CAP enhances Software as a Service – E-com Fraud Risk

This white paper is one of a series, outlining how CAP is being used to enhance a Software as a Service application.

Keywords:

Cloud, SaaS, Fraud, CDN, Risk, Security

The context:

A large retailer is outsourcing their e-commerce application to a third party provider. This relationship has existed for a long time, however the retailer grew increasingly frustrated with the cost and time involved in enacting any changes to the user experience and the lack of certain features. Adding to the sense of frustration was the fact that any requests were completely at the mercy of the third party provider.

The solution:

CAP was installed in the Amazon cloud between the end users and the third-party provider to help overcome some of these problems. By taking advantage of the CAP Agent’s built-in adaptive static content caching and ability to act inline in real time with minimum performance impact, the retailer resumed control of their brand and reputation.

Deployment diagram:

Why CAP:

No other product on the market offers a CDN-like capability that also involves enhancing and enriching content on the fly on a massive scale as well as addressing urgent security shortcomings. The retailer needs this capability to have some measure of control over third party software they are otherwise unable to change.

The story:

The retailer was experiencing increasing losses due to fraud, particularly from those originating from online sales. The problem was escalating such that each and every transaction was authorized, or cancelled, by a human customer service agent in a bid to mitigate the risk. This in turn led to lost sales due to lengthy order times, and unresolved transactions. The retailer‘s board were increasingly anxious to lower the level of fraud losses as well as maintain their reputation and the trust of their customers.

Using CAP, a plan was quickly hatched to resolve the issue:

The solution was twofold; block known (blacklisted) users or addresses and then automate the decision process (i.e. authorise, or decline the transaction).

Blocking included only allowing orders from specific countries (geo location blocking), impossible orders e.g. land speed checks and frequency of use e.g. device id checks. Implementing security rules e.g. against SQL injection attacks. Blacklisting names, addresses and card numbers using lookup tools to maintained blacklist databases. Automation of decision process included allowing orders to be processed unless they broke one or more of the rules imposed. Only these suspect orders were then fired into a case manager for processing by a human customer servicer agent.

Example administration maintenance and reporting dashboard:

The limitations:

The rules and automation were only as good as the decision making process and data behind them. Although the decisioning became more sophisticated, and required further rule enhancements, as time went on.

Business benefits:

The retailer experienced an immediate, and large scale (number and monetary value), reduction in losses due to fraud. This was coupled with an almost complete reduction of workload for their customer service department allowing for cutbacks and reallocation of personnel. Sales also increased as public confidence in the brand returned and flourished.

Rules blocks used:

Http Request Tracker (get user agent information)

Maxmind Geo Info (IP lookup)

Unique Lookup (database lookup)

Http Redirect (log out user)

Set Variable

Case Manager Trigger