# trust and safety Published articles for trust and safety. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## 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. ## 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. ## Dual-Embedding Trust Scoring DevFeed: [Dual-Embedding Trust Scoring]() Original publisher: [Read original article]() Author: Eric Chang Published: 2026-02-25T00:00:00Z Content type: article Language: en Sources: [Scribd Tech]() Topics: [trust and safety](), [Embeddings](), [Artificial Intelligence](), [Machine learning](), [data](), [datasets](), [genai]() Tags: [ai](), [content-trust-series](), [data](), [dataset](), [embeddings](), [featured](), [generative-ai](), [machine-learning](), [machinelearning](), [pii](), [privacy](), [research](), [scribd](), [trust](), [trust-and-safety]() ### AI overview Scribd describes a Content Trust Score that combines Generative AI signals, proprietary multilingual embeddings, and classical machine learning to assess the severity of documents violating defined trust and safety pillars. The research covers illegal, explicit, privacy/PII, and low-quality content, using annotated data from roughly 100,000 documents. ### Source excerpt Scribd is a digital library serving academics and lifelong learners, offering hundreds of millions of documents. This very nature presents a significant concern: content trust and safety. Protecting our library from undesirable and unsafe content is a top priority, but the multilingual and multimodal (text and images) nature of our platform makes this mission very challenging. Also, while third-party tools exist, they often fall short, lacking the nuance to handle our specific trust and safety categories.