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Project 06AdvancedFinance7-10 focused hoursFraud-evaluation strategy case study
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Fraud Detection Case Study

Design an evaluation strategy for a highly imbalanced fraud detection problem without relying on misleading headline metrics.

Class ImbalanceClassificationPrecisionRecallF1Thresholds
FRAME→DATA→ANALYZE→MODEL→EVALUATE→COMMUNICATE

BUSINESS CONTEXT

Why this project matters

A payments team needs a fraud-detection workflow that catches costly fraudulent transactions without overwhelming investigators or blocking too many legitimate customers. The main challenge is not just modeling, but choosing the right evaluation and operating threshold.

PROJECT OBJECTIVE

What you are expected to accomplish

Audit an imbalanced fraud dataset, compare evaluation strategies, and design a threshold-selection approach that reflects fraud costs and review capacity.

DATASET OVERVIEW

Expected dataset structure

Expect transaction-level data containing merchant, customer, amount, geography, time, and fraud-label information, with fraud cases representing only a small fraction of records.

Main entities / rows

One row per transaction

Target variable

is_fraud (Yes/No)

transaction_id

string

Unique identifier for each transaction.

transaction_amount

float

Payment size, often highly skewed.

merchant_category

categorical

Merchant grouping that may correlate with fraud risk.

device_type

categorical

Customer device or platform metadata.

transaction_hour

integer

Hour of day or time-based fraud signal.

country

categorical

Customer or transaction geography.

prior_chargeback_count

integer

Historical behavior signal if available at scoring time.

is_fraud

binary

Rare target indicating confirmed fraud.

Data quality issues to expect

  • Fraud prevalence is low, so class imbalance is a central issue rather than a side note.
  • Some historical-risk features may leak future information if they are not time-safe.
  • Transaction timestamps, merchant labels, and extreme values may need careful validation.

QUESTIONS TO ANSWER

Focus the work around meaningful decisions

  1. 01

    How imbalanced is the fraud problem, and why does that make accuracy unreliable?

  2. 02

    What precision-recall tradeoff would suit the business best?

  3. 03

    How should threshold selection account for analyst review capacity?

  4. 04

    Which features appear useful, and which might be leakage-prone?

  5. 05

    What evaluation summary would a payments stakeholder actually trust?

PROJECT ROADMAP

Move through the case study in a practical sequence

  1. 01

    Step 01

    Clarify the fraud decision

    Define what action follows a positive prediction and what each error costs.

  2. 02

    Step 02

    Audit the dataset and target

    Measure fraud prevalence, inspect feature timing, and confirm the transaction grain.

  3. 03

    Step 03

    Clean with deployment realism

    Handle anomalies, missing history, and labels while keeping inference-time constraints in mind.

  4. 04

    Step 04

    Compare baseline strategies

    Establish how naive models behave before optimizing anything complex.

  5. 05

    Step 05

    Evaluate with the right metrics

    Inspect confusion matrices, precision, recall, F1, and precision-recall behavior across thresholds.

  6. 06

    Step 06

    Choose an operating threshold

    Balance fraud capture, customer friction, and analyst capacity.

  7. 07

    Step 07

    Present a fraud-evaluation policy

    Summarize the chosen threshold, assumptions, and monitoring recommendations.

TASKS / MILESTONES

Concrete work to complete

  • Calculate fraud prevalence and explain why a naive classifier can appear successful.
  • Inspect features for time-of-availability issues before modeling.
  • Create baseline metrics at multiple thresholds instead of only using the default threshold.
  • Compare confusion matrices and precision-recall behavior across candidate operating points.
  • Estimate how many alerts per day each threshold would generate.
  • Write a threshold recommendation tied to business tradeoffs and analyst capacity.
  • Document what should be monitored after deployment.

SUGGESTED VISUALIZATIONS

Visuals worth creating

  • Class-distribution chart
  • Confusion matrix at chosen threshold
  • Precision-recall curve
  • Threshold versus alerts and fraud-capture plot
  • Fraud-rate comparison by merchant or transaction segment

DELIVERABLES

What the learner should produce

  • Fraud dataset audit
  • Evaluation notebook or decision memo
  • Threshold comparison tables or charts
  • Business-focused threshold recommendation
  • Monitoring and follow-up considerations

SUCCESS CRITERIA

What strong completion looks like

  • The project explains why imbalance changes how success should be measured.
  • Threshold selection is tied to capacity and cost, not habit.
  • Potential leakage or unrealistic features are identified explicitly.
  • The final recommendation is specific enough to guide a real fraud workflow.

GUIDANCE

Support when you need a nudge

Reveal practical hints, checkpoints, and framing help without exposing a full finished solution.

Complete this project when the work is yours

Use this only after you have worked through the roadmap, milestones, and your own analysis.

KEY LEARNING OUTCOMES

Skills reinforced by this project

  • Evaluate rare-event classifiers using fraud-appropriate metrics.
  • Design threshold strategies around real operational constraints.
  • Audit fraud features for leakage and deployment realism.
  • Translate model performance into a decision policy.

RELATED LESSONS

Revisit published lessons that support this build

Published lesson

Why Accuracy Is Not Enough

Open lesson→

Published lesson

How to Answer Metric Questions in Interviews

Open lesson→
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