1. High frequency rule: If the number of transactions from a card in the past 7 days is above 75, then current transaction is fraud.
2. High dollar amount: If the amount of transactions from a card in the past 7 days is above $95,000, then current transaction is fraud.
We can plot credit card transactions on a plane as shown below. X-axis is the number of transactions in last 7 days. Y-axis is the total dollar amount of transactions in the last 7 days. Based on the two variables, every transaction is represented by a dot in the plane. In this plot, red dots represent fraud transactions and blue represent good. Fraud transactions normally have higher frequency and cumulative amount since fraudsters spend money more aggressively. Thus most of the red dots are located in the up right corner (high frequency and high cumulative amount).
A high frequency rule detects 2 red dots (yellow rectangle) and a high dollar amount rule detection 2 red dots (blue rectangle). Totally, we detect 4 red dots. If we want to detect more red dots, we have to lower the thresholds for those rules which will lead to many normal transactions being mistakenly marked as fraud.
The most effective way to separate red and blue dots is the straight line that runs northwest-southeast direction as shown in the figure. Unfortunately, the line can not be described by intuitive rules. What a statistical predictive model does is to find such a line through learning from the data. A statistical predictive model can not be described intuitively but it is far more accurate. It is often to see that a single predictive model outperforms hundreds or thousands of intuitive business rules combined. There is a newer post about the comparison of predictive models and intuitive rules from 13 aspects.