Rules that a machine learning algorithm could use to identify fraudulent credit card transactions

Unusual spending patterns: An algorithm could analyze the cardholder's historical spending patterns and flag any transactions that deviate significantly from those patterns. For example, if a cardholder typically spends $50 or less at a time, a transaction for $500 might be flagged as suspicious.

High-value transactions: Transactions above a certain dollar amount could be flagged as potentially fraudulent. For example, a machine learning algorithm might flag any transaction over $1,000 as potentially fraudulent.

Location-based anomalies: An algorithm could flag transactions that originate from a location that is not typically associated with the cardholder. For example, if a cardholder typically uses their card in California, a transaction made from New York could be flagged.

Time-based anomalies: Transactions that occur outside of the cardholder's usual purchasing patterns could be flagged. For example, if a cardholder typically makes purchases during regular business hours, a transaction made at 3 AM might be flagged as suspicious.

Multiple small transactions: A series of small transactions made in a short amount of time could be flagged as potentially fraudulent. For example, if a cardholder makes five $1 transactions in the span of 10 minutes, a machine learning algorithm might flag those transactions as suspicious.

Card-not-present transactions: Transactions where the card is not physically present could be flagged as higher risk. For example, if a cardholder typically uses their card in person, a transaction made online might be flagged as potentially fraudulent.

Known fraud patterns: An algorithm could be trained on historical fraud data to identify patterns and flag transactions that match those patterns. For example, if there is a pattern of fraudsters making small transactions to test a stolen card before making larger purchases, an algorithm could flag transactions that match that pattern.

User behavior: An algorithm could track the user behavior of the cardholder and flag any transactions that deviate significantly from that behavior. For example, if a cardholder typically types quickly and accurately, a transaction that takes much longer to complete and contains errors might be flagged as suspicious.