# fraud detection Published articles for fraud detection. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## 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. ## What is CIAM in 2026 and why does it matter? DevFeed: [What is CIAM in 2026 and why does it matter?]() Original publisher: [Read original article]() Author: Ravleen Kaur Published: 2026-09-15T00:00:00Z Content type: tutorial Language: en Sources: [Twilio Blog]() Topics: [identity and access management](), [trust](), [ai-governance](), [Security](), [Artificial Intelligence](), [IAM](), [Generative AI]() Tags: [ai-agents](), [ai-governance](), [email](), [fraud](), [fraud-detection](), [identity](), [identity-and-access-management](), [industry-insights](), [messaging](), [mobile](), [trust]() ### AI overview This article explains how customer identity and access management (CIAM) is evolving from point-in-time password checks into a continuous trust and control layer. It describes CIAM's role in authenticating users, governing delegated authority for AI agents, evaluating risk across channels, and balancing low-friction access with fraud detection. It also distinguishes customer identity from workforce IAM. ### Source excerpt Discover how customer identity and access management (CIAM) uses continuous trust, deepfake defense, and agentic AI governance to protect users. ## Announcing On-Demand State Repartitioning for Apache Spark™ Structured Streaming on Databricks DevFeed: [Announcing On-Demand State Repartitioning for Apache Spark™ Structured Streaming on Databricks]() Original publisher: [Read original article]() Author: Thangam Vaiyapuri; Jay Palaniappan; B. Micheal Okutubo; Zifei Feng Published: 2026-09-14T21:04:30Z Content type: release Language: en Sources: [Databricks]() Topics: [Streaming](), [databricks](), [real-time](), [Amazon S3](), [API]() Tags: [amazon-s3](), [api](), [data-engineering](), [databricks](), [engineering](), [fraud-detection](), [infrastructure](), [monitoring](), [net-11-preview-7](), [production](), [real-time](), [streaming]() ### AI overview Databricks announces on-demand state repartitioning for Apache Spark Structured Streaming in Public Preview, available in Databricks Runtime 18 and later. The capability lets production stateful streaming queries resize their partition count while preserving checkpoint state, supporting workloads such as aggregations, stream-stream joins, deduplication, sessionization, and transformWithState. Coveo reports reducing related Amazon S3 API costs by 40%. ### Source excerpt Anyone running stateful Apache Spark™ Structured Streaming queries in production... ## Message Queue vs Event Bus vs Broker DevFeed: [Message Queue vs Event Bus vs Broker]() Original publisher: [Read original article]() Author: Nikki Siapno Published: 2026-09-10T12:22:39Z Content type: article Language: en Sources: [Level Up Coding System Design Newsletter]() Topics: [systems](), [Latency]() Tags: [architecture](), [consumer](), [fraud-detection](), [latency](), [messages](), [notifications](), [payment](), [queue]() ### AI overview This article distinguishes message queues, event buses, and brokers by the meaning of the message. It explains that queues assign work to consumers, support retries and dead-letter queues, smooth traffic spikes, and can develop growing backlogs and higher latency under sustained overload. ### Source excerpt They often show up in the same architecture diagram, which makes them look interchangeable. They're not. ## 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. ## Uncovering the Shape of Fraud with Cosmos Explorer: Visual Metaphors Behind Millions of Transactions DevFeed: [Uncovering the Shape of Fraud with Cosmos Explorer: Visual Metaphors Behind Millions of Transactions]() Original publisher: [Read original article]() Author: João Bernardo Narciso Published: 2026-04-07T17:24:51Z Content type: article Language: en Sources: [Feedzai]() Topics: [Data visualization](), [data]() Tags: [data](), [data-visualization](), [dataviz](), [decision-making](), [design](), [feedzai](), [fraud](), [fraud-detection](), [patterns](), [research]() ### AI overview Feedzai's Data Visualization Research team is developing Cosmos Explorer, an interface that uses universe-inspired visual metaphors to help analysts examine patterns, trends, outliers, and possible fraud across hundreds of millions or billions of transactions. The project explores how to preserve meaningful details at very large scale while supporting data analysts and data scientists. ### Source excerpt Uncovering the Shape of Fraud with Cosmos Explorer: Visual Metaphors Behind Millions of Transactions The Data Visualization Research team is developing Cosmos Explorer, an interface that leverages universe-related visual metaphors to convey information about the billions of transactions processed by Feedzai. Pedro Cruz, professor at Northeastern University, partnered with Feedzai to bring this idea to life by contributing with his creativity and expertise to solve this challenging visualization problem. https://medium.com/media/1b2ecfadd91d204640462f89fa6ff67f/href When we look out into the universe, we don't just see emptiness. We see an unimaginable scale: billions of galaxies, each containing billions of stars, each a point of light carrying its own story. No single observer can take it all in at once. Yet with the right instruments, patterns emerge: the structure of the cosmos itself becomes visible. In the digital realm, there is another universe just as vast and intricate. Every day, hundreds of millions of events flow through Feedzai's system which assesses them to protect consumers all over the world. Each one is a unique data point (e.g., a purchase, a login, a transfer). Individually, they don't tell us much. Together they form a living universe of behavior that represents the diversity in people's lives. But fraud lurks in everyday transactions, with criminals trying to hide their activities within the sheer volume of transactions. The question is: how can we represent those patterns meaningfully, the normal behaviors and the fraudulent behaviors, the trends and the outliers, to empower data analysts and data scientists in their decision-making processes? The biggest challenge is scale. No one can look at billions of events one by one. Aggregation helps, but it smooths over the details, which often encode the most interesting signals like the faint outlines of fraud or unusual clusters of activity. But what if we could see it all at once? Not just a summa ## Braintree vs Stripe: Which Payment Gateway Is Better for SaaS in 2026? DevFeed: [Braintree vs Stripe: Which Payment Gateway Is Better for SaaS in 2026?]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-04-06T00:00:00Z Content type: comparison Language: en Sources: [Dodo Payments Blog]() Topics: [Software as a service](), [stripe](), [Developer experience](), [API]() Tags: [2026](), [comparison](), [comparisons](), [developer-experience](), [documentation](), [features](), [fees](), [fraud-detection](), [global](), [merchant-of-record](), [payments](), [saas](), [stripe]() ### AI overview This guide compares Braintree and Stripe for SaaS businesses across pricing, features, subscription billing, global coverage, developer experience, fraud tools, support, and documentation. It also introduces the Merchant of Record model as an alternative. ### Source excerpt A detailed comparison of Braintree and Stripe for SaaS businesses - covering fees, features, global coverage, and when to consider a Merchant of Record instead. ## 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. ## Building Resilient Fintech Infrastructure for Scale DevFeed: [Building Resilient Fintech Infrastructure for Scale]() Original publisher: [Read original article]() Author: David Weiss Published: 2026-04-02T00:00:00Z Content type: article Language: en Sources: [Cockroach Labs]() Topics: [Resilience](), [Data Infrastructure](), [Database](), [CockroachDB](), [legacy]() Tags: [cockroach-labs](), [cockroachdb](), [compliance](), [fintech](), [fraud-detection](), [infrastructure](), [legacy](), [outages](), [postgresql](), [resilience](), [uptime]() ### AI overview The article argues that fintech and quant firms need resilient data infrastructure to handle growth, real-time payments, instant settlement, AI-driven fraud detection, and cross-border compliance. It describes the limits of legacy database architectures and presents SumUp's migration from PostgreSQL to CockroachDB as an example of improved resilience and near-zero downtime. ### Source excerpt The fintech companies and quant firms that define the next decade aren't just building better products. They're succeeding with more resilient fintech infrastructure. ## The Best No-Code Payment Tools for Indie Hackers DevFeed: [The Best No-Code Payment Tools for Indie Hackers]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-03-24T00:00:00Z Content type: comparison Language: en Sources: [Dodo Payments Blog]() Topics: [No-code](), [Tool](), [webflow]() Tags: [2026](), [article](), [checkout](), [compliance](), [fraud-detection](), [indie-hackers](), [integrations](), [no-code](), [payment](), [payments](), [subscription](), [tax]() ### AI overview This comparison explains three types of no-code payment tools for indie hackers: hosted payment links, branded hosted checkout pages, and embedded overlay checkouts. It discusses tradeoffs involving domain control, tax and compliance responsibilities, integrations, international customers, fraud detection, and business growth. ### Source excerpt Discover the top no-code payment solutions that allow indie hackers to launch and scale their products without writing a single line of billing code. ## Durable Digest: December 2025 DevFeed: [Durable Digest: December 2025]() Original publisher: [Read original article]() Author: Temporal Technologies Published: 2025-12-19T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [reliability](), [Availability](), [Replication](), [OpenTelemetry](), [Artificial Intelligence](), [Netflix](), [Ruby](), [Monitoring]() Tags: [2025](), [ai-agents](), [announcements](), [blog-post](), [deployment](), [fraud-detection](), [high-availability](), [how-to](), [netflix](), [openai](), [opentelemetry](), [product-launches]() ### AI overview Temporal's December 2025 Durable Digest reviews major product launches and updates, including Nexus GA, High Availability and replication features, Worker Versioning, SCIM User Management, the Ruby SDK, and an OpenMetrics endpoint. It also highlights OpenAI Agents SDK integration, a Netflix builder spotlight, and resources on AI fraud detection and OpenTelemetry observability. ### Source excerpt It's been a packed year at Temporal. In this issue: major product launches, a builder spotlight from Netflix, and two fresh "How to Temporal" resources ## What to look for in a Payment Processing tool for your SaaS DevFeed: [What to look for in a Payment Processing tool for your SaaS]() Original publisher: [Read original article]() Author: Joshua D'Costa Published: 2025-11-11T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [Software as a service](), [Tool](), [Security](), [User experience (UX)]() Tags: [chargebacks](), [fraud-detection](), [integrations](), [pricing](), [saas](), [security](), [tool](), [ux]() ### AI overview A practical guide for SaaS companies choosing a payment processing tool. It explains payment processor responsibilities and outlines evaluation factors including checkout UX, integrations, pricing, security, fraud detection, chargeback management, currencies, and payment methods. ### Source excerpt Find the best payment processor for your SaaS with a practical checklist covering checkout UX, integrations, pricing, security, and global methods. ## The Modern Data Toolbox DevFeed: [The Modern Data Toolbox]() Original publisher: [Read original article]() Author: Doximity Published: 2025-08-18T00:36:00Z Content type: article Language: en Sources: [Doximity]() Topics: [data](), [Large Language Model](), [Machine learning](), [data-modeling](), [Statistics](), [Machine Learning, Security Attacks]() Tags: [data](), [data-modeling](), [datasets](), [fraud](), [fraud-detection](), [large-language-models-llms](), [machine-learning](), [statistics]() ### AI overview The article explains how to choose among large language models, machine learning, and statistical methods based on data characteristics, goals, scale, and explainability requirements. It argues that complex data problems often benefit from hybrid systems that combine these approaches, illustrating the idea with a multi-layered fraud detection system for payment processing. ### Source excerpt Matching the Tool to the Task A Quick Recap In a previous article, we focused on the strengths of Large Language Models (LLMs), traditional Machine Learning (ML), and statistical methods and recommended 4 key questions to help you choose the right tool for a data solution. Your Data: Is it structured or unstructured? Bounded or unbounded? Your Goal: Do you need prediction, generation, or inference? Your Data Volume: Are you working with massive datasets or limited samples? Your Need for Transparency: Is deep explainability or strict repeatability a requirement? The key takeaway was that LLMs excel at understanding and generating unstructured, unbounded language; ML models are the gold standard for prediction on structured data; and statistics are invaluable for inference and causality, especially with limited data. However, the most complex and valuable real-world problems rarely fit neatly into one box. What if you need to understand unstructured customer feedback and use it to accurately predict churn? This is where hybrid approaches come in, combining the capabilities of each tool to create a system that is greater than the sum of its parts. Below, we present a few examples showcasing how working with hybrid data approaches helps unlock greater value. Hybrid Data Solutions In our experience, the most effective data solutions often emerge from combining multiple data modeling approaches. Rather than viewing LLMs, ML, and statistics as competitors, we recommend considering them as complementary parts of your broader data toolbox. 1. A Multi-Layered Fraud Detection System built using ML, LLM and Statistics Let's consider a high-stakes and regulated environment of a payments processing system. The primary challenge is to detect and block fraudulent transactions in real-time without incorrectly declining legitimate purchases. In addition, the decision-making process should be transparent and auditable. The analytics workhorse of such a system will be a real-time trans ## Feedzai TrustScore: Enabling Network Intelligence to Fight Financial Crime DevFeed: [Feedzai TrustScore: Enabling Network Intelligence to Fight Financial Crime]() Original publisher: [Read original article]() Author: Sofia Guerreiro Published: 2025-07-25T11:59:49Z Content type: article Language: en Sources: [Feedzai]() Topics: [Network](), [Artificial Intelligence](), [AI Models]() Tags: [ai](), [ai-models](), [banking](), [feedzai](), [financial](), [financial-fraud](), [fraud](), [fraud-detection](), [genai](), [machine-learning](), [network](), [network-intelligence](), [report](), [research](), [scams]() ### AI overview This blog post introduces Feedzai TrustScore, part of the Feedzai IQ solution, as an AI-based approach to financial fraud detection. It describes how network intelligence and aggregated knowledge from Feedzai's processed events are intended to help detect threats earlier and adapt to changing fraud patterns. ### Source excerpt By Sofia Guerreiro, Ricardo Ribeiro Pereira, Iker Perez, Jacopo Bono Detecting financial fraud is like finding a moving needle in a shifting haystack. Fraud accounts for a tiny fraction of financial transactions, often less than 0.1%. At the same time, fraudsters are constantly adapting their tactics to evade detection. And this happens within a live and dynamic environment, where financial behaviors and technologies are changing over time. In short, this is an exceptionally difficult problem for financial institutions. With the rise of digital banking and new technologies like GenAI, this problem is becoming even more challenging. In fact, fraud schemes are spreading faster, growing more sophisticated, and becoming increasingly coordinated across geographies. Meanwhile, many financial institutions still rely on detection systems that are either too rigid (e.g., relying on handcrafted rules) or too narrow (e.g., dependent on custom AI models), and neither option can keep up with the pace of change. At Feedzai, we've developed a new approach. In this blog post, we introduce Feedzai TrustScore, part of the Feedzai IQ™ solution. This is a state-of-the-art AI solution that combines the aggregated knowledge from our network of $8.02T yearly processed events to detect threats earlier, adapt continuously, and safeguard financial systems with speed and precision. Traditional Fraud Detection: Catching a Moving Target With a Fixed Net Every year, millions of people fall victim to financial fraud or scams, with a recent report revealing an average loss of over $2,000 per victim. To protect their customers, banks and other financial institutions have made great efforts towards detecting these suspicious transactions, sometimes temporarily blocking them, conducting internal reviews, and contacting the account owner to validate the activity. However, this is an inherently adversarial task, as malicious actors adapt to whatever detection system the banks have in place. These playe ## Building a Real-Time AI Fraud Detection System with Spring Kafka and MongoDB DevFeed: [Building a Real-Time AI Fraud Detection System with Spring Kafka and MongoDB]() Original publisher: [Read original article]() Author: Tim Kelly Published: 2025-04-21T15:05:48Z Content type: tutorial Language: en Sources: [Nic Raboy]() Topics: [MongoDB](), [Tutorial](), [Kafka](), [Embeddings](), [real-time](), [Development]() Tags: [ai](), [anomaly-detection](), [artificial-intelligence](), [building](), [embeddings](), [financial](), [fraud-detection](), [java](), [kafka](), [mongodb](), [pipeline](), [real-time](), [scalable-architecture](), [synthetic](), [tutorial](), [vector-search]() ### AI overview This tutorial builds a real-time fraud detection pipeline with MongoDB Atlas Vector Search, Apache Kafka, AI-generated embeddings, and MongoDB Change Streams. It compares new financial transactions with a user's historical transactions and flags potential fraud when no similar transactions exist or similar transactions are already marked as fraudulent. ### Source excerpt In this tutorial, we'll build a real-time fraud detection system using MongoDB Atlas Vector Search, Apache Kafka, and AI-generated embeddings. We'll demonstrate how MongoDB Atlas Vector Search can be ... The post Building a Real-Time AI Fraud Detection System with Spring Kafka and MongoDB appeared first on DEV. ## FIPS-ing the Un-FIPS-able: Apache Spark DevFeed: [FIPS-ing the Un-FIPS-able: Apache Spark]() Original publisher: [Read original article]() Published: 2025-04-17T00:00:00Z Content type: article Language: en Sources: [Chainguard: Unchained]() Topics: [Apache Spark](), [chainguard](), [container images](), [Cryptography](), [data-processing](), [distributed-systems](), [Data Infrastructure](), [Cybersecurity]() Tags: [apache](), [apache-spark](), [chainguard](), [chainguard-containers](), [cloud-native](), [compliance](), [container-images](), [cryptography](), [cybersecurity](), [data-infrastructure](), [fedramp](), [fips](), [fraud-detection](), [open-source](), [real-time-data-streaming](), [recommendation-systems](), [spark](), [spark-operator]() ### AI overview Chainguard announces FIPS-validated container images for Apache Spark and Spark Operator, built entirely from source. The article explains the demand for FIPS-compatible Spark in regulated environments and describes the effort to overcome incompatibilities between Spark and FIPS-approved cryptographic libraries. ### Source excerpt Chainguard now offers FIPS-validated container images for Apache Spark and Spark Operator. See how we did it. ## KIP-932 explores queue semantics and Share Groups for Apache Kafka DevFeed: [KIP-932 explores queue semantics and Share Groups for Apache Kafka]() Original publisher: [Read original article]() Published: 2025-03-05T11:35:00Z Content type: opinion Language: en Sources: [Gunnar Morling]() Topics: [Kafka](), [Streaming](), [stream-processing](), [Architecture & Design]() Tags: [apache-kafka](), [fraud](), [fraud-detection](), [job](), [kafka](), [microservices](), [stream-processing](), [streaming]() ### AI overview The article examines KIP-932, which explores adding queue semantics to Apache Kafka through Share Groups. It explains how Kafka's partition-based consumer model limits parallelism and ordered processing, and why individual message acknowledgment and rejection are important for queueing workloads such as independent job processing. ### Source excerpt Table of Contents Towards Queue Support in Kafka--Introducing Share Groups Share Groups in Action Retry Behavior and State Management Share Group State Persistence Summary and Outlook In the "Let's Take a Look at...!" blog series I am going to explore interesting projects, developments and technologies in the data and streaming space. This can be KIPs and FLIPs, open-source projects, services, and more. The idea is to get some hands-on experience, learn about potential use cases and applications, and understand the trade-offs involved. If you think there's a specific subject I should take a look at, let me know in the comments below! That guy above? Yep, that's me, whenever someone says "Kafka queue". Because, that's not what Apache Kafka is. At its core, Kafka is a distributed durable event log. Producers write events to a topic, organized in partitions which are distributed amongst the brokers of a Kafka cluster. Consumers, organized in groups, divide the partitions they process amongst themselves, so that each partition of a topic is read by exactly one consumer in the group. ## Replay 2025 in London: Conference highlights on resilient backend systems DevFeed: [Replay 2025 in London: Conference highlights on resilient backend systems]() Original publisher: [Read original article]() Author: Lauren Bennett Published: 2025-01-29T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [systems](), [Back end](), [Cloud](), [Cloudflare]() Tags: [2025](), [backend](), [cloud](), [cloudflare](), [community](), [deployments](), [developer](), [event](), [fraud](), [fraud-detection](), [hackathon](), [infrastructure](), [london](), [productivity](), [real-time](), [resilience](), [scale](), [systems]() ### AI overview This promotional article invites developers to Replay 2025 in London and previews sessions on resilient, scalable backend systems. It highlights case studies involving Temporal Cloud from JPMorgan Chase, ZoomInfo, Salesforce, Cloudflare, and The Washington Post's Arc XP. ### Source excerpt Join Replay 2025 in London, March 3-5, to learn from industry leaders like Salesforce and Vodafone. Attend keynotes, hands-on workshops, and a hackathon focused on building resilient, scalable systems. Don't miss the developer event of the year! ## "Show Me What's Wrong!": Enhancing Fraud Detection Analysis by Combining Charts and Text DevFeed: ["Show Me What's Wrong!": Enhancing Fraud Detection Analysis by Combining Charts and Text]() Original publisher: [Read original article]() Author: Beatriz Feliciano Published: 2024-11-22T18:35:14Z Content type: article Language: en Sources: [Feedzai]() Topics: [Transactions](), [data](), [synthetic-data](), [Tool]() Tags: [data](), [data-analysis](), [data-visualization](), [financial-fraud](), [fraud](), [fraud-detection](), [fraud-investigation](), [image](), [interface](), [research](), [synthetic-data](), [transactions]() ### AI overview The article describes a fraud-analysis tool that combines charts and text to help analysts review suspicious financial transactions. It explains that tabular review can make it difficult to identify patterns and anomalies within a 1-to-5-minute review window, and presents an interface using synthetic data to help analysts prioritize investigation areas. ### Source excerpt Every year, millions of people fall victim to financial fraud. In 2023, the losses tied to this type of crime were estimated at US$159 billion just in the US, with some people losing all of their retirement savings to scammers. However, the impacts of this issue stretch beyond someone's finances. It can also impact a victim's life in many dimensions. Detecting and quickly acting upon suspicious transactions is essential to tackle this problem. Finding Fraud Through Data Tables To review the data of alerted transactions, analysts look at information in tabular format (similar to what is presented in Figure 1), scrolling through it to assess past activity patterns of the alerted person and comparing those with the alerted event. "How much money was spent on average on past transactions?" or "Is that significantly different from the amount on the current alert?" are some questions they might try to answer during their review. Figure 1: Image of a table that analysts typically use to review the data of alerted transactions. The issue with this approach is that finding groups of patterns and anomalies in tabular data can be overwhelming since it requires an increased cognitive load from analysts to interpret the data effectively. This becomes even more complex since these professionals must review and classify the alerted transaction in a short time -- between 1 and 5 minutes. Revamping the analysis To solve this problem, we present a tool that combines charts and text to guide the analysis of financial transactions. As presented in Figure 2, the tool (populated with synthetic data) is divided into three regions that provide different levels of information detail -- from the most high-level to the most detailed. The goal is that the analyst can scan the charts and prioritize their review towards specific areas of the alert. Figure 2: Proposed interface composed of multiple regions: the Knowledge Area Console (A) to detect suspicious areas of the analysis; the Knowledge Are ## Replay 2024 recap: Day 2 DevFeed: [Replay 2024 recap: Day 2]() Original publisher: [Read original article]() Author: Chris Kielkopf Published: 2024-09-20T04:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [Cloud](), [reliability](), [Microservice](), [Transactions]() Tags: [cloud](), [community](), [conference](), [debugging](), [fraud-detection](), [payment](), [recap](), [reliability](), [retry](), [services]() ### AI overview A recap of Day 2 of Temporal Replay 2024 covers the conference keynote, the Nexus feature for connecting durable executions across boundaries, and customer talks about using Temporal for cloud modernization, payment services, and order management. ### Source excerpt Get the top highlights and key takeaways from Day 2 of Temporal's annual community conference. ## 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 ## How to build a real-time fraud detection system DevFeed: [How to build a real-time fraud detection system]() Original publisher: [Read original article]() Author: Joe Karlsson Published: 2023-05-09T00:00:00Z Content type: tutorial Language: en Sources: [Tinybird]() Topics: [real-time](), [Streaming](), [data]() Tags: [analytics](), [build](), [data](), [fraud](), [fraud-detection](), [how-to](), [i-built-this](), [real-time](), [streaming]() ### AI overview A tutorial about building a real-time fraud detection system using Tinybird, streaming data, analytics, and instant alerts. ### Source excerpt Build a real-time fraud detection system using Tinybird. Stop fraud fast with streaming data, smart analytics, and instant alerts. ## Blockchain In Go: Part IV: Fraud Detection DevFeed: [Blockchain In Go: Part IV: Fraud Detection]() Original publisher: [Read original article]() Published: 2022-05-05T00:00:00Z Content type: tutorial Language: en Sources: [William Kennedy]() Topics: [Blockchain](), [audit trail](), [Go Language](), [Database](), [Code]() Tags: [audit-trail](), [blockchain](), [blockchain-signature-verification](), [build-blockchain-from-scratch](), [cryptographic-audit](), [digital-wallet](), [ecdsa](), [ethereum](), [fraud](), [fraud-detection](), [genesis-file](), [go](), [go-blockchain](), [golang-blockchain](), [hash](), [storage](), [verification]() ### AI overview This fourth post in a series explains how the Ardan blockchain detects forgery or changes to blocks and transaction data. It describes the cryptographic audit trail formed by block-header fields, including hashes linking each block to the previous one, so a node can verify its local blockchain copy and detect mismatches. ### Source excerpt Introduction In the first three posts, I explained there were four aspects of a blockchain that this series would explore with a backing implementation provided by the Ardan blockchain project. Digital accounts with electronic signatures and verification Transaction distribution and synchronization between computers Redundant storage and consensus by different computers Detection of any fraud to past transactions The first post focused on how the Ardan blockchain provides support for digital accounts, signatures, and verification. The second post focused on transaction distribution and synchronization between different computers. The third post, focused on how the Ardan blockchain handles consensus between different computers which results in the redundant storage of the blockchain database. In this fourth post, I will focus on how the Ardan blockchain can detect forgery or changes to the underlying blocks and transaction data. ## Shopify's Playbook for Scaling Machine Learning DevFeed: [Shopify's Playbook for Scaling Machine Learning]() Original publisher: [Read original article]() Author: Solmaz Shahalizadeh Published: 2022-02-11T19:30:01Z Content type: article Language: en Sources: [Shopify Engineering](), [Shopify Engineering - Shopify Engineering]() Topics: [Machine Learning & Artificial Intelligence](), [data](), [Shopify](), [Algorithm](), [Amazon S3]() Tags: [algorithm](), [building](), [data](), [fraud-detection](), [guide](), [machine-learning](), [organization](), [s3](), [shopify]() ### AI overview Shopify shares a pragmatic, technology-independent playbook for scaling machine learning across an organization. The guide emphasizes choosing a worthwhile user problem, understanding the business domain, and ensuring that high-quality, accessible data is available. Shopify illustrates these principles with its initial order fraud detection problem. ### Source excerpt Through our experience building our first few models, Shopify Data carved out a pragmatic step-by-step guide that has enabled us to successfully scale machine learning across our organization. [Next page]()