Systems | Development | Analytics | API | Testing

Real-Time Fraud Detection with Edge-to-AI | Cloudera Data in Motion Demo

Aug 17, 2026 By Cloudera, Inc. In Cloudera
Learn how to build an end-to-end, real-time edge-to-AI data pipeline to tackle critical enterprise challenges like credit card fraud detection. In this demo, Diby Malakar (Product Lead for Data in Motion) demonstrates how to process an average of 5,000 transactions per second in low hundreds of milliseconds to detect fraud instantly. Discover how Cloudera’s Data in Motion suite enables application developers to ingest, govern, and enrich streaming edge data to power instant AI model inference and live analytics.
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integrate

Real-Time Fraud Detection Pipelines: How Fintechs Use ETL for Streaming Data

Jul 7, 2026 By Donal Tobin In Integrate
Your fraud detection system analyzes yesterday's transactions while criminals steal millions today. Financial institutions lose an estimated $33 billion annually to card fraud alone, much of it preventable with real-time detection capabilities. Traditional batch processing that analyzes data hours or days after transactions occur simply cannot keep pace with sophisticated fraud schemes exploiting the settlement window gap.
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zymr

How AI Agents are Redefining Fraud Detection in Financial Services

Sep 22, 2025 By Harsh Raval In Zymr
It’s 2025, and fraudsters are no longer following yesterday’s rules. We’re seeing crypto scams, AI-driven phishing, and deepfake impersonations, making old-school manual detection look almost quaint. Let’s get real: financial crime is booming, and fast. Globally, fraud scams and bank schemes cost $485.6 billion in 2023 alone. In the U.S., consumers reported losing $12.5 billion to fraud in 2024, a 25% spike from 2023 (Federal Trade Commission).
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chaossearch

5 Ways to Use Log Analytics and Telemetry Data for Fraud Prevention

Apr 27, 2023 By David Bunting In ChaosSearch
As fraud continues to grow in prevalence, SecOps teams are increasingly investing in fraud prevention capabilities to protect themselves and their customers. One approach that’s proved reliable is the use of log analytics and telemetry data for fraud prevention. By collecting and analyzing data from various sources, including server logs, network traffic, and user behavior, enterprise SecOps teams can identify patterns and anomalies in real time that may indicate fraudulent activity.
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testlio

How to Prevent Authorized Push Payment Fraud with Mobile App Testing

Mar 3, 2023 By Angela Stephen In Testlio
This recent article from Finextra regarding the UK payments watchdog consulting on new authorized push payment (APP) fraud reporting rules has me thinking. How can we prevent fraud and protect consumers – and ourselves from scams? APP fraud happens when fraudsters deceive consumers or individuals into sending a payment under false pretenses to a bank account controlled by the fraudster. Real-time payment schemes are irrevocable, so victims cannot reverse a payment once sent.
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Fraud Detection with Cloudera Stream Processing

Aug 12, 2022 By Cloudera In Cloudera
This video shows how Cloudera DataFlow powered by Apache NiFi solves the first-mile problem by making it easy and efficient to acquire, transform, and move data so that we can enable streaming analytics use cases with very little effort. It will also briefly discuss the advantages of running this flow in a cloud-native Kubernetes deployment of Cloudera DataFlow. Then, we will explore how we can run real-time streaming analytics using Apache Flink, and we will use Cloudera SQL Stream Builder GUI to easily create streaming jobs using only SQL language (no Java/Scala coding required).
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cloudera

Fraud Detection With Cloudera Stream Processing Part 2: Real-Time Streaming Analytics

Jul 18, 2022 By André Araújo In Cloudera

In part 1 of this blog we discussed how Cloudera DataFlow for the Public Cloud (CDF-PC), the universal data distribution service powered by Apache NiFi, can make it easy to acquire data from wherever it originates and move it efficiently to make it available to other applications in a streaming fashion.

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cloudera

Fraud Detection with Cloudera Stream Processing Part 1

Jun 28, 2022 By André Araújo In Cloudera

In a previous blog of this series, Turning Streams Into Data Products, we talked about the increased need for reducing the latency between data generation/ingestion and producing analytical results and insights from this data. We discussed how Cloudera Stream Processing (CSP) with Apache Kafka and Apache Flink could be used to process this data in real time and at scale. In this blog we will show a real example of how that is done, looking at how we can use CSP to perform real-time fraud detection.

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