# Sift Science Fraud Prevention Platform for Digital Business 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. ## Testing Trust in Prediction Markets | Part 1 DevFeed: [Testing Trust in Prediction Markets | Part 1]() Original publisher: [Read original article]() Author: David Phillips Published: 2026-09-03T13:15:00Z Content type: opinion Language: en Sources: [Sift Science]() Topics: [integrity]() Tags: [data-insights](), [fairness](), [fraud](), [integrity](), [prediction-markets](), [prediction-markets-legislation](), [trust]() ### AI overview Part 1 examines how reported insider trading, irregularities, settlement issues, and operational design choices in prediction markets can undermine claims about fairness, market integrity, collective intelligence, and truth. It argues that gaps between provider claims and market realities can erode public trust and invite political or regulatory responses. ### Source excerpt This April, U.S. federal prosecutors charged an Army sergeant with using sensitive classified information to bet on Polymarket that U.S. forces would enter Venezuela and remove Maduro from power. About the same time, Kalshi disclosed that it had fined and suspended three congressional candidates for five years after they traded on prediction markets tied to [...] The post Testing Trust in Prediction Markets | Part 1 appeared first on Sift. ## How to Build a Fraud Signal-Sharing Strategy Across Teams and Vendors DevFeed: [How to Build a Fraud Signal-Sharing Strategy Across Teams and Vendors]() Original publisher: [Read original article]() Author: Jacob Sanchez Published: 2026-09-04T16:37:55Z Content type: tutorial Language: en Sources: [Sift Science]() Topics: [data](), [Security](), [dashboards](), [Slack]() Tags: [cross-team-fraud-collaboration](), [dashboards](), [data](), [fraud](), [fraud-kpis](), [fraud-prevention-strategy](), [fraud-signal-sharing](), [signal](), [signal-sharing-strategy](), [slack](), [strategy](), [teams](), [trust-and-safety](), [vendor-data-sharing]() ### AI overview This how-to article discusses building fraud signal-sharing programs across internal teams and vendors. It explains how shared signals such as PII, IP addresses, device data, and activity patterns can support fraud prevention, security, legal, compliance, growth, marketing, and customer support. It also compares informal sharing through Slack and email with shared dashboards and reports. ### Source excerpt I recently joined Jerry Hoff, CEO of AppSec Training, for a Blueprint Series session on fraud signal sharing, and it's a topic I keep coming back to. Fraud, trust and safety, and security teams often work from separate systems with no shared view of the same bad actor. That gap slows response time and lets [...] The post How to Build a Fraud Signal-Sharing Strategy Across Teams and Vendors 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. ## Testing Trust in Prediction Markets | Part 2 DevFeed: [Testing Trust in Prediction Markets | Part 2]() Original publisher: [Read original article]() Author: David Phillips Published: 2026-09-10T17:44:43Z Content type: opinion Language: en Sources: [Sift Science]() Topics: [integrity](), [data]() Tags: [analysis](), [comparison](), [data-insights](), [digital-trust](), [fraud](), [integrity](), [prediction-markets](), [trust]() ### AI overview Part 2 examines whether prediction markets can reliably produce trustworthy information. It compares Kalshi's centralized contract resolution with Polymarket's decentralized, oracle-based model and discusses risks involving contract interpretation, voting power, concentrated profits, and market integrity. ### Source excerpt Missed Part 1? Read it here. Prediction markets make a bold claim that they are engines of truth discovery. Proponents argue that by aggregating collective judgment, prediction markets generate forecasts that are far more valuable than traditional gambling or sports betting. While that claim has merit, a prediction market's output is not merely a well [...] The post Testing Trust in Prediction Markets | Part 2 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.