[机器学习|理论&实践]​ 机器学习在财务欺诈检测中的应用

import pandas as pd
import numpy as np

# 加载财务交易数据，这里使用模拟数据
data = pd.read_csv('financial_transactions.csv')

# 查看数据的前几行
print(data.head())

# 查看数据的基本信息
print(data.info())

# 统计缺失值
print(data.isnull().sum())

# 选择特征，这里简单演示，实际需要根据数据情况进行选择
selected_features = ['amount', 'frequency', 'location', 'time']

# 提取选择的特征
X = data[selected_features]

# 提取标签，假设欺诈用1表示，正常用0表示
y = data['fraud_label']

# 处理缺失值，可以采用填充或删除的方式
X = X.fillna(X.mean())

# 处理异常值，可以使用统计方法或者箱线图等方式识别和处理
# 这里简单演示删除金额为负值的异常数据
X = X[X['amount'] > 0]
y = y[X.index]

from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, classification_report

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 选择随机森林模型
model = RandomForestClassifier()

# 训练模型
model.fit(X_train, y_train)

# 在测试集上进行预测
y_pred = model.predict(X_test)

# 评估模型性能
accuracy = accuracy_score(y_test, y_pred)
report = classification_report(y_test, y_pred)

print(f'模型准确率: {accuracy}')
print(f'分类报告:\n{report}')

# 使用 Python 中的 scikit-learn 库进行模型构建

from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, confusion_matrix

# 假设 data 包含交易数据，is_fraud 列为目标变量
data = pd.read_csv("transaction_data.csv")

# 特征选择
features = data[['TransactionAmount', 'Frequency', 'Location']]

# 目标变量
target = data['is_fraud']

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)

# 使用随机森林进行训练
model = RandomForestClassifier()
model.fit(X_train, y_train)

# 在测试集上进行预测
y_pred = model.predict(X_test)

# 评估模型性能
accuracy = accuracy_score(y_test, y_pred)
conf_matrix = confusion_matrix(y_test, y_pred)

print(f"准确率：{accuracy}")
print(f"混淆矩阵：{conf_matrix}")

# 假设 data 包含员工行为数据，is_internal_fraud 列为目标变量
employee_data = pd.read_csv("employee_data.csv")

# 特征选择
employee_features = employee_data[['ActivityFrequency', 'RecordModification', 'AccessLevel']]

# 目标变量
employee_target = employee_data['is_internal_fraud']

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(employee_features, employee_target, test_size=0.2, random_state=42)

# 使用逻辑回归进行训练
employee_model = LogisticRegression()
employee_model.fit(X_train, y_train)

# 在测试集上进行预测
employee_pred = employee_model.predict(X_test)

# 评估模型性能
accuracy = accuracy_score(y_test, employee_pred)
conf_matrix = confusion_matrix(y_test, employee_pred)

print(f"准确率：{accuracy}")
print(f"混淆矩阵：{conf_matrix}")