5 Benefits of Applying Machine Learning to Your Fraud Solution17-April-2018
According to Rich Stuppy, COO at Kount, the combination of supervised and unsupervised machine learning is key in keeping fraudsters away while saving costs
Many elements contribute to the expanding fraud problem that online companies must contend with. These include: new criminal tactics, prolific growth of Internet-able and commerce capable devices, payment platforms offering an array of payment types, and the growing scale of data breaches globally.
At the forefront of this escalation is online fraud. Online fraud has realized a rapid rise as consumers have migrated towards online for their purchasing needs and, subsequently, fraudsters have followed the money. And because fraudsters don’t comply to any set rules, their tactics are aimed at exploiting opportunities for the least amount of effort with the greatest gains.
There are two types of fraud in today’s market: friendly and criminal. Both types of fraud are getting more and more difficult to identify and stop. Merchants’ reliance on legacy fraud management solutions that use static decline lists or limited rule-based risk scoring is not up to the task of deterring today’s fraudsters. Because fraud is ever-changing, adopting a multi-layered approach that uses dynamic data and latest technologies such as machine learning in combination with human and domain intelligence is essential to identifying and stopping fraud.
At the heart of today’s enterprise class fraud solutions is machine learning, an application of the broader artificial intelligence (AI) market that speaks to computer systems that automatically learn and improve from experience without being explicitly programmed. When applied to fraud, machine learning provides the analytic powers to identify patterns and help stop fraudulent activity long before the crime impacts a merchant’s bottom line.
The most advanced fraud solutions use a combination of supervised and unsupervised machine learning to instantaneously gain current environment and historical insight, associate and connect bad actors - as well as relative risk and safety of a transaction. This insight provides organizations with more confidence in every transaction, while combating the ever-changing tactics of fraudsters and allowing merchants to approve more orders, uncover new revenue opportunities and improve profits.
Unsupervised machine learning assimilates billions of data points across a vast global network to share critical information used in policy making, determining transaction validity, scoring, device identification, geo location, IP proxies, dynamic monitoring, continuous authentication, etc. Fed by the analysis of millions of data points, unsupervised machine learning is focused on identifying the risk of a transaction. Unsupervised machine learning is continually updated by new data from millions of transactions, analysis of patterns and the building of personas that represent fraud.
Supervised machine learning simultaneously analyzes that same data through hundreds of proven models to create a rating that shows the relative risk or safety of a transaction. Supervised machine learning is updated at the feature engineering level to ensure that the information is continually aligned with business objectives. To accomplish this, supervised machine learning needs to be updated daily and tweaked offline to properly train the models to identify fraud. Conducted offline, a data scientist or business analyst can change the model (aka supervise its learning) until it is able to correctly identify fraud.
Specific advantages of deploying machine learning into a fraud solution are:
- Enhanced automation that leads to fewer manual reviews
- Greater speed in making a risk assessment by quickly identifying patterns in data
- Increased accuracy to identifying good orders versus fraudulent orders
- Allows data to be better classified and trends to be identified quickly
- Efficient utilization of resources is achieved as models are constantly updated with new data and feature extraction.
Organizations that leverage machine learning are empowering their decision makers with the ability to access data, understand its meaning and make informed decisions to stop fraud before it impacts the business’ bottom line and the overall brand. Security, risk, and fraud management professionals face a myriad of obstacles when implementing a fraud strategy including cost, implementation cycle, access to data, detecting legitimate vs fraudulent orders, customization, and ability to scale with a business, etc. Machine learning enhances a company’s ability to stay ahead of criminals regardless of the tools and tactics they use to commit fraud.
About Rich Stuppy
For more than a decade Rich has been involved in developing fraud mitigation, compliance and big data strategies. Rich came to Kount after 14 years with a fortune 50 retailer. His background in enterprise-class systems has helped shape Kount’s system into an industry leading platform that helps clients boost sales and beat fraud.
Kount helps businesses boost sales by reducing fraud. The company has been recognized as an award winning, reliable and highly-scalable platform. Our all-in-one, SaaS platform simplifies fraud detection by applying patented machine learning through Kount's proprietary platform offering maximum protection for some of the world's best-known brands. Companies using Kount accept more orders from more people in more places than ever before.