The Future of Fraud Detection
By Rohan Nanda, Nicholas Garcia, & Alejandra Caro Rincon
Advances in technology give criminals increasingly powerful tools to commit fraud, especially using credit cards or internet bots. To combat the evolving face of fraud, researchers are developing increasingly sophisticated tools, with algorithms and data structures capable of handling large-scale complex data analysis and storage.
Source: Merchant911[1]
The most popular area of current fraud detection research has been in credit card, but we see online bots and Ad click fraud as growing concerns for the future. With rapid reduction in the cost of computing power, publishers can exploit vulnerabilities by creating bots to click on Ads to generate more revenue.
source: Phua, Clifton, et al. “A comprehensive survey of data mining-based fraud detection research.” arXiv preprint arXiv:1009.6119 (2010).
Credit-card Fraud Detection
Banks typically implement a single fraud detection and prevention system that tries to capture fraudulent transactions based on a model generalized to all their customers. This network model incorporates general fraud trends from different products across the bank. However, this approach is ineffective in the long run as they are too broad to find ever more sophisticated forms of fraud. Credit card associations are combining network as well as custom models to develop a comprehensive system that detects fraud upon point of sale. For instance, MasterCard implements the following approach:
Source: MasterCard Fraud Analytics
With a diverse set of data mining and neural network analysis techniques, and over 100 parameters to evaluate, MasterCard’s Expert Monitoring system aids issuer banks in detecting fraud within minutes of the transaction[2].
Source: MasterCard Fraud Analytics
Custom models or targeted modeling enhance the accuracy of fraud detection by pulling customer-specific data points[2]. In future, this technique will be standardized across all card associations and banks. Nonetheless, this approach is difficult because of customer’s privacy concerns for customer data. Consequently, the challenge the credit companies must master is implementing such a system without spooking the customer. A second challenge is the timeliness of the detection. Customers want their transactions approved in seconds, not minutes. To address this issue, better machine learning algorithms are needed to raise flags about fraudulent transaction in real-time. Standardized techniques are desirable across industries, however they must account for user heterogeneity and security preferences, and models have to bed constantly update in order to detect and learn emerging fraudulent behaviors.
Other challenges in fraud detection systems include but are not limited to:
- Imbalanced data distribution: the number of fraudulent transactions is much smaller than legitimate ones. History has shown that models trained on such data do not perform well, however bootstrapping and other resampling techniques are used to counter this in order to ‘con’ the model into thinking that it has more data to work with[3]
- Non-stationary data: with a continuous stream of transactions available, models have to be retrained often. However, this problem is compounded with imbalanced class distributions[3]
- Non-availability of public data: Due to the sensitive nature of the topic, often datasets are not available to effectively evaluate existing methods of fraud detection[3]
Online Ad Click Fraud Detection
In most of our entries we have been very interested in fraud detection in the financial industry. In this entry we also want to mention alternative emerging fraud behaviors; also how they harm some business and the strategies used in the industry to detect it.
Online click fraud is the act of clicking on advertisements without a specific interest on the product. Such practice is usually performed by software in a systematic way, increasing the marketing expenses for the business offering the product. This also harms the credibility of the advertising companies and the online advertising industry as a whole. Click forensics estimate that fraud clicks correspond to a 19% of overall clicks through ads. [4]
In order to identify fraudulent clicks there are several machine learning techniques being developed. For instance, detecting duplicate clicks over decaying windows is an important technique to accomplish such task. These type of models consist of eliminating the expired information according to the number of object collected or the activity in a certain period of time, over which the analysis is performed. Some of the most common algorithms implemented are based on Bloom filters, a data structure for testing whether an element belongs to a set. The particular characteristic of this approach is that these probabilistic data structures don’t allow false negatives. Thus avoiding classify a set of fraudulent clicks as legit.
Yet, beyond the technical approach of this problem it is important to note the important role and the challenges regulation around the world. The heterogeneity across regulatory frameworks in different countries poses great challenges for many industries to detect fraud. For instance, in countries where Electronic privacy laws are too strict it is harder to gather data, detect fraudulent patterns, and thus track and identify fraudsters. To learn more about the specific tools that are in the process of being implemented to combat fraud, please see our Overview of the Industry blog post here.
Works Cited
[1]Merchant911. (n.d.). Credit Card Fraud Trends. Retrieved April 17, 2014, from http://www.merchant911.org/fraud-trends.html
[2]MasterCard. (n.d.). How the Past Changes the Future of Fraud. Retrieved April 17, 2014, from http://www.mastercard.com/us/company/en/docs/Modeling_white_paper.pdf
[3]Pozzolo, A. D. (n.d.). Learned lessons in credit card fraud detection from a practitioner perspective. Learned lessons in credit card fraud detection from a practitioner perspective. Retrieved April 18, 2014, from http://www.sciencedirect.com/science/article/pii/S095741741400089X
[4] http://searchengineland.com/click-fraud-q42010-62471