利用机器学习实现实时交易欺诈检测

以下是一个基于Python的银行反欺诈AI应用示例代码，演示如何利用机器学习实现实时交易欺诈检测。该示例使用LightGBM算法训练模型，并通过Flask框架构建实时检测API：

python

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from lightgbm import LGBMClassifier
import joblib
from flask import Flask, request, jsonify

# 1. 加载示例数据（需替换为真实交易数据集）
data = pd.read_csv("bank_transactions.csv")
# 数据包含字段：transaction_id, amount, time, ip_address, device_type, location, is_fraud

# 2. 数据预处理与特征工程
def preprocess_data(df):
    # 处理时间特征
    df['hour'] = df['time'].dt.hour
    df['day_of_week'] = df['time'].dt.dayofweek
    
    # 设备指纹特征
    device_history = pd.read_csv("device_history.csv")  # 历史设备使用记录
    device_fraud_score = device_history.groupby('device_id')['is_fraud'].mean().reset_index()
    df = df.merge(device_fraud_score, on='device_id', how='left')
    df['device_fraud_score'] = df['device_fraud_score'].fillna(0)
    
    # 地理位置风险评分
    location_risk = pd.read_csv("location_risk.csv")
    df = df.merge(location_risk, on='location', how='left')
    df['location_risk'] = df['location_risk'].fillna(0)
    
    # 交易频率统计（窗口统计需时序处理&