E-commerce platforms and websites are now universal across industries, be it clothing or artworks or even agricultural products. It is not only because customers have become comfortable browsing and buying items from online marketplaces, but also because the Covid-19 pandemic involuntarily pushed millions of families worldwide to online shopping and payment systems. This has led to an exponential surge in financial transactions on e-commerce sites.
But e-commerce companies are no strangers to scammers, fraudsters, theft, pilferage and piracy, etc. Online fraudsters cannot shoplift from online inventories. But they do target unsuspecting customers and vendors by exploiting various technical and financial flaws. And they are continuously in search of new vulnerabilities and methodologies to perpetrate frauds on e-commerce platforms.
In just one year (2022), e-commerce platforms worldwide witnessed 41 billion USD in losses from online payment fraud alone!
Online merchants and e-commerce platforms must remain perennially vigilant against online fraud and deploy advanced technologies like Artificial Intelligence (AI), Machine Learning (ML) and Big Data Analytics to automate fraud detection. These technologies, when coupled with Graph Neural Networks (GNNs), also allow e-commerce platforms to extract demographic and financial insights and churn out astoundingly personalized recommendations for each and every customer.
However, such AI/ ML systems are highly resource-intensive and require massive computational power to churn out highly accurate insights in real-time. This is where Cloud-based Graphics Processing Units (GPUs) can elevate their data processing capabilities.
This article will provide a walkthrough of fraud detection in e-commerce systems and how GPUs can help accelerate AI-based e-commerce fraud detection systems.
Table of Contents
Different Types of E-commerce Fraud
E-commerce fraud refers to any deceptive technique wherein fraudsters, scammers and cybercriminals intercept a commercial transaction occurring on an e-commerce platform with the motive of personal or monetary gain.
Global e-commerce sales are tipped to reach USD 5.55 trillion in 2022, accounting for one-fifth of total retail sales, and providing fraudsters an excellent platform to perpetrate scams, adulteration and piracy. Roughly 1/4th of all e-commerce brands worldwide reported experiencing account takeover fraud in 2021. Some well-known fraud methodologies that are known to cause massive disturbance in the e-commerce industry include –
- Identity theft: wherein someone steals the other person’s details and digital credentials, claiming themselves as that person. Such details include social security numbers, credit card details, phone numbers and email addresses, etc.
- Merchant fraud: selling fake or damaged or pirated products or adulterated food products, not delivering products after accepting payments, misrepresenting information and refusing to reimburse/ replace, and so on. Such behavior is not limited to reputable e-commerce platforms, but can also be perpetrated by tech-savvy merchants through their own fly-by-night sales websites.
- Friendly fraud: wherein an individual makes a purchase through their credit/ debit cards and then immediately files a chargeback stating non-receipt of goods/ services or claiming theft of debit/ credit card details.
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AI/ ML Solutions for E-commerce Fraud Prevention
Technological advancement has brought complexity to the internal architecture of online marketplaces. It is near-impossible for humans to comprehend and interpret fraudsters’ actions where a plethora of payment methods and a staggering flurry of transactions are involved.
Customers will shift to more secure online stores if their customer experience and journey are affected negatively. Thus, e-commerce companies should come up with far-sighted measures and develop heuristic solutions for eliminating fraud. AI/ ML systems can effortlessly remediate this difficulty since AI can pick up patterns and pinpoint anomalies that humans cannot even perceive.
Through AI/ ML models, e-commerce companies can build predictive data models running on sophisticated GPU-powered architecture to identify fraud or risks in advance. These are better than heuristic models because AI/ ML algorithms are trained on a stupendous variety of datasets. Hence, they also have less propensity to throw up false positives.
In the case of fraud-detection software, the more the volume of data used for training the model, the better the neural networks’ capability to identify fraud. Therefore, AI/ ML engineers use supervised and unsupervised Deep Learning programs to identify deception in real time. E-commerce companies have also been relentlessly migrating towards custom-built fraud detection systems that are highly adept at extracting transaction information and recognizing patterns in purchase and transaction activities.
Upgrading to GPU-accelerated infrastructure will facilitate faster AI/ ML inference model training as well as lightning-fast processing of massive reams of data. With better computation, fraud detection and elimination become expeditious, regardless of data volumes and number of parameters involved.
Common E-commerce Fraud Patterns
Some typical patterns/ anomalies that AI-automated e-commerce fraud-detection systems can look for include:
- Low-value orders: Cybercriminals can use various techniques to manipulate the payment page to buy for as low as possible. E-commerce fraud detection systems can easily be trained to recognize if any account places too many minimum denomination orders.
- Sudden increase in volume: Scammers use stolen or fake credit cards (available on the Dark Web and other forums) to purchase high-worth products. The cash they are spending on such purchases is obviously not their own. Detecting anomalous increase in sales of such high-worth products can pinpoint frauds and reduce the incidence of criminal activities and unauthorized transactions on e-commerce platforms.
- Use of multiple credit cards: Another pattern that automated systems can be trained to flag as suspicious is the usage of multiple credit cards by a user to brute force any purchase. Fraudsters may be deploying this methodology to test/ misuse stolen credit card details.
- Repeated declined transactions: Multiple attempts using multiple cards on a single transaction, all failing mainly because of security code errors/ MFA declining.
Role of GPU in E-commerce Fraud Detection
E-commerce frauds are exorbitant, leading to lost revenues in conjunction with brand reputation damage. 90% of e-commerce merchants surveyed worldwide in 2021 stated considering e-commerce fraud management very important to their overall business strategy, both from customer safety viewpoint and to avoid revenue loss.
Here are some key reasons e-commerce companies need GPU-accelerated infrastructure to run their AI/ML fraud detection systems.
More Training Data = Enhanced Fraud Detection = More Processing Power Requirement
The title itself clarifies that as the volume of data being input for AI/ ML training increases, the more refined the fraud detection system becomes. In other words, training with more comprehensive datasets, taking into consideration different forms of financial hoodwinking and fraud, it becomes easier for machines to learn and classify such incidents on different grounds.
Traditional CPU-based systems struggle to keep up with such gargantuan data volumes. Cloud GPU resources, on the other hand, can dynamically scale and offer enterprise-grade computation power. Through their parallel processing, multi-core architecture, dedicated GPUs can effortlessly sieve and evaluate the various financial, transactional and demographic datasets and minimize the chances of fraud quickly and efficiently.
Adaptive Authentication and Analytics
Companies can achieve adaptive response capabilities by implementing Deep Learning methods. Adaptive authentication and analytics have emerged as an advanced way of preventing frauds and risks associated with e-commerce. Top e-commerce companies have incorporated adaptive Deep Learning to improve the likelihood of detecting deceitful actions based on insights gained from recent confirmed cases.
In adaptive authentication (or risk-based authentication), the AI system will flag user activity as suspicious if the user logs in from a different browser, device, or geolocation. Based on the users’ behavior or pattern, the AI system will pose security questions or require the user to verify through another validation link/ code as an additional security factor.
Again, in adaptive analytics, the AI system will check for various parameters like login attempt, account creation date, and behavior to understand the user’s shopping tendencies. So, imagine a system executing these numerous adaptive identification and validation strategies on thousands of users simultaneously. Cloud GPUs become beneficial in such scenarios, and even help such systems scale and endure conditions of extreme stress (e.g., when millions of shoppers concurrently come online during mega-sale events).
Real-time Fraud Detection
The exponential evolution of real-time fraud prediction and detection has overwhelmed traditional rules-based technologies. Identifying fraud in real-time is a critical task since any delay or hesitation may lead to unbearable financial or reputation loss.
AI/ ML systems feed on massive datasets comprising classifications about fraudulent and non-fraudulent user behavior. Furthermore, these ML models use multiple supervised and unsupervised learning algorithms to predict user behavior. GPU processing is unavoidable when undertaking such resource-intensive operations.
Enterprise-grade Cloud GPUs with hundreds/ thousands of Tensor and CUDA cores can execute diverse workloads in parallel and manifolds accelerate these AI/ ML operations. Prediction and Inference operations, including flagging suspicious user behavior and notifying operations team, can thus be sped up and accomplished in real-time.
Securing Payment Gateways
Cybercriminals and scammers often exploit vulnerabilities in payment gateways and financial transaction pages to purchase items for free. AI/ ML systems can help identify these vulnerabilities and secure e-commerce platforms by highlighting mishandling of security protocols, refusing to validate fake/stolen credit cards, and rejecting lapsed digital certificates in websites.
These AI-enabled systems act as self-improving sentinels validating payments and prohibiting fraudulent/ spammy financial activities in mere seconds. However, operating such complex AI/ ML system needs massive computational power. GPUs come equipped with large on-chip memory and fast interconnectivity bandwidth. Furthermore, so efficient are the latest GPUs that Nvidia’s A100 GPU can outperform CPUs by 237 times across AI/ ML benchmarks.
We hope this article provides a holistic view of how GPU-powered AI/ ML solutions can prevent e-commerce companies from getting digitally robbed. Many e-commerce companies have already begun leveraging AI/ ML solutions that lean on Cloud GPU-powered infrastructure. This enables them to effortlessly incorporate multiple payment factors, user behavior datasets and complex algorithms to identify frauds and scams. GPUs can also be used to comprehend sophisticated mathematical algorithms known as ANNs/GNNs to offer personalized recommendations on e-commerce platforms.
If you’re an e-commerce enterprise or are in the process of developing an e-commerce marketplace, we highly recommend incorporating GPU-accelerated workflows in your algorithms. These enhance manageability and scalability, while bolstering the overall AI/ ML training and computation speed.
Ace Cloud offers incredibly powerful, parallel computing power with resizable GPU instances to boost your AI computing. E-commerce platform management and fraud detection have never been easier!
What is a GPU and how does it help in E-commerce fraud detection?
A GPU (Graphics Processing Unit) is a specialized processor designed to accelerate graphics rendering, but it can also be used for general-purpose computing. GPUs can help with E-commerce fraud detection by performing complex mathematical computations in parallel, which allows for faster and more efficient data analysis.
How can GPUs be integrated into E-commerce fraud detection systems?
GPUs can be integrated into E-commerce fraud detection systems by using them to process large datasets and perform machine learning algorithms that detect fraudulent behavior. This requires specialized software that can utilize the power of the GPU to accelerate computation.
What are the benefits of using GPUs for E-commerce fraud detection?
The benefits of using GPUs for E-commerce fraud detection include faster processing times, higher accuracy rates, and the ability to handle large datasets more efficiently. This results in better fraud detection capabilities and a more secure E-commerce platform.
What are some common ML algorithms for e-commerce fraud detection accelerated with GPUs?
Common machine learning algorithms used in E-commerce fraud detection that can be accelerated with GPUs include decision trees, random forests, support vector machines, and neural networks.
Are there any limitations to using GPUs for E-commerce fraud detection?
The main limitation of using GPUs for E-commerce fraud detection is the initial cost of purchasing and setting up the hardware and software required to utilize the GPU’s processing power. Additionally, GPUs may not be suitable for all types of fraud detection algorithms.
How can businesses determine if using GPUs for E-commerce fraud detection is right for them?
Businesses can determine if using GPUs for E-commerce fraud detection is right for them by evaluating their current fraud detection capabilities, identifying areas where they can improve, and assessing the cost and benefits of investing in GPU technology. A thorough cost-benefit analysis should be conducted before making a decision.
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