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April 14, 2020 - Swetha Basavaraj

How to Choose a Fraud and Risk Management Platform That Delivers Complete Protection for the Entire Customer Lifecycle

By comprehensively monitoring, managing, and protecting customer lifecycles, businesses can focus on growing their good customers. while keeping bad actors out.

When a new user engages online with a business for the first time, a process of assessment, trust-building, and risk management begins. For the user, the emphasis is on trust. Can I trust this business with my information, is my information safe, and will I receive the service I expect, free of undue friction? For the business, the challenge is to consistently answer good users in the affirmative, while simultaneously blocking bad actors from their platforms. Once a user has been vetted and onboarded, a business must engage in continuous monitoring from that point forward, to ensure the user’s account engages only in legitimate activities, shows no signs of compromise, and is secure from threats. 

Achieving this level of complete protection for the entire customer lifecycle can be a daunting prospect in today’s fast-evolving fraud and risk landscape. Digital fraudsters have a seemingly endless supply of illicit data to rely on for everything from credential stuffing to synthetic identity theft, and they’re harnessing the power of AI and automation to mount massive, bot-powered attacks of unprecedented speed and scale. Business and finance continue to migrate online, more and more activity is taking place on mobile, and new third-party apps, tokens, and APIs are contributing to a vastly expanded attack surface area. 

The products that comprise DataVisor’s advanced fraud and risk management platform were built to make the goal of complete protection for the entire customer lifecycle a reality. 

Onboarding Good Users and Blocking Bad Actors

The first determination a business must make upon the arrival of a new user is whether the user is, in fact, legitimate. In other words, are they a real human or a bot? 

Device Intelligence

Gathering device intelligence is critical to the process of vetting and onboarding new users. To enhance an organization’s ability to make accurate distinctions, DataVisor has built dEdge. dEdge collects real-time intelligence from connected devices and enables organizations to proactively defend mobile and web applications against device manipulations and fast-evolving threat attacks. 

Machine Learning Engine

Given the speed, scale, and scope of modern fraud attacks, making these determinations accurately and in real time requires the use of an unsupervised machine learning-powered solution that can process raw data at scale without having to rely on rules, labels, and legacy knowledge, and that can adapt to fast-evolving technologies that make the creation of bots worrisomely easy.

DataVisor’s dVector and dCube solutions offer these capabilities. If a business wants these capabilities in the form of managed services, they can choose dVector. Businesses with internal teams that are ready to own and control their own processes can integrate dCube as their comprehensive fraud management solution.

Advanced Rules Engine

In those instances where a business already knows the criteria that define a bad actor—for example, when it’s evident they’re coming from a data center or specific IP—then whitelists and blacklists can be created to deal with these scenarios. DataVisor’s Advanced Rules Engine enables users to create and manage rule sets to systematically organize rules, and track and validate rule performance with advanced capabilities such as backtesting and forward testing. Teams can also leverage comprehensive features for advanced rule creation.

Complete Protection for the Entire Customer Lifecycle

Once a user is onboarded, a business, of course, hopes it’s the beginning of a long and mutually beneficial relationship. Unfortunately, that isn’t always what happens. A malicious entity might slip through unseen. Once on the platform, a fraudulent user account can be used to wreak all kinds of havoc, from spreading spam, perpetrating scams, and committing content abuse, to engaging in buyer-seller collusion. In other cases, problems result from downstream account compromise. A good user’s account gets hacked, and begins to commit malicious and fraudulent acts. Too often, by the time these anomalous behaviors are detected, the damage is done, the account is drained of value, and the fraudster is long gone.

Preventing Account Takeover

For all these reasons and more, constant monitoring is critical. dVector and dCube render this process of ongoing checks and balances easy to manage—in real time, and at scale. DataVisor also offers a sophisticated account takeover (ATO) solution that can monitor user behavior to detect if an account has been compromised. Through ongoing analysis of the actions that comprise good user behavior, it becomes possible to accurately surface unusual patterns that could indicate potential account compromise.

Automated Feature Engineering

DataVisor has also introduced Feature Platform, which accelerates the feature engineering process from weeks to minutes, and enables users to create advanced fraud features and build sophisticated models and rules. DataVisor’s Feature Platform automates feature engineering by producing thousands of auto-derived features based on user-imported raw data and mapped fields, as well as different user activities and use cases such as promo abuse, transaction fraud, and content abuse. Features are created using attributes—such as device IDs, user agents, and email addresses–to provide more powerful features for advanced fraud detection. To further improve model performance, DataVisor’s Feature Platform can recommend select features optimized for specific fraud types. These advanced featured engineering capabilities can be integrated with DataVisor’s Advanced Rules Engine, as well as with dCube and dVector.

The Importance of Comprehensive Protection

The signature advantage that DataVisor offers is the ability to integrate all these products to deliver comprehensive protection. For example, a business can leverage Feature Platform to create large numbers of valuable new features, which can, in turn, be used to improve the performance of the rules engine. Additionally, these products can integrate with a company’s existing solutions. Because DataVisor also offers superior decision engine capabilities, it is possible to aggregate, integrate and process a wide array of signals to derive a final result that is reliably actionable and which enables enterprises to confidently take meaningful and appropriate actions.

Advanced Case Management

Consistently producing accurate and reliable fraud intelligence in real time and at scale requires advanced case management capabilities, which are a core part of DataVisor’s product suite. Using a product like dCube enables fraud teams to efficiently review cases, take bulk action on groups of correlated accounts, and set auto-decisioning rules to improve review speed by orders of magnitude. Teams can additionally access a full audit trail to view historical activity on any given account, including who performed the actions, and for what reasons.

Enhancing Customer Experiences for Good Users

Ultimately, complete protection for the entire customer lifecycle is about ensuring exemplary experiences for good users of a platform, while simultaneously blocking bad actors from infiltrating and infecting that platform. The financial and reputational costs of failing to do so can be extreme, and it’s important to understand that the cost of losing a good customer to undue friction can often be greater than the costs associated with inadvertently allowing a bad actor through the net. 

DataVisor’s e-book A Guide to Complete Protection Across the Customer Account Lifecycle cites recent research from American Express which notes that “more than half of Americans have scrapped a planned purchase or transaction because of bad service, and 33 percent say they’ll consider switching companies after just a single instance of poor service.” The e-book also quotes a Merchant Fraud Journal, which points out that “legitimate customers view declines as a personal insult, and will often retaliate by actively speaking badly about a brand. This kind of negative word-of-mouth is devastating for merchants seeking a foothold with their target audience.” 

Successful long-term relationships with good users are essential for business success. When an enterprise chooses to adopt and integrate an advanced, AI and machine learning-powered fraud and risk management platform, they make a clear commitment to complete protection for the entire customer lifecycle. 

Photo of Swetha Basavaraj
about Swetha Basavaraj
Swetha is a senior product manager at DataVisor. She has a diverse experience of over 10 years leading teams in various capacities such as a product manager, entrepreneur and engineer to launch new B2B products in Yahoo, IBX (now Tradeshift), VolvoCars and IBM. Her past and current work has focused on building scalable enterprise products using latest technologies including machine learning.
Photo of Swetha Basavaraj
about Swetha Basavaraj
Swetha is a senior product manager at DataVisor. She has a diverse experience of over 10 years leading teams in various capacities such as a product manager, entrepreneur and engineer to launch new B2B products in Yahoo, IBX (now Tradeshift), VolvoCars and IBM. Her past and current work has focused on building scalable enterprise products using latest technologies including machine learning.