10+ Great Metrics and Strategies for Fraud Detection
Emphasis here is on web log data. More than one rule must be triggered to fire an alarm. You may use a system such as hidden decision trees to assign a specific weight to each rule.
- Monte Carlo simulations to detect extreme events. Example: large cluster of non-proxy IP addresses that have exactly 8 clicks per day, day after day. What is the chance of this happening naturally?
- IP address or referral domain belongs to a particular type of blacklist, or whitelist. Classify the space of IP addresses into major clusters: static IP, anonymous proxy, corporate proxy (white-listed), edu proxy (high risk), highly recycled IP (higher risk), etc.
- Analyse domain name patterns, example: a cluster of domain names, with exactly identical fraud scores, are all of the form xxx-and-yyy.com, and their web page all have the same size (1 char).
- Association analysis: buckets of traffic with a huge proportion (>30%) of very short (< 15 seconds) sessions that have two or more unknown referrals (that is, referrals other than Facebook, Google, Yahoo or a top 500 domain). Aggregate all these mysterious referrals across these sessions – chances are that they are all part of a same Botnet scheme (used e.g. for click fraud).
- Mismatch in credit card fields: phone number in one country, email or IP adress from a proxy domain owned by someone located in another country, physical address yet in another state, name (e.g. Amy) and email address (e.g. email@example.com) look very different, and a Google search on the email address reveals previous scams operated from same account, or nothing at all
- Referral web page or search keyword attached to a paid click contains gibberish or text strings made of letters that are very close on the keyboard, such as fgdfrffrft.
- Email address contains digits other than area code, year (e.g. 73) or zip-code (except if from someone in India or China)
- Time to 1st transaction after sign-up is very short
- Abnormal purchase pattern (Sunday at 2am, buy most expensive product on your e-store, from an IP outside US, on a B2B e-store targeted to US clients)
- Same small popular dollar amount (e.g. $9.99) across multiple merchants with same merchant category, with one or two transactions per cardholder