Archive for the ‘Uncategorized’ Category

Data Breach Involving40 Million Accounts at Target

Sunday, December 22nd, 2013

According to USA Today, up to 40 million accounts’ customer information were stolen between Nov. 27 and Dec.15.

BDM is selected as a Leading Marketing Analytics Consultant Announced by Research Firm SourcingLine

Friday, December 13th, 2013

BDM is selected as a Leading Marketing Analytics Consultant Announced by Research Firm SourcingLine. It is included in their Leaders Matrix. http://www.prnewswire.com/news-releases/leading-marketing-analytics-consultants-announced-by-research-firm-sourcingline-235560411.html

Detect Compromised Cards Before Fraud Happens

Wednesday, October 3rd, 2012
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.

Tuesday, June 12th, 2012

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

Tuesday, May 22nd, 2012

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.

Check Fraud Detection Model Is Put Into Production

Friday, March 30th, 2012

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

Friday, February 17th, 2012

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

Monday, November 21st, 2011

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

Sunday, October 30th, 2011

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

Tuesday, August 30th, 2011

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.