Data Mining and Fraud Prevention

Visa, MasterCard affected by ‘massive’ data breach

admin — Sat, 31 Mar 2012 12:46:54 +0000

On the other hand, small data breach events (e.g, skimming) are happening on daily basis (a few hundred or thousand cards are skimmed here and there across millions of POS/ATM). They are less dramatic and wouldn’t make the headline. But their cumulative damage over a year is huge. That’s why we have developed Fraudulent Card Early Detection Solution.

Check Fraud Detection Model Is Put Into Production

admin — Fri, 30 Mar 2012 21:32:44 +0000

Feedback from the client, one of the top 15 banks in US, was great. The volume of existing ASI19 check fraud alerts is reduced while more frauds are detected.

Stop Frauds Three Months Before They Happen

admin — Fri, 17 Feb 2012 12:58:34 +0000

BDM’s algorithm is able to produce a list of likely compromised cards, 19% of which will have fraudulent activities within 3 months in the future. If we reissue them today, we can reduce significant amount of fraud loss with least amount of impact to good customers.

Optimize Debit Card Daily Transaction Limit

admin — Tue, 22 Nov 2011 05:05:15 +0000

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.

Long Term Card Fraud Prediction

admin — Mon, 31 Oct 2011 03:00:06 +0000

Most 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

admin — Tue, 30 Aug 2011 18:26:47 +0000

There 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

admin — Mon, 01 Aug 2011 12:07:00 +0000

About 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

admin — Sun, 13 Mar 2011 22:41:01 +0000

Based 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

admin — Fri, 18 Feb 2011 03:40:00 +0000

Neural 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

admin — Tue, 28 Sep 2010 04:45:08 +0000

BDM 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.