Anomaly Detection Project

011/Exercise/readme.md

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+### Case Study: Electric Motorcycle Battery Swap Fraud Analysis
+
+**Introduction:**
+
+This exercise entails analyzing a dataset from a battery swap service operating across different stations in a city. The goal is to detect potential fraudulent activities within the swap data to address revenue losses.
+
+**Objectives:**
+
+- Identify inconsistencies in the dataset indicative of fraudulent activities.
+- Propose additional data points for enhanced fraud detection.
+- Develop automated methods for real-time fraud detection and alert generation.
+
+**Dataset Overview:**
+
+The dataset comprises the following information for each swap:
+
+- Date and time of swap
+- Station location
+- Swapper's identity
+- IDs of received and returned batteries
+- Associated bike information
+- Client paying for the swap
+- Charger used
+- Client ID involved in the transaction
+- Gauge readings at swap start and end
+
+**Approach:**
+
+- **Data Exploration**: Examine distributions and relationships between variables to spot anomalies.
+- **Statistical Analysis**: Utilize descriptive statistics to identify outliers and unusual patterns.
+- **Machine Learning**: Employ anomaly detection algorithms such as Isolation Forest or One-Class SVM.
+- **SQL Queries**: Execute queries to detect irregularities and inconsistencies within the dataset.
+
+**Additional Data Points for Fraud Detection:**
+
+1. Location-based data to detect swaps occurring in unusual or unauthorized areas.
+2. Historical swap patterns of clients and stations for anomaly detection.
+3. Transaction timestamps to identify suspicious activity during non-operational hours.
+
+**Automated Fraud Detection:**
+
+- Implement scheduled scripts to run anomaly detection algorithms on new data regularly.
+- Utilize triggers in SQL databases to generate alerts for suspicious transactions in real-time.
+
+By combining statistical analysis, machine learning techniques, and SQL queries, we can effectively identify and flag fraudulent activities within the battery swap dataset, mitigating revenue losses and ensuring operational integrity.