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Fraud Detection System - Real-Time Transaction Scoring

SYSTEM-DESIGNhardInterview freq: very_high
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🤖
Fraud Detection System - Real-Time Transaction Scoring
Detect fraudulent transactions in real-time to minimize financial losses.
system-designhardFreq: very_highTrust & Safety
Prerequisites:Handling Class ImbalanceAnomaly DetectionGraph Neural Networks
🎯
Problem Framing
Business Objective

Minimize financial losses due to fraudulent transactions.

ML Objective

Binary classification to predict the probability of a transaction being fraudulent.

Constraints
  • P99 latency < 50ms for transaction scoring
  • High class imbalance (fraudulent transactions < 1%)
  • Regulatory compliance for data privacy
  • Cold start problem with new users
Clarifying Questions
  • What is the acceptable false positive rate?
  • How often do we receive new transaction data?
  • What are the regulatory requirements for data handling?
  • What is the expected volume of transactions per second?
  • How do we handle transactions from new users with no history?
Out of Scope
✗ Post-fraud investigation processes
✗ User notification systems
✗ Non-real-time fraud detection methods
📊
Offline Metrics
PR-AUC because it is more informative than ROC-AUC for imbalanced datasets
F1-Score to balance precision and recall
📈
Online Metrics
Fraud loss saved per day
False positive rate in production
⚖️
Trade-offs

High precision may reduce false positives but increase false negatives, potentially missing fraud.

💼
Business Metric

Total fraud loss saved as a percentage of total transaction volume

🗄️
Data Sources
  • Transaction logs (real-time, structured)
  • User account details (daily batch, structured)
  • Historical fraud reports (weekly batch, structured)
🏷️
Data Properties
Labeling Strategy

Programmatic labeling using historical fraud reports to label past transactions as fraudulent or not.

Data Volume

Approximately 10 million transactions per day, 1 TB/month

Freshness

Features must be updated in real-time to reflect the latest transaction data.

⚠️
Data Challenges
  • Sparse labels due to low fraud rate
  • Cold start for new users with no transaction history
  • Potential data privacy issues with user data
  • Distribution shift as fraud patterns evolve
🔧
Key Features
numerical
Transaction Amount
Unusually high amounts can indicate fraud.
numerical
Transaction Time
Fraudulent transactions often occur at unusual times.
numerical
User Transaction Frequency
Sudden spikes in activity can be suspicious.
categorical
Merchant Category
Certain categories may have higher fraud rates.
categorical
Device ID
New or unusual devices can be a fraud indicator.
⚡
Feature Engineering Notes
Real-time vs Batch

Transaction Amount and Time need real-time computation, while User Transaction Frequency can be pre-computed.

Feature Store

Required

🐍
Feature Snippet
Python
def compute_transaction_features(transaction):
    features = {}
    features['amount'] = transaction['amount']
    features['time'] = transaction['timestamp'].hour
    features['device_id'] = transaction['device_id']
    return features
🤖
Model Selection
Baseline

Logistic Regression due to its simplicity and interpretability.

Proposed Model

Graph Neural Network (GNN) to capture complex relationships between users and transactions.

GNNs can model the interactions between users and transactions effectively, capturing patterns that simpler models miss.
✗
Alternatives Rejected
Random Forest: Rejected due to high latency in real-time scoring.
XGBoost: Rejected because it struggles with capturing complex user-transaction interactions without extensive feature engineering.
🏋️
Training Strategy

Offline training with periodic updates to incorporate new fraud patterns.

🚀
Serving Infrastructure
LatencyP99 < 50ms end-to-end
Throughput10,000 QPS
ArchitectureSingle-stage scoring to minimize latency, using a pre-trained GNN model.
CachingCache recent transaction scores with a TTL of 5 minutes to reduce redundant computations.
FallbackRule-based system using historical fraud patterns if the model is unavailable.
📡
Data Drift Signals
Shift in transaction amount distribution
Change in user transaction frequency patterns
📉
Model Drift Signals
Decrease in PR-AUC over time
Increase in false positive rate
🔔
Alerting & Retraining
Retraining Trigger

PR-AUC drops below 0.7 or false positive rate exceeds 5%

Alerting Strategy

On-call engineer paged if model drift detected, auto-retrain initiated if thresholds exceeded.

🎯
Interview Angles
  • How do you handle class imbalance in fraud detection?
  • What are the trade-offs between precision and recall in this system?
  • How do you ensure real-time performance under high load?
⚠️
Common Mistakes
Ignoring the impact of class imbalance on model performance
Overlooking the need for real-time feature computation
Failing to consider regulatory constraints in data handling
Choosing models that cannot meet latency requirements
📌
Key Decisions
  • Choosing a GNN for its ability to model complex interactions
  • Implementing a caching strategy to meet latency requirements
  • Defining clear retraining triggers based on model performance metrics

Practice

(1/5)
1. In a spam detection system, which feature is most likely to be effective in identifying spam emails?
easy
A. Email length
B. Presence of certain keywords
C. Sender's email domain
D. Time of day email was sent

Solution

  1. Step 1: Identify effective features for spam detection

    Keywords like 'free', 'win', or 'prize' are often used in spam emails.

  2. Step 2: Eliminate less effective features

    Email length (A) and time of day (D) are not strong indicators of spam. Sender's domain (C) can be spoofed.

  3. Final Answer:

    Option B -> Option B

  4. Quick Check:

    Keywords often signal spam. [OK]
Hint: Keywords often signal spam. [OK]
Common Mistakes:
2. In a content-based filtering system, what is a likely failure mode if item embeddings are not updated frequently?
medium
A. Increased computational cost
B. Decreased model interpretability
C. Stale recommendations
D. Overfitting to recent trends

Solution

  1. Step 1: Consider the impact of infrequent updates

    Without frequent updates, item embeddings may not reflect recent changes, leading to stale recommendations.

  2. Step 2: Eliminate wrong options

    A: Computational cost is not directly affected by update frequency. B: Interpretability is unrelated to update frequency. D: Overfitting is more about model complexity than update frequency.

  3. Final Answer:

    Option C -> Option C

  4. Quick Check:

    Infrequent updates lead to stale recommendations. [OK]
Hint: Infrequent updates lead to stale recommendations. [OK]
Common Mistakes:
3. When deploying a fine-tuning pipeline at scale, should you prioritize using RLHF or DPO for optimizing model performance, and why?
medium
A. Prioritize RLHF because it directly improves model accuracy.
B. Prioritize DPO because it simplifies the model architecture.
C. Prioritize RLHF because it reduces computational costs.
D. Prioritize DPO because it optimizes data quality, which is foundational.

Solution

  1. Step 1: Understand the roles of RLHF and DPO

    RLHF aligns outputs with human preferences, while DPO ensures high-quality data input, which is foundational for any model training.

  2. Step 2: Eliminate wrong options

    A: RLHF improves alignment, not accuracy directly. C: RLHF does not reduce computational costs. D: DPO does not simplify architecture but improves data quality.

  3. Final Answer:

    Option D -> Option D

  4. Quick Check:

    Data quality is foundational; prioritize DPO. [OK]
Hint: Data quality is foundational; prioritize DPO. [OK]
Common Mistakes:
4. A hate speech detection model is suddenly showing a high false negative rate. What is the most likely cause?
medium
A. The training data is imbalanced with more negative samples.
B. The model's learning rate is too high.
C. The model is overfitting the training data.
D. The feature extraction process is incorrect.

Solution

  1. Step 1: Analyze the symptom

    A high false negative rate often indicates that the model is not sensitive enough to positive samples.

  2. Step 2: Eliminate wrong options

    Overfitting would increase false positives, a high learning rate causes instability, and incorrect feature extraction would affect all predictions.

  3. Final Answer:

    Option A -> Option A

  4. Quick Check:

    High false negatives = Imbalanced data [OK]
Hint: High false negatives = Imbalanced data [OK]
Common Mistakes:
5. In a pairwise learning to rank system, what is a common failure mode if the model is not performing well?
medium
A. The model is overfitting due to too many features
B. The model is not capturing the relative order of item pairs
C. The model is not considering the entire list of items
D. The model is using an inappropriate loss function for regression

Solution

  1. Step 1: Identify the failure mode specific to pairwise ranking.

    Pairwise models can fail if they don't capture the relative order of item pairs.

  2. Step 2: Eliminate wrong options.

    Overfitting and inappropriate loss functions are generic issues, and pairwise doesn't consider the entire list, making C incorrect.

  3. Final Answer:

    Option B -> Option B

  4. Quick Check:

    Pairwise = focus on item pairs [OK]
Hint: Pairwise = focus on item pairs [OK]
Common Mistakes:
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