship detection updates 10x faster, reducing model iteration from days to hours.
• Process risk signaling data that’s only available at inference time, removing complex ETL pipelines and stale data.
• Ensure feature consistency and auditability, keeping fraud models explainable and reliable.

With Chalk, Verisoul stops fake account abuse, identity manipulation, and automated attacks before they happen—without compromising speed, accuracy, or transparency.

Verisoul’s fintech, gaming, SaaS, and market research customers face relentless attacks. The challenge isn’t just identifying fake accounts—it’s recognizing the evolving ways attacks manifest across industries:

• Bot-driven fake account creation, flooding platforms with inauthentic users.
• Location obfuscation through proxy networks and VPNs to bypass security.
• Synthetic identities blending real and fake data to create seemingly legitimate profiles.
• Multi-accounting and identity farming manipulating promotions, reviews, and credit systems.
• Fraudulent engagement and data pollution distorting analytics and inflating KPIs.

These threats don’t just pose financial risks—they erode user trust, damage platform integrity, and create long-term reputational harm.

Before implementing Chalk, Verisoul relied on manual, in-house feature engineering pipelines, requiring Python notebooks and BigQuery queries to be manually converted into API logic.

This led to three major bottlenecks:

• Data staleness – Data for decisions needs to be fresh, but the existing infrastructure, built for batch or streaming data, couldn’t support real-time feature computation.
• Slow signal iteration – Managing multiple feature pipelines across research, training, and production environments made testing and deploying new models slow and inefficient. Even minor updates required full dataset recomputation.
• Lack of auditability – Customers need transparency on fraud decisions, but engineering teams had to manually pull and piece together computed feature tables, slowing response times and debugging efforts.

Decisions Based on Inference-Time Data

By leveraging Chalk, Verisoul has transformed its decision models into a true real-time system. Instead of relying on outdated or precomputed risk signals, they now make decisions using fresh, continuously updated data—ensuring the most accurate fraud detection possible.

Even a 100-millisecond delay in data freshness affects fraud detection accuracy… Real-time processing makes us up to 4x better than traditional IP-based fraud lists.

Niel Ketkar Co-Founder, Verisoul

Instant Fraud Detection Iteration

Risk detection requires analyzing vast amounts of nuanced signals, from behavioral patterns to network data. Chalk brings all these elements together in a unified system, enabling Verisoul to easily manage and refine their models. Instead of juggling fragmented datasets and manual updates, their team can now iterate on detection strategies within a single, centralized framework—enhancing accuracy and scalability.

Query planner snapshot: Verisoul's sophisticated approach to decisioning leverages expressions, real-time database reads, complex joins, Python-based feature engineering, and more.

Verisoul’s engineers can now instantly recompute and validate new fraud signals using unified feature pipelines, allowing them to deploy updates 10x faster and stay ahead of evolving threats.

Our product provides a decision—fake, suspicious, or real—but behind that, we analyze multiple sub-scores and thousands of features. Chalk brings all these pieces together in one place, allowing us to easily recompute and refine them, which is massively valuable.

Niel Ketkar Co-Founder, Verisoul

Balancing Speed with Auditability

With Chalk, Verisoul gained full transparency into every signal. Every decision is now tied to auditable feature logs, making it painless to provide customers with clear, actionable insights into why an account was flagged.

We had partially computed features saved in tables that took time and effort to pull for customers—now we have auditability for every signal in real-time with Chalk.

Raine Scott Co-Founder, Verisoul

Since implementing Chalk, Verisoul has significantly improved detection speed, accuracy, and scalability.

Fake account tactics evolve daily—Verisoul moves faster. With Chalk as its feature engineering backbone, Verisoul has built an industry-leading real-time fake account detection engine that processes thousands of decisions per second using inference-time data.

The next phase? Verisoul is working on introducing a new paradigm into fraud detection—enabling every customer to customize the intelligence and decisioning to their specific definition of a fake user. With Chalk, Verisoul can dynamically fine tune the underlying data and models to optimize the accuracy for each customer’s use case.

By combining speed, precision, and adaptability, Verisoul continues to set the standard for real-time fake account prevention.

Our competitive edge hinges on the speed at which we analyze risk patterns, test new detection features, and deploy updates. With Chalk, our iteration speed went from days to hours.

Raine Scott Co-Founder, Verisoul