Fintech Leader FIS Built a Real-Time Identity and Fraud Detection Platform Powered by Chalk
Client
Use Case
Industry
Cloud
Challenges
- Compute hundreds of features per transaction in under 50ms at P99 latency for debit authorization
- Reduce fraud and false positives at massive global scale
- Maintain enterprise compliance while supporting billions of daily transactions
Solutions
- Compute-first features with sub-40ms at P99 latency
- Unified online + offline features with guaranteed correctness and consistency
- Ensured all data remained within FIS’s environment, maintaining compliance protocols
Overview
FIS is a Fortune 500 company and one of the largest payments processors in the world, powering nearly a quarter of all U.S. debit transactions. It powers core banking, payments, and risk management systems for thousands of financial institutions. Within FIS, the Financial Intelligence (Fintel) team was created to build next-generation identity and fraud detection systems that protect every transaction processed across debit, credit, and ACH.
Debit card authorization sits at the center of global payments. It is one of the most complex, latency-sensitive, and high-stakes workloads in fintech. Every authorization must be evaluated in milliseconds, across billions of transactions, with zero margin for error.
The Challenge
The debit card authorization flow represents the holy grail of real-time decisioning. Fraud models must compute hundreds of behavioral features and return a decision instantly, at the moment a customer taps their card or completes a purchase.
Legacy fraud platforms relied on static rules, models, and batch pipelines that could not adapt quickly to emerging fraud patterns or the scale of FIS’s transaction volume. The Fintel team set out to build a modern, ML-driven platform capable of operating at global scale while continuously learning from new behaviors and maintaining enterprise-grade reliability.
They needed infrastructure that could meet demanding business, technical, and compliance requirements.
Requirements:
- Reduce fraud losses and false positives for issuers
- Maintain frictionless customer approvals and minimize false declines
- Compute hundreds of real-time features per transaction
- Deliver sub-50ms latency at P99 to ensure instant authorization decisions
- Scale across FIS’s global transaction volume, handling billions of events per day
- Maintain compliance and data control
The Solution
FIS selected Chalk to power real-time feature computation for its debit authorization models.
Chalk’s compute-first architecture allowed FIS to define, validate, and serve features directly from existing data sources within their own environment and without data movement. The platform provided a unified framework for both online and offline feature computation, accelerating model development while maintaining compliance.
Capability
FIS use case
- Unified feature computation
- Enabled data scientists to define features once and reuse them across training and inference, ensuring consistency and reproducibility.
- Built-in validation and testing
- Ensured feature correctness through unit tests and eliminated online/offline skew.
- Materialized aggregates
- Enabled real-time rolling computations, such as “transactions in the past 24 hours,” at sub-40ms P99 latency.
- Offline data acceleration
- Generated training datasets in hours instead of weeks, improving experimentation velocity.
- In-VPC deployment
- Ensured all data remained within FIS’s AWS environment, maintaining compliance protocols.
Architecture
Chalk was deployed entirely within FIS’s AWS environment, running both the control and data planes inside the company’s EKS clusters.
This architecture integrated Chalk’s compute-first design with FIS’s existing infrastructure, allowing the team to achieve high throughput while maintaining full data residency and compliance.
Component
Implementation
- Performance
- Consistent sub-40ms P99 latency for feature computation across hundreds of features per transaction.
- Scale
- Thousands of authorizations per second supported with minimal compute overhead.
- Data Sources
- Features computed directly from Snowflake and Databricks.
- Models
- Multiple XGBoost models sharing consistent feature definitions
- Security
- All data and computation remained within FIS’s VPC.
Outcome
FIS went from proof of concept to production in just 12 weeks, meeting its goal of launching a fully operational, compliant, real-time authorization system faster than any prior internal effort.
With Chalk, the Fintel team built a fraud detection engine that continuously learns from new data, scales globally, and supports rapid experimentation without adding infrastructure complexity.
Metric
Before Chalk
After Chalk
- Model iteration speed
- Week-long data generation and training cycles
- 2 days or less iteration cycles, multiple models in parallel
- Operational efficiency
- Manual offline feature generation and validation
- Unified feature computation with automated validation
- Scalability
- Batch-oriented processing
- Real-time system supporting thousands of transactions per second
Why FIS Chose Chalk
FIS evaluated multiple feature store solutions, including Tecton and Fennel, but needed a system built for real-time performance and enterprise control. Chalk offered a compute-first approach to feature engineering, providing the functionality of a feature store with sub-40ms latency for hundreds of features and full deployment inside FIS’s environment.
- Engineering partnership: Chalk’s engineering team partnered directly with FIS from pilot to production.
- Collaborative support: Fast, hands-on communication replaced traditional vendor layers. Questions were resolved in minutes, not days.
- Problem-solving mindset: Chalk adapted quickly to complex enterprise constraints, helping FIS meet aggressive goals without tradeoffs in compliance or scale.
Key Takeaway
By adopting Chalk’s compute-first architecture, FIS proved that global-scale fraud prevention and enterprise compliance can coexist in real-time systems. The Fintel team runs a platform that adapts instantly to new fraud patterns, minimizes false positives, and delivers faster, more reliable decisions across billions of transactions each day.