Outlier Detection - Deposits - Others - Dollar Value
Description
This test identifies anomalous transactions by comparing recent deposit values against other users' historical patterns. It calculates symbol-specific statistics, including mean and standard deviation of transaction dollar values, and flags outliers that significantly exceed certain thresholds. Transactions are analyzed in the context of the aggregate behavior of others for a given symbol.
Use Cases
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Fraud Detection: Identify users engaging in potentially fraudulent activities, such as sudden and uncharacteristically large deposits, which deviate significantly from their historical transaction behavior or the average activity for a specific symbol.
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Risk Management: Flag anomalous transactions that may indicate operational risks, such as incorrect transaction processing, system errors, or misuse of accounts, enabling financial institutions to mitigate risks proactively.
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Anti-Money Laundering (AML) Monitoring: Detect unusually large transactions that could indicate money laundering activities.
Required Data
Deposits Data
| Variable | Description |
|---|---|
timestamp | Timestamp of the deposit made (YYYY-MM-DD hh:mm:ss). |
user_id | User ID to identify the individual user. |
currency_type | The currency type of deposit being made; fiat or crypto. |
symbol | The asset symbol; e.g., BTC, ETH, USD, EUR. |
price_usd | The price of the symbol in USD. |
amount | The amount of the symbol being deposited. |
Parameters
| Parameter | Description | Type | Default Value | Configurable |
|---|---|---|---|---|
analysis_window | Overall duration (days) for analysis. | Integer | 1 (days) | Yes |
historical_window | Number of days to consider for historical data. | Integer | 90 (days) | Yes |
historical_minimum_number_transactions | Minimum number of historical transactions per user required. | Integer | 5 | Yes |
analysis_minimum_aggregate_dollar_threshold | Minimum aggregate dollar value of transactions per user in analysis period. | Float | 500 | Yes |
historical_minimum_number_days | Minimum number of unique active days required. | Integer | 2 | Yes |
Methodology
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Compute Historical Statistics For each user and symbol, calculate the historical mean and standard deviation of transaction dollar values. Additionally, compute aggregate symbol-wide statistics, including the mean of historical averages and the median of standard deviations for each symbol.
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Symbol-Wide Outlier Detection Compare a user’s transactions against the historical behavior of others for the same symbol. Transactions are flagged if their value deviates significantly from the symbol-wide mean plus a defined threshold.
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Categorization and Reporting Flagged transactions are classified as symbol-wide anomalies. These are then prepared for reporting, either as CSV exports or tickets for further investigation.