Robbed in the Daylight

Online businesses are losing billions of dollars every year to fraud. Businesses in finance, ecommerce, social networking, and other verticals are being targeted everyday by individuals that have made it their job to steal from these businesses. These are individuals (fraudsters) with skills ranging from novice to highly skilled programmers, systems and networking experts that can hack into businesses and steal. The damages they cause range from stealing identity, steal products, to stealing the customers. With sophisticated programs they appear as normal customers as they engage in attackes on a business. Most businesses are ill-prepared and have little or no plan of action to combat these types of challenges. The ones that do have process in place to deal with this type of attacks deal with them in a reactionary way. They plug the hole after it’s discovered. After it’s done some damage. By monitoring and analyzing data, fraudsters can be detected before they can inflict significant damage.

Methods
A fraudster may create multiple accounts on a social network site. By multiple I mean hundreds and sometimes thousands of phony accounts. This obviously would be difficult for a human being to do, manually. So the fraudster will develop code to do the work automatically. The code would automatically signup accounts on the web site with bogus information and create user profiles. These profiles will attract legitimate users that will offer their information in hope of connecting with others. This information can sometimes be very personal. But mainly, the fraudster is interested in collecting account information to sell to other businesses or trick users to join other online services. This scam could be collecting the customer’s email addresses which they can use in Phishing scams. A Phishing scam is when a fraudulent web site poses as a legitimate web site to collect information from victims, information like their credit card number or online credentials to other web sites.




Detection
An online business will need to collect and analyze data to detect fraudulent activities. The sophistication level of fraudster’s method can make it difficult to detect their behavior. However, by informing themselves, businesses can review their online data for unusual visitor activity and investigate the cause. This is done by learning the patterns in their data. Every business will have its own unique patterns of visitor interaction. One of the common methods of detecting fraudulent online activity is to look at large amount of activity by a single IP address, in a short amount of time. This could mean a fraudster is running a robot program from a single computer to perform attacks. One has to make sure the IP address is not a proxy IP address which at times represents many different individuals, possibly legitimate ones. The activities to look for can be further segmented to focus more on the high valued ones. These can be account signup, login, and sending emails to other users.

To look more like a legitimate visitor, a fraudster’s attack can come from multiple computers spread across a large geographic location, or at least across multiple IP addresses, making it harder to detect their attack. Fraudsters with more resource at their disposal can hop from location to location and have banks of computers and modems to avoid detection. Like a business, they optimize their code and methods to be more effective.

Prevention
A common method of prevention for online fraud is using CAPTCHA. Wikipedia’s definition of CAPTCHA is: “type of challenge-response test used in computing to ensure that the response is not generated by a computer”.
These are sometimes images of single words or phrases that are morphed and distorted so a human can read them but it would be difficult for computers to decipher. These images are placed on web sites where visitors signup or log into their account, or perform some high value actions. The visitor is asked to type in what they see before proceeding with the action. Bellow is a sample of CAPTCHA image you might see on a web site.






There is no substitute for prevention like educating your visitors/ customers on fraudulent activities that they might get subjected to. Businesses need to regularly communicate with their visitor community on what to watch out for, in relation to their site, and not become a victim of online fraud.

As the Internet has evolved and continues to evolve, so has the online fraudulent and criminal activities. Businesses that have not been paying any attention to this area will need to start engaging now. Chances are they are being subjected to fraud in one form or another. To start with, the online business will need to stay vigilant on educating themselves on different forms of fraud. They would then need to develop process for collecting and analyzing data that would provide insight into possible fraud activity. Next, there needs to be tool(s) put in place to support the designed processes. These can be software available online or developed by business's own resources. There are also companies that provide services to combat fraud.

Fraud management should be treated like most other business processes management. It needs to have its own life-cycle (Education, data gathering and analysis, detection, and prevention). First, learning what are the different fraud methods being used, then through data analysis fraudulent activities are detected, then prevention measures are put in place (plugging the hole), and then the process begins all over again. This represents a continuous cycle. Those involved in managing fraud need to understand that, just like pests in the house, once you see one or two cockroaches, there are probably hundreds or thousands lurking in places that you can’t see them.



Steve Bashiri

WebAnalytics

As my first post, I though I write about some hot technical best practices in web analytics. But instead, I thought it best to write about a more fundamental question. One that has been shaping this industry for nearly thirteen years. What is Web Analytics? Well, easy. It's the practice of measurement and monitoring of all that interact with an organization's web properties.

There are two important aspects of this definition. Measurement and interaction.
But what are these measurements? Are these the campaigns, purchases, sign ups, downloads, and conversions? Or page views, visits, and visitors? The answer is all of the above, and much more.


And how about the interaction? The interaction we measure is from visitors. And by visitor, I mean the broad meaning of visitor, which includes crawlers, robots, and spiders.

Until recently, web analytic vendors have been developing tools that measured content navigation. whether it being campaigns, purchases, or conversions, it mostly dealt with granular interaction with content. In the ten to fifteen years life of web analytics, it's only been in the recent years that vendors have started measuring the interactions from the visitor's perspective. From voice of customer to customer experience to performance and up time monitoring, all have entered the web analytics arena. These tools shift the focus from purely content analysis to visitor analysis. Measuring visitor's experience has become popular with online marketing professionals.

Now the question is, whats next for this evolution? What will we measure next?

Steve Bashiri