# Fraud Prevention Published articles for Fraud Prevention. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Online Gambling Fraud Prevention for iGaming Operators DevFeed: [Online Gambling Fraud Prevention for iGaming Operators]() Original publisher: [Read original article]() Author: Ben Price Published: 2026-09-11T21:00:00Z Content type: article Language: en Sources: [Sift Science]() Topics: [Security, Privacy and Abuse Prevention](), [Bot](), [Sports]() Tags: [2026](), [bonus-abuse](), [bots](), [fraud-prevention](), [gambling](), [gambling-fraud](), [igaming](), [igaming-fraud-prevention](), [online-gambling](), [online-gambling-fraud](), [prevent-fraud](), [sports-betting-fraud]() ### AI overview The article examines fraud in U.S. iGaming and sportsbook operations, focusing on signup, deposit, and cashout risks. It highlights organized bonus abuse involving synthetic identities, stolen credentials, linked accounts, bots, device farms, and fabricated identities. ### Source excerpt In just Q1 2026, U.S. iGaming has generated about $3.04 billion, marking over 20% growth year-over-year and nearly $1 billion in April 2026 alone. For the full 2025 year, iGaming hit over $10 billion in revenue, up 27.6% over the previous year. With it being such a profitable industry, there's no wonder why it's ripe [...] The post Online Gambling Fraud Prevention for iGaming Operators appeared first on Sift. ## Expanding Fraud Strategies Beyond Payment Fraud DevFeed: [Expanding Fraud Strategies Beyond Payment Fraud]() Original publisher: [Read original article]() Author: Sift Trust and Safety Team Published: 2026-08-26T18:08:35Z Content type: article Language: en Sources: [Sift Science]() Topics: [account takeover]() Tags: [account-takeover](), [chargeback-disputes](), [chargebacks](), [compliance](), [customer-trust](), [fraud](), [fraud-and-compliance](), [fraud-prevention](), [fraud-strategy](), [loyalty-point-fraud](), [loyalty-points](), [non-payment-fraud](), [payment-fraud](), [promo-abuse](), [trust-and-safety]() ### AI overview This article explains why fraud programs should address risks across the full customer lifecycle rather than focusing only on payment fraud. It discusses account takeover, fake accounts, promo abuse, loyalty points, giveaways, and coordination between fraud, risk, and compliance teams. ### Source excerpt Most fraud programs start with payment fraud, and for good reason. It's the fastest path to measurable loss. But teams that stop there often miss account takeover, fake account creation, and promo abuse until those problems show up in support tickets, chargeback disputes, or a spike in customer complaints. By the time it's visible, it's [...] The post How to Solve for More Than Just Payment Fraud appeared first on Sift. ## iGaming Fraud Prevention: Key Strategies to Implement DevFeed: [iGaming Fraud Prevention: Key Strategies to Implement]() Original publisher: [Read original article]() Author: Ben Price Published: 2026-09-14T21:00:00Z Content type: article Language: en Sources: [Sift Science]() Topics: [Security, Privacy and Abuse Prevention](), [Exploit](), [math]() Tags: [2025](), [2026](), [acquisition](), [bonus-abuse](), [customer](), [fraud-prevention](), [gaming-fraud](), [igaming](), [igaming-fraud](), [igaming-fraud-prevention](), [industrial](), [marketing](), [multi-account-abuse](), [multi-accounting-detection](), [multi-accounting-fraud](), [network](), [prevent-fraud](), [revenue](), [synthetic](), [time](), [trust-and-safety](), [verification](), [volume]() ### AI overview This article explains how bonus abuse and multi-accounting have become major sources of iGaming fraud. It describes fraudsters exploiting promotional offers through repeated registrations, synthetic identities, stolen credentials, device farms, residential proxies, and synthetic documents, and argues that operators should justify prevention spending by measuring protected revenue. ### Source excerpt While every operator budgets for promotions as a customer acquisition cost, very few budget for the version of that cost that never converts into a real player. Bonus abuse and multi-accounting now account for the single largest fraud category in iGaming, making up 64% of fraud according to a recent study. But unlike chargebacks or [...] The post iGaming Fraud Prevention: Key Strategies to Implement appeared first on Sift. ## What is Trust and Safety? DevFeed: [What is Trust and Safety?]() Original publisher: [Read original article]() Author: Ben Price Published: 2026-08-28T23:00:00Z Content type: article Language: en Sources: [Sift Science]() Topics: [Security, Privacy and Abuse Prevention](), [Transactions](), [account takeover]() Tags: [account-takeover](), [authentication](), [chargebacks](), [digital-platforms](), [digital-trust](), [digital-trust-safety](), [fraud-detection](), [fraud-prevention](), [payment-fraud](), [trust](), [trust-and-safety](), [trust-safety]() ### AI overview This article explains Trust and Safety as an organizational capability for protecting digital platforms, users, transactions, and interactions. It describes how the discipline brings together fraud prevention, content moderation, abuse prevention, and policy enforcement across the user journey, including payment fraud, fake account creation, account takeover, and content integrity. ### Source excerpt Trust and Safety is the practice of protecting the integrity of digital platforms, their users, and the transactions and interactions that take place on them. As a discipline, trust and safety spans fraud prevention, content moderation, abuse prevention, and policy enforcement across the full user journey. For fraud teams and platform operators, it represents a [...] The post What is Trust and Safety? appeared first on Sift. ## Know Your Super Bowl: How Fraud Teams Can Prepare for Peak Payment Volume Spikes DevFeed: [Know Your Super Bowl: How Fraud Teams Can Prepare for Peak Payment Volume Spikes]() Original publisher: [Read original article]() Author: Sift Trust and Safety Team Published: 2026-09-02T16:15:14Z Content type: opinion Language: en Sources: [Sift Science]() Topics: [Security, Privacy and Abuse Prevention](), [Finance]() Tags: [agentic-commerce-fraud](), [business](), [digital-trust-safety](), [fraud-prevention](), [fraud-prevention-planning](), [incident-response-plan](), [payment-fraud](), [payment-volume-spikes](), [peak-payment-volume](), [product](), [seasonal-fraud-trends](), [shared-payment-credentials]() ### AI overview The article explains how fraud teams can prepare for predictable and unexpected payment volume spikes. It recommends identifying business-specific peak periods, coordinating forecasts and risk tolerance in advance, and tightly controlling fraud-rule changes around high-volume events. ### Source excerpt Every business has a Super Bowl. It just isn't always the Super Bowl. For one company, it might be Black Friday. For another, Valentine's Day. For others, the biggest payment spike of the year comes from a product launch, ticket release, or viral moment nobody saw coming. That was the premise of a recent Merchant [...] The post Know Your Super Bowl: How Fraud Teams Can Prepare for Peak Payment Volume Spikes appeared first on Sift. ## Payment Security Best Practices for SaaS Founders DevFeed: [Payment Security Best Practices for SaaS Founders]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-04-15T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [Security](), [Software as a service](), [Encryption](), [tokenization]() Tags: [best-practices](), [checkout](), [collection](), [compliance](), [data-transmission](), [encryption](), [fraud-detection](), [fraud-prevention](), [guide](), [payment](), [payments](), [saas](), [security](), [tokenization]() ### AI overview This guide explains layered payment security practices for SaaS companies, including secure checkout design, HTTPS and TLS, encryption, tokenization, fraud prevention, and incident response. It emphasizes minimizing exposure to card data and using payment-provider infrastructure. ### Source excerpt Essential payment security practices for SaaS companies - from tokenization and 3D Secure to fraud detection and secure checkout design. Protect your customers and revenue. ## Friendly Fraud: What It Is and How Digital Sellers Can Fight It DevFeed: [Friendly Fraud: What It Is and How Digital Sellers Can Fight It]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-04-05T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [Security, Privacy and Abuse Prevention](), [Software as a service](), [Software]() Tags: [chargebacks](), [digital-products](), [fraud](), [fraud-detection](), [fraud-prevention](), [guide](), [logs](), [payments](), [saas](), [subscription]() ### AI overview This guide explains friendly fraud, in which a legitimate customer disputes an authorized purchase, and distinguishes it from third-party fraud. It focuses on why digital businesses are vulnerable and presents practical prevention and chargeback-dispute strategies. ### Source excerpt Learn what friendly fraud is, why it costs digital businesses billions, and 10 practical strategies to prevent and dispute friendly fraud chargebacks. ## Chargeback Prevention for SaaS: 12 Strategies That Actually Work DevFeed: [Chargeback Prevention for SaaS: 12 Strategies That Actually Work]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-04-04T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [Software as a service](), [Transactions](), [Software]() Tags: [billing](), [chargebacks](), [fraud](), [fraud-prevention](), [merchant-of-record](), [optimization](), [payments](), [saas](), [subscription]() ### AI overview This guide presents 12 chargeback-prevention strategies for SaaS and digital product businesses. It explains how subscription confusion, unrecognized billing descriptors, digital delivery, long billing cycles, and limited customer communication contribute to disputes, and discusses Merchant of Record coverage. ### Source excerpt Reduce chargebacks with 12 proven prevention strategies for SaaS and digital product sellers, from descriptor optimization to Merchant of Record coverage. ## Chargeback Fraud Prevention: How to Protect Your Digital Business Revenue DevFeed: [Chargeback Fraud Prevention: How to Protect Your Digital Business Revenue]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-04-04T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [Security, Privacy and Abuse Prevention](), [Software as a service](), [account takeover](), [Software]() Tags: [account-takeover](), [chargebacks](), [data-breaches](), [digital-products](), [fraud-prevention](), [merchant-of-record](), [payment-fraud](), [payments](), [phishing](), [revenue](), [saas]() ### AI overview A guide to preventing chargeback fraud for SaaS and digital product businesses. It explains types of chargeback fraud, signals to monitor, fraud-prevention tools, and how the Merchant of Record model can shift fraud liability. ### Source excerpt Stop chargeback fraud with proven prevention strategies for digital product sellers and SaaS companies, from fraud signals to MoR protection. ## How to Set Up Payments in Base44 Apps DevFeed: [How to Set Up Payments in Base44 Apps]() Original publisher: [Read original article]() Author: Aarthi Poonia Published: 2026-03-23T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [App](), [No-code](), [Software as a service]() Tags: [billing](), [compliance](), [fraud-prevention](), [how-to](), [merchant-of-record](), [no-code](), [payment-processing](), [payments](), [subscriptions](), [tax](), [vat](), [webhooks]() ### AI overview A guide to adding payments to Base44 no-code applications with Dodo Payments using payment links and webhooks. It describes Dodo Payments' Merchant of Record services, including payment processing, subscription management, tax handling, compliance, and fraud prevention. ### Source excerpt Guide to adding payment processing to Base44-built applications. Use Dodo Payments for checkout, subscriptions, and global tax compliance in your no-code app. ## Face value: What it takes to fool facial recognition DevFeed: [Face value: What it takes to fool facial recognition]() Original publisher: [Read original article]() Author: Tomáš Foltýn Published: 2026-03-13T10:00:00Z Content type: article Language: en Sources: [WeLiveSecurity]() Topics: [Cybersecurity](), [Security & Privacy](), [Artificial Intelligence](), [spoofing]() Tags: [ai](), [conference](), [cybersecurity](), [financial-services](), [fraud](), [fraud-prevention](), [identity](), [privacy](), [real-time](), [uk]() ### AI overview ESET cybersecurity advisor Jake Moore demonstrates how facial recognition can be misused and defeated. His tests used identity-finding smart glasses, an AI-generated face to bypass bank onboarding, and real-time face-swapping software to evade a facial recognition watchlist. ### Source excerpt ESET's Jake Moore used smart glasses, deepfakes and face swaps to 'hack' widely-used facial recognition systems - and he'll demo it all at RSAC 2026 ## How to Accept Online Payments on Your Website in 2026 DevFeed: [How to Accept Online Payments on Your Website in 2026]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-03-09T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [Software as a service](), [Website](), [Security](), [tokenization]() Tags: [2026](), [ach](), [authentication](), [authorization](), [b2b](), [card](), [chargebacks](), [checkout](), [compliance](), [data](), [engineering](), [fees](), [fraud](), [fraud-prevention](), [gateway](), [global](), [guide](), [how-to](), [merchant-of-record](), [payments](), [saas](), [tax]() ### AI overview A practical guide for SaaS teams, indie founders, and digital product companies on accepting online payments in 2026. It explains payment flows, gateways, processors, merchant accounts, tokenization, 3D Secure, PCI DSS, taxes, fraud prevention, chargebacks, and region-specific payment methods. ### Source excerpt Learn how to accept online payments in 2026 with the right stack for global tax, local methods, checkout flows, and Merchant of Record coverage. ## Benchmarking LLMs in Real-World Applications: Pitfalls and Surprises DevFeed: [Benchmarking LLMs in Real-World Applications: Pitfalls and Surprises]() Original publisher: [Read original article]() Author: Jean Alves Published: 2025-11-25T15:31:00Z Content type: article Language: en Sources: [Feedzai]() Topics: [LLM evaluation / benchmarking](), [benchmarking](), [Generative AI](), [Security, Privacy and Abuse Prevention]() Tags: [benchmarking](), [computer-vision](), [evaluation](), [financial-fraud](), [fraud-prevention](), [hallucinations](), [large-language-models](), [llms](), [machine-learning](), [trust]() ### AI overview This article explains how Feedzai's ScamAlert uses Generative AI to identify interpretable scam signals from submitted screenshots, and why systematic evaluation and benchmarking are needed to assess consistency, explanations, performance, and limitations such as hallucinations. ### Source excerpt By Jean V. Alves and Ferran Pla Fernández Moving beyond binary classification provides novel insights. In the real world, scams rarely present themselves in black and white. Fraudsters exploit nuance, impersonate legitimate brands, and mask malicious intent with seemingly ordinary behavior. That's why Feedzai has launched ScamAlert (patent pending), a Generative AI-based system innovating on the current paradigm of scam prevention, in response to this growing challenge. Traditional detection systems treat the problem as a binary choice: scam or not a scam, often outputting an estimated "scam likelihood" measure. This value, even if accurate, doesn't tell users why something is risky or what they should watch out for, leaving them with little guidance on how to stay safe. A potential scam SMS The binary approach can often suffer from a lack of context. While a text message may look suspicious in a vacuum (e.g., a payment request via a less safe method) the user may have other reasons to believe in its legitimacy, such as a past history of such requests. Consequently, an incorrect risk estimate based on missing context may lead users to distrust the system's abilities. A traditional binary classification system outputs only a risk estimate ScamAlert, on the other hand, makes judgements on what it knows. Users submit a screenshot of the suspected scam, and ScamAlert identifies observable red flags, patterns or behaviors that are often associated with fraud, such as suspicious links or spelling errors. This approach empowers the user with interpretable insights into the detected risk signals, instead of a vague numeric value. This places the user in the driver's seat, by presenting them with the facts and enhancing their awareness and judgment. To fully understand a systems' ability to perform this task, we pair this labelling approach with a rigorous evaluation and benchmarking protocol. We test the consistency of model outputs for the same input; the model's ability t ## Building Trust in a Digital World: The Role of Machine Learning in Behavioral Biometrics DevFeed: [Building Trust in a Digital World: The Role of Machine Learning in Behavioral Biometrics]() Original publisher: [Read original article]() Author: Javier Liébana Published: 2024-06-21T14:01:53Z Content type: article Language: en Sources: [Feedzai]() Topics: [Digital Trust](), [Machine learning](), [Machine Learning, Security Attacks]() Tags: [core](), [digital-trust](), [feedzai](), [fraud-detection](), [fraud-prevention](), [machine-learning](), [online-fraud-prevention](), [research]() ### AI overview This article explains how Feedzai's Digital Trust solution uses behavioral, device, network, operating system, and browser data to support digital identity verification and transaction authorization. It describes a machine learning model that continuously evaluates collected data and uses insights from previous fraud to improve fraud detection and prevention. ### Source excerpt In the world of financial services, the bank or financial institution's relationship with the customer relies on digital trust, which is anchored in two fundamental principles. First, it must ensure the person engaging through digital banking channels is genuinely the individual they claim to be. Second, it must confirm that this person is authorized to complete the intended financial transaction. Addressing these crucial requirements is the core mission of Feedzai's Digital Trust solution. The solution collects and analyzes comprehensive user behavioral data, scrutinizes device information for potential threats, such as malware attacks, and evaluates contextual factors like network, operating system, or browser information to gain a complete understanding of the user's environment. However, the high volume and heterogeneous nature of the collected data, among other challenges, makes detecting potential fraudulent sessions with high accuracy a formidable endeavor. In this blog post we explore how a new machine learning (ML) model that performs a continuous evaluation of collected data and leverages insights from previous frauds to vastly improve Digital Trust's fraud prevention capabilities. We will start with an introduction to technical details behind our Digital Trust solution, going into the challenges of fraud detection and prevention. We continue by explaining how we can apply ML to boost fraud detection and how we deployed the new Fraud model to dozens of Feedzai customers. Table of Contents 1. Digital Trust data collection - 1.1 The user journey - 1.2 Behavioral biometrics data - 1.3 User's behavior - 1.4 Device and network data 2. Challenges to detect fraud in Digital Trust 3. Machine Learning for Digital Trust - 3.1 The holistic approach - 3.2 New Fraud Model 4. Deploying the model 5. In summary Digital Trust data collection To better identify the challenges that are typically faced when designing a fraud prevention system based on Digital Trust, first we ## The future of the App Store DevFeed: [The future of the App Store]() Original publisher: [Read original article]() Author: Marco Arment Published: 2021-09-13T21:18:11Z Content type: opinion Language: en Sources: [Marco.org]() Topics: [App](), [browser](), [Payment Fraud]() Tags: [apple](), [apple-pay](), [browser](), [fraud-prevention](), [payment](), [payment-fraud](), [processors]() ### AI overview An opinion article predicts that changes to Apple's App Store payment rules will preserve in-app purchases while allowing external purchase links. It argues that most apps will need to offer both options, with reader apps receiving an exemption, and that external payments could increase competition and enable new business models. ### Source excerpt After the dust settles from the developer class-action settlement, the South Korean law, the JFTC announcement, and the Apple v. Epic decision, I think the most likely long-term outcome isn't very different from the status quo -- and that's a good thing. Allowing external purchases Here's what I think we'll end up with: Apple will still require apps to use their IAP system for any qualifying purchases that occur in the apps themselves. All app types will be allowed to link out to a browser for other purchase methods. Most apps will be required to also offer IAP side-by-side with any external methods.1 Only "Reader apps" will be exempt from this requirement.2 Apple will have many rules regarding the display, descriptions, and behavior of external purchases, many of which will be unpublished and ever-changing. App Review will be extremely harsh, inconsistent, capricious, petty, and punitive with their enforcement.3 Apple won't require price-matching between IAP and external purchases. These few but important corrections reduce Apple's worst behavior and should relieve most regulatory pressure. The result won't look much different than the status quo: Most big media apps (qualifying as "reader" apps) won't offer IAP, but will finally be allowed to link to their websites from their apps and offer purchases there. Many games will offer both IAP and external purchases, with the external choice offering a discount, bonus gems, extra loot boxes, or other manipulative tricks to optimize the profitability of casino games for children (commissions from which have been the largest portion of Apple's "services revenue" to date). Most importantly, many products, services, and business models will become possible that previously weren't, leading to more apps, more competition, and more money going to more places. External purchase methods will evolve to be almost as convenient as IAP (especially if Apple Pay is permitted in this context), and payment processors will reduce the burd