Kestrel_
$47M
Fraud recovered
In year one
<50ms
Scoring latency
P99
-38%
False positives
Vs prior rules engine
5.6x
First-year ROI
Payback in 4 months
The challenge
RULES HIT THE WALL
Kestrel's legacy rules engine had drifted past 14,000 conditions; every extra false positive cost the bank a customer relationship. Card fraud patterns were evolving faster than static logic, leaving an 18-month gap between new attacks and rule updates.
The board wanted an AI for banking approach that could adapt continuously, support AI fraud detection banking at scale, and still produce audit-ready reasoning for regulators. Most off-the-shelf finance AI platforms could not meet that bar.
14,000
Stale rules
72%
False-positive rate
18 mo
Rules lag
The approach
FOUR PHASES
We rebuilt fraud as an enterprise-grade AI integration for banks: laying a data foundation, training and validating models, executing a phased production rollout, then operating and auditing the system as a living finance AI platform. Each phase delivered usable AI solutions for finance while de-risking the next.
Phase 1
Wks 1–6Data Foundation
We laid the enterprise data foundation for AI integration in banking by unifying channels, defining stable contracts, and engineering model-ready fraud behavior features.
Built a real-time feature store on Snowflake with 4 years of transaction history
Ingested cards, ACH, wires, and digital channels into one AI for banking data layer
Engineered 220 fraud and behavior features for downstream models
Streaming CDC pipelines with data contracts to keep the platform regulator-safe
Phase 2
Wks 6–12Model Development
The modeling phase combined strong fraud detection performance with explainability, stability testing, and controlled challenger rollout discipline.
Gradient-boosted ensemble plus deep sequence model for spending patterns
Champion/challenger setup with holdout segments across products and regions
SHAP-based explainability wired into dashboards for risk and compliance
Bias and stability audits across portfolios before full AI fraud detection banking rollout
Phase 3
Wks 12–20Production Deploy
We moved from validation to live serving with latency SLOs, staged exposure controls, and operational readiness across both teams.
Kafka-driven model serving at <50ms P99 latency for every transaction
Shadow mode against rules engine for 4 weeks to validate AI for banking performance
Phased cutover by card portfolio and geography to manage exposure
Runbook, SLOs, and on-call for joint alien.fi + bank operations teams
Phase 4
Wks 20–44Operate & Audit
After go-live, Kestrel ran fraud AI as a governed system with continuous retraining, compliance checkpoints, and managed support.
Weekly retraining pipeline with automated drift and data-quality checks
Monthly AI integration for banks review with risk, fraud, and compliance stakeholders
Quarterly regulator-ready audit pack generated from model and feature history
24/7 managed support from alien.fi to keep the finance AI platform healthy
"
The honest version: alien.fi was the third firm we tried. They were the first to actually ship an AI for banking model we could explain to regulators. The recovered fraud paid for the engagement four times over.
MN
Mark Novak
EVP of Risk, Kestrel Bank
More like this
RELATED STUDIES