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.
Posts Tagged ‘credit card fraud’
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.
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.
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 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 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.
Traditional fraud prevention methods try to detect fraud when it happens. By exploring the unusually behavior patterns long before when fraud occurs, we are able to proactively prevent fraud with minimum impact to good customers.
Through the principle of survival of the fittest, the natural evolution can find the best genes for the environment. A genetic algorithm (GA) simulates the natural evolution process to search the best solutions or fraud detection rules in our case. With our cutting edge GA based proprietary technology, a large number of initial fraud detection rules evolve to detect more fraud at lower false positive. After many generations of evolutions, the best fraud detection rules are the ones that survive.
It is a true story. I met David at Predictive Analytics World in Washington DC last October (of course, he later became one of my linkedin connections). He told me emotionally that in the past a few months his bank card transactions were blocked twice by the bank’s fraud prevention. He said he had stopped using the card and would cancel it in a few months.
I realize how serious the situation is for the bank.
Firstly, the bank will lose the future income from David if he was not bothered by the bank’s fraud prevention and might stay with the bank for, say, 5 more years. Supposing the bank makes $300 every year from David, that would be equivalent of $1,400 loss in present value.
Secondly, we can say that a lot of the time the fraud prevention is making mistakes. If the fraud prevention places holds on 100,000 cards a year, how many of them are mistakes and thus inconvenience good customers like David? 10,000? or 50,000? or 80,000? or 99,000?
Fraud prevention is a double-edged sword. On the one hand, it saves fraud losses. On the other hand, it reduces good revenue which could be significant. Do you want to share your insights on this?
If you are a C level executive or manager who owns the whole card or account operation, it is the bottom line that matters. I developed a methodology that uses the notion of the total cost to measure the impact of a fraud prevention to the bottom line. The total cost includes fraud loss, operation cost and opportunity loss due to false positive. The approach is not perfect but still useful. We can not afford not to measure the cost of inconveniencing good customers by fraud prevention. Please send me an email (email@example.com) if you want the document.
We are extremely excited about that Dr. Larry Bookman joined Business Data Miners. Dr. Larry Bookman has over 20 years of experience in applying data mining technologies and statistics to solve business problems in the financial, telecommunications, and leasing industries. Larry has delivered analytic solutions to dozens of companies, including credit risk evaluations, consumer fraud, customer segmentations, customer profitability analyses, customer attrition, sales forecasting, customer loyalty analyses, market basket analyses, and product cross-sell modeling. Larry’s credit risk solutions have enabled several of the largest banks and telecommunications companies to save over $200 million from making bad loans and incurring bad debt. Larry’s background includes the founding of three companies and is a consultant, advisor and board member to public and private companies. Larry holds a PhD in Computer Science from Brandeis University. He holds two patents (data transactional semantics) and has two pending patents (value-based ratings and communications brokering) and has published two books in Artificial Intelligence and over thirty conference papers.