Fraud Detection — Complete Guide
Fraud Detection — Complete Guide: free step-by-step lesson with examples, common mistakes, and interview tips — part of ML.NET Tutorial on Toolliyo Academy.
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ML.NET Tutorial · Lesson 35 of 100
Fraud Detection
Foundations ✓ → Models → NLP & advanced → MLOps
Models · 2 — Classify & regress · ~6 min · Module 4: Classification Models
What is this?
Fraud detection scores transactions with binary classification on amount, time, merchant, and device signals.
Why should you care?
AIPredict’s flagship module blocks risky payments in milliseconds.
See it live — copy this example
Use a .NET console or Web API project with Microsoft.ML. Run dotnet run after pasting.
public class Tx { public float Amount { get; set; } public float Hour { get; set; } public float MerchantRisk { get; set; } }
public class FraudPred { [ColumnName("PredictedLabel")] public bool IsFraud { get; set; } public float Probability { get; set; } }
var pipeline = ml.Transforms.Concatenate("Features", "Amount", "Hour", "MerchantRisk")
.Append(ml.BinaryClassification.Trainers.FastTree());What happened?
- Score in the payment API.
- Alert queue for mid scores.
- Never block solely on one brittle rule.
Practice next
- Define Tx/FraudPred.
- Train FastTree.
- Wire threshold to alert vs block.
- Add DeviceRisk.
- Shadow-mode log before blocking.
Remember
Tx features. FastTree score. Tiered actions.
AIPredict fraud scorer
Checkout calls PredictionEngine.
Outcome: High-risk txs alert; legit checkout flows.
Interview prep for this lesson
Practice these questions aloud after reading—each links to a full structured answer.
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