How to Set Up a Fraud Detection Solution on BigQuery Using SparkCognition

A fraud detection solution helps organizations prevent illegitimate activity such as theft of personal information, fraudulent charges to credit cards, account takeovers and other cyber crimes. These solutions are used by e-commerce merchants, banks and other financial institutions, retailers, insurance companies and other types of physical and online businesses. They help protect against criminal and friendly fraud fraud detection solution, reduce brand damage and accept more good orders by eliminating false declines. They work in real time to identify and alert suspicious activities — and often provide detailed analytics to guide the investigation and resolution process.

Detecting fraud is challenging because thieves use various methods to steal from customers. Identity theft is the most common method, but criminals quickly change their tactics when they see that a system can detect them. For this reason, a good fraud detection system must deploy flexible methods to catch thieves. It must be able to recognize a variety of scams, including phishing, click fraud, fake IDs, social engineering and more. But it also shouldn’t be so strict with identity checks that it turns off legitimate shoppers. Otherwise, buyers will go elsewhere.

Many of the top fraud detection solutions are based on machine learning. They analyze massive amounts of data from different sources to identify patterns that indicate the likelihood of fraud. The best ones have adaptive models that learn and adjust over time to improve their accuracy. They can also be customized for the specific needs of an organization, which is important since fraudsters are constantly finding new ways to fleece businesses.

This pattern describes how to set up a basic fraud detection pipeline on BigQuery using SparkCognition, an open source machine learning tool that specializes in natural language processing and data exploration. The system uses a BigQuery table to store historical credit card transaction and customer demographics data, and uses this data as training sets for machine learning models.

The model generates a list of all possible matches, which can then be evaluated to determine whether the match is fraud or not. This can be done for a single field or multiple fields, with the results being displayed on a dashboard or fed into another system. For example, a fraud analyst can examine the results from the model to determine if the transaction should be rejected or approved.

The most effective fraud detection systems are integrated with a range of other software platforms to support the entire business workflow. This includes an internal ticketing and case management system to assign, track and resolve investigations. Some solutions, like Kount Control, also offer post-authorization tools to intercept disputes and deflect chargebacks. Other solutions, such as Featurespace ARIC, incorporates the power of artificial intelligence to find fraud patterns based on the behavior of individuals or groups. These systems can also be integrated with text message services to send suspicious transactions to teams that can review and respond to them in real time. They can also be plugged into a business intelligence platform such as Looker to further streamline reporting and analytics.