Financial Fraud Detection: Imbalanced Classification

Module: Fintech AI | Difficulty: Advanced

Class Imbalance

Cost-Sensitive Learning

where .

SMOTE

Real-Time Detection

Evaluation

import numpy as np
from sklearn.ensemble import IsolationForest

class FraudDetector:
    def __init__(self, contamination=0.01):
        self.model = IsolationForest(contamination=contamination)
    def fit(self, X):
        self.model.fit(X)
    def predict(self, X):
        scores = self.model.decision_function(X)
        return (scores < 0).astype(int)
    def real_time_score(self, features, threshold=0.5):
        # Lightweight model for real-time
        score = self.fast_model.predict_proba(features.reshape(1,-1))[0,1]
        return {'fraud_prob': score, 'flagged': score > threshold}

Research Insight: Real-time fraud detection requires sub-100ms latency. Two-stage approaches use a lightweight model for initial screening and a complex model for flagged transactions. Graph neural networks capture fraud patterns in transaction networks.