Delivered the optimal debit card signature transaction daily limit for a client bank. Performing the similar analysis to optimize PIN-based transaction daily limit. The core idea is to maximize the net of transaction fee plus overdraft fee minus fraud loss. The main technical challenge is to merge 100 million records in settlement, authorization, overdraft and claim fuzzily and iteratively.
Optimize Debit Card Daily Transaction Limit
November 21st, 2011Long Term Card Fraud Prediction
October 30th, 2011Most of the debit/credit card fraud detection is based on transaction level rules or scores that target the fraud transactions happening in real time. Fraud prevention operation is struggling to stop fraud in real/near real time.
If we can predict which cards will likely have fraudulent activities within the next a few months, we can either reissue those cards today or build transaction level rules to monitor them. The long term card fraud prediction is, just like long term weather forecast, doable. At BDM, we have tested such a method and found the results are good and actionable.
“Superficial” vs. “Inherent” Fraud Detection Rules
August 30th, 2011There are two types of rules, “superficial” rules and “inherent” rules. Superficial rules are easier to create and they are straightforward to understand, e.g., transactions happening at certain retailers. These rules may be good for a few weeks and their performance drop quickly. Inherent rules capture more time lasting ( and usually hidden and mutli-dimensional) patterns of fraud behaviors. To create them,we may have to use more sophisticated methods including predictive modeling. The PRM SQL statements for some of these inherent rules that we created have 5,000 characters. These rules work like magic and have very low false positives.
Detection of Common Point of Compromise
August 1st, 2011About 25% of compromised good cards identified by BDM will have fraudulent activities within the next 3 months. (In comparison, usually about 1% of cards alerted by a popular point of compromise (POC) service from a big company have fraudulent within 12 months.) The common points of compromise detected by BDM are highly accurate. Identifying POC accurately and early and reissuing cards provide the following benefits: 1. It is better than real time fraud detection because the fraud loss can be stopped before it happens; 2. Reissuing cards is less intrusive to good customers than blocking suspicious transactions.
Every BDM rule alert stops $32 additional debit card fraud
March 13th, 2011Based on the actual debit card fraud detected by BDM rules since June of 2010, every alert generated by BDM rules stops $32 fraud. The following are some facts about the BDM rules. (BDM stands for Business Data Miners.)
1. $32 is the total dollar amount of debit card fraudulent transaction actually stopped by blocking cards divided by the total number of alerts generated by BDM rules.
2. $32 does not include fraud transactions detected by existing non-BDM rules. For example, if both existing non-BDM and BDM rules alert on the same card and an analyst blocks it, the fraud loss prevented is excluded from $32.
3. The total additional fraudulent transaction amount prevented due to BDM rules is multiple million dollars annually.
4. There is no increase in operation cost at all. This is because: a. the volume of alerts generated by BDM rules is less than 10% of total number of alerts; b. we helped the client identify ineffective rules and retire them.
5. BDM rules are not only statistically accurate but also understandable. For example, we have BDM rules targeting on risky retailers, risky MCC and risky cities.
6. BDM rules are written in standard SQL statements that can be deployed in many fraud prevention products.
7. BDM rules are refreshed monthly based on latest transactions and fraud information.
In fraud prevention analysts’ own words, “these BDM rules are amazing”.
Neural Networks and Fraud Prevention
February 17th, 2011Neural networks have been widely used for fraud prevention in banking industry. But there are many predictive modeling technologies that are far more powerful than neural networks. We have consistently detected more frauds using methods other than neural networks.
BDM’s Debit Card Fraud Detection Rules Won A Contract With One Of Top 15 Banks
September 27th, 2010BDM signed a contract with one of the top 15 banks in America to provide debit card fraud detection rules. In a head to head competition, BDM’s rules detected far more frauds given the same number of alerts based on historical data than competitors did. BDM rules were deployed into production and have screened almost 200 million debit card transactions in real time. They have delivered solid results and achieved annual ROI of 1,000%. BDM rules are unique in that they detect fraud with extremely high accuracy statistically while they are interpretable by fraud prevention staff.
Our new debit card fraud detection rules perform well in production.
July 9th, 2010For testing purpose, they generate only 120 alerts every day. One full time fraud prevention analyst should be able to handle the volume. Within a week, $55K of fraud attempts have been denied exclusively due to these new rules. Two very experienced analysts enthusiastically told me that they like the rules.
These rules are designed to target the fraudulent transactions at historically risky retailers, from possibly compromised cards, with high transaction amount against card balance, etc.
There rules are completely transparent and are standard database SQL statements.
Implementing new fraud solution into the production system.
June 8th, 2010We are working to implement our new bank card fraud solution into the production system. The initial results are very positive. As one of the analysts who has worked with the new alerts put it, “this rule can catch so many frauds.”.
Analyze billions of data records
March 22nd, 2010I am using MapReduce and Hive (on Amazon EC2) to analyze large data sets.