Data Mining and Fraud Prevention

Detect Compromised Cards Before Fraud Happens

admin — Thu, 04 Oct 2012 02:07:01 +0000

Take action before fraud happens. Business Data Miners proprietary algorithm is able to detect

cards that will likely have fraudulent activities within 6 months in the future. If these cards are

reissued or closely monitored, we can reduce a significant amount of the fraud loss. In a live

testing of our algorithm at a top 15 bank, 19% of cards that we predicted as being

compromised had fraudulent activities within 3 months after our prediction. We estimate that

the annual fraud savings is above $1 million. By reissuing risky cards, we prevent fraudulent

activity before it happens. An added benefit of doing this is that the impact of fraud on good

customers is eliminated.

Business Data Miners proprietary algorithm is able to detect cards that will likely have fraudulent activities within 6 months in the future. If these cards are reissued or closely monitored, we can reduce a significant amount of the fraud loss. In a live testing of our algorithm at a top 15 bank, 19% of cards that we predicted as being compromised had fraudulent activities within 3 months after our prediction. We estimate that the annual fraud savings is above $1 million. By reissuing risky cards, we prevent fraudulent activity before it happens. An added benefit of doing this is that the impact of fraud on good customers is eliminated.

Our bad debt model has saved the client tens of millions dollars over the past three years.

admin — Tue, 12 Jun 2012 15:05:51 +0000

As of 6/3, our bad debt model has saved tens of millions dollars over the past three years by reducing the delinquency rate of a large bank by 50%!

Build Fraud Detection Model Without Historically Known Fraud Data

admin — Tue, 22 May 2012 06:00:51 +0000

When there are no historically known fraud data, we have to build unsupervised models that do not require a target variable. There are a number of such models that we can try: one-class support vector machine, principal component based compression net, etc. They are detecting data points that are abnormal, not fraudulent per se. We only hope that being abnormal is highly related to being fraudulent.

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.