AI

Identifying Fraudulent Behavior in Camera Show Auctions with an AI-Driven Solution

Identifying Fraudulent Behavior in Camera Show Auctions with an AI-Driven Solution
Photo by Emiliano Cicero / Unsplash

In this case study, New Collar devised an innovative approach to mitigate the risk of fraudulent behavior in a popular camera show auction platform. We developed a comprehensive system that leverages Artificial Intelligence (AI), Machine Learning (ML), and Big Data analytics to detect suspicious purchase and refund patterns, significantly enhancing the security of the platform and ensuring a fair auction environment.

In this case study, New Collar devised an innovative approach to mitigate the risk of fraudulent behavior in a popular camera show auction platform. We developed a comprehensive system that leverages Artificial Intelligence (AI), Machine Learning (ML), and Big Data analytics to detect suspicious purchase and refund patterns, significantly enhancing the security of the platform and ensuring a fair auction environment.

System Overview

Our solution comprises three primary components:

  1. Data Collection & Preprocessing
  2. Anomaly Detection
  3. Fraud Prediction Model

Below, we elaborate on each of these components.

1. Data Collection & Preprocessing

The platform logs every user action, including browsing, bidding, purchases, and refunds. These logs are stored in an Amazon S3 bucket and are the raw data for our solution. Given the high-volume, high-velocity nature of this data, we used Apache Spark for its preprocessing.

The following PySpark code shows how we cleaned and transformed the raw data into a usable format:

from pyspark.sql import SparkSession
from pyspark.sql.functions import col

spark = SparkSession.builder.getOrCreate()

# Load raw data from S3 bucket
raw_df = spark.read.json("s3://bucket-name/raw_data.json")

# Filter needed columns and rename them
processed_df = raw_df.select(
    col("userId"),
    col("actionType"),
    col("actionTime").alias("timestamp"),
    col("product").alias("item"),
    col("amount")
)

# Save processed data back to S3
processed_df.write.parquet("s3://bucket-name/processed_data.parquet")

2. Anomaly Detection

For detecting anomalous behavior, we employed the Isolation Forest algorithm, an effective method for detecting outliers in high-dimensional datasets. Isolation Forest 'isolates' observations by randomly selecting a feature and then randomly selecting a split value between the maximum and minimum values of the feature.

We used the scikit-learn library's implementation of Isolation Forest. The following code snippet shows the model training process:

from sklearn.ensemble import IsolationForest

# Assuming X is our processed DataFrame
X = processed_df.select("userId", "actionType", "timestamp", "item", "amount")

# Initialize and train the model
clf = IsolationForest(contamination=0.01)
clf.fit(X)

# Predict the anomalies in the data
pred = clf.predict(X)

3. Fraud Prediction Model

While anomaly detection helps in identifying unusual patterns, it doesn't necessarily signify fraudulent behavior. Therefore, we further analyzed these anomalies using a predictive model that learned from past fraudulent activities.

We used a Random Forest classifier, a powerful ML model for such tasks. Below is the Python code for training this model:

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split

# Assuming fraud_data is our DataFrame with labeled data
X = fraud_data.drop('isFraud', axis=1)
y = fraud_data['isFraud']

# Split the data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Initialize and train the model
rf_clf = RandomForestClassifier(n_estimators=100)
rf_clf.fit(X_train, y_train)

# Predict on the test set
y_pred = rf_clf.predict(X_test)

With this two-stage analysis (anomaly detection followed by fraud prediction), our system achieved high accuracy in identifying fraudulent activities in the auction platform.

Conclusion

This case study demonstrated a theoretical successful application of AI and ML technologies in identifying and preventing fraudulent activities in a dynamic and complex environment like an online auction platform. New Collar's robust, data-driven solution not only improved the platform's security but also enhanced its credibility and trust among users.

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