Businesses, health systems, utilities and even governments run on data — unseen torrents of it constantly sloshing back and forth across globe-spanning networks, at a speed and volume too large for any human brain to fathom. Fraudsters often lurk within that hidden world, exploiting weaknesses in systems or using crafty techniques to mask their activities.
Organizations can use big data technology to guard against various types of fraud, from data breaches to false claims that an item never arrived, by training algorithms to recognize what is and is not normal behavior within a system. The technology required for such operations is complex and requires big investments from e-commerce experience builders like Signifyd, which uses data science technology to protect its customers from abuse. Meanwhile, cybersecurity outfits like ActZero use similar technology to help businesses recognize the potential presence of hackers within their own systems.
To learn more about what data science-driven cybersecurity looks like in practice, we caught up with data science leaders at Signifyd and ActZero.
Diana Rodriguez
Senior Director of Data Science
Company background: Signifyd helps retailers produce e-commerce experiences for customers. The company provides a financial guarantee against approved orders that turn out to be fraudulent, which places data security for customers and vendors front and center.
Describe the data sets your technology runs on and how that data is collected.
Our data sets consist of hundreds of billions of dollars’ worth of transaction data from thousands of online merchants selling in every retail vertical in more than 100 countries around the world. If you want to envision that data set of transactions, think of a top 10 online merchant. Now think bigger — and bigger still. That commerce network is at the core of what we do, and I constantly work with and on the technology that drives it. Our data is enriched with data sets from third-party providers, which amplifies our ability to understand the identity and intent of every order placed on our global commerce network. Our commerce network data is collected through API custom integrations with some retailers and through standard Signifyd applications available through all the major e-commerce platforms such as Shopify Plus, Magento, Salesforce, BigCommerce and others.
How Signifyd Uses Data Science
Signifyd’s models harvest their most valuable insights from behavior patterns that indicate whether an e-commerce order is legitimate or fraudulent, or whether a customer complaint involves an honest failure on a retailer’s part or a dishonest attempt by a fraudster to take advantage of the retailer. Those anomalies can come in the form of disparities between shipping addresses and billing addresses, transaction history, device ID and location, among thousands of other signals.
What are the most valuable insights or patterns you look for in the data?
Our challenge is to apply the latest developments in machine learning to turn fuzzy concepts like trust into solvable, quantitative problems. Do I trust this order and the consumer behind it? Secondly, fraud is an adversarial problem. Unlike, say, a self-driving car, where the innovators, developers and society at large all share an interest in making sure the artificial intelligence involved works well and evolves into better versions rapidly, fraudsters are out to …….
Source: https://builtin.com/data-science/data-science-cybersecurity