Join the data streaming community and stay informed about new blog posts by subscribing to my newsletter and follow me on LinkedIn or X (former Twitter) to stay in touch. And make sure to download my free book about data streaming use cases, including several success stories around gaming, loyalty platforms, and personalized advertising.

Fantasy Sports: Real-Time Gaming Meets Real-World Sports

Fantasy sports allows users to create virtual teams based on real-life athletes. As matches unfold, players earn points based on the performance of their selected athletes. The better the team performs, the higher the user’s score—and the bigger the prize.

Key characteristics of fantasy gaming:

• Multi-sport experience: Users can play across cricket, football, basketball, and more.
• Live interaction: Scoring is updated in real time as matches progress.
• Contests and leagues: Players join public or private contests, often with cash prizes.
• Peak traffic patterns: Most activity spikes in the minutes before a match begins.

This user behavior creates a unique business and technology challenge. Millions of users make critical decisions at the same time, just before the start of each game. The result: extreme concurrency, massive request volumes, and a hard dependency on data accuracy and low latency.

Real-time infrastructure isn’t optional in this model. It’s fundamental to user trust and business success.

Dream11: A Fantasy Sports Giant with Massive Scale

Founded in India, Dream11 is the largest fantasy sports platform in the country—and one of the biggest globally. With over 230 million users, it dominates fantasy gaming across cricket and 11 other sports. The platform sees traffic that rivals the world’s largest digital services.

Bipul Karnanit from Dream11 presented very interesting overview at Current 2025 in Bangalore India. Here are a few statistics about Dream11’s scale:

• 230M users
• 12 sports
• 12,000 matches/year
• 44TB data per day
• 15M+ peak concurrent users
• 43M+ peak transactions/day

During major events like the IPL, Dream11 experiences hockey-stick traffic curves, where tens of millions of users log in just minutes before a match begins—making lineup changes, joining contests, and waiting for live updates.

This creates a business-critical need for:

• Low latency
• Guaranteed data consistency
• Fault tolerance
• Real-time analytics and scoring
• High developer productivity to iterate fast

Apache Kafka at the Heart of Dream11’s Platform

To meet these demands, Dream11 uses Apache Kafka as the foundation of its real-time data infrastructure. Kafka powers the messaging between services that manage user actions, match scores, payouts, leaderboards, and more.

Apache Kafka enables:

• Event-driven microservices
• Scalable ingestion and processing of user and game data
• Loose coupling between systems with data products for operational and analytical consumers
• High throughput with guaranteed ordering and durability

Solving Kafka Consumer Challenges at Scale

As the business grew, Dream11’s engineering team encountered challenges with Kafka’s standard consumer APIs, particularly around rebalancing, offset management, and processing guarantees under peak load.

To address these issues, Dream11 built a custom Java-based Kafka consumer library—a foundational component of its internal platform that simplifies Kafka integration across services and boosts developer productivity.

Dream11 Kafka Consumer Library:

• Purpose: A custom-built Java library designed to handle high-volume Kafka message consumption at Dream11 scale.
• Key Benefit: Abstracts away low-level Kafka consumer details, simplifying tasks like offset management, error handling, and multi-threading, allowing developers to focus on business logic.
• Simple Interfaces: Provides easy-to-use interfaces for processing records.
• Increased Developer Productivity: Standardized library lead to faster development and fewer errors.

This library plays a crucial role in enabling real-time updates and ensuring seamless gameplay—even under the most demanding user scenarios.

For deeper technical insights, including how Dream11 decoupled polling and processing, implemented at-least-once delivery, and improved throughput with custom worker pools, watch the Dream11 engineering session from Current India 2025 presented by Bipul Karnanit.

Fantasy Sports, Real-Time Expectations, and Business Value

Dream11’s business success is built on user trust, real-time responsiveness, and high-quality gameplay. With millions of users relying on accurate, timely updates, the platform can’t afford downtime, data loss, or delays.

Data Streaming with Apache Kafka enables Dream11 to:

• React to user interactions instantly
• Deliver consistent data across microservices and devices
• Scale dynamically during live events
• Streamline the development and deployment of new features

This is not just a backend innovation—it’s a competitive advantage in a space where milliseconds matter and trust is everything.

Dream11’s Kafka Journey: The Backbone of Fantasy Sports at Scale

Fantasy sports is one of the most demanding environments for real-time data platforms. Dream11’s approach—scaling Apache Kafka to serve hundreds of millions of events with precision—is a powerful example of aligning architecture with business needs.

As more industries adopt event-driven systems, Dream11’s journey offers a clear message: Apache Kafka is not just a messaging layer—it’s a strategic platform for building reliable, low-latency digital experiences at scale.

Whether you’re in gaming, finance, telecom, or logistics, there’s much to learn from the way fantasy sports leaders like Dream11 harness data streaming to deliver world-class services.

Join the data streaming community and stay informed about new blog posts by subscribing to my newsletter and follow me on LinkedIn or X (former Twitter) to stay in touch. And make sure to download my free book about data streaming use cases, including several success stories around gaming, loyalty platforms, and personalized advertising.

The post Powering Fantasy Sports at Scale: How Dream11 Uses Apache Kafka for Real-Time Gaming appeared first on Kai Waehner.

Join the data streaming community and stay informed about new blog posts by subscribing to my newsletter and follow me on LinkedIn or X (former Twitter) to stay in touch. And make sure to download my free book about data streaming use cases.

The Business of Mobility Services (Ride-Hailing, Food Delivery, Taxi Aggregators, etc.)

Mobility services have become an essential part of modern urban life. They offer convenience and efficiency through ride-hailing, food delivery, car-sharing, e-scooters, taxi aggregators, and micro-mobility options. Companies such as Uber, Lyft, FREE NOW (former MyTaxi; acquired by Lyft recently), Grab, Careem, and DoorDash connect millions of passengers, drivers, restaurants, retailers, and logistics partners to enable seamless transactions through digital platforms.

These platforms operate in highly dynamic environments where real-time data is crucial for pricing, route optimization, customer experience, and fraud detection. However, this very nature of mobility services also makes them prime targets for fraudulent activities. Fraud in this sector can lead to financial losses, reputational damage, and deteriorating customer trust.

To effectively combat fraud, mobility services must rely on real-time data streaming with technologies such as Apache Kafka and Apache Flink. These technologies enable continuous event processing and allow platforms to detect and prevent fraud before transactions are finalized.

Why Fraud is a Major Challenge in Mobility Services

Fraudsters continually exploit weaknesses in digital mobility platforms. Some of the most common fraud types include:

1. Fake Rides and GPS Spoofing: Drivers manipulate GPS data to simulate trips that never occurred. Passengers use location spoofing to receive cheaper fares or exploit promotions.

2. Payment Fraud and Stolen Credit Cards: Fraudsters use stolen payment methods to book rides or order food.

3. Fake Drivers and Passengers: Fraudsters create multiple accounts and pretend to be both the driver and passenger to collect incentives. Some drivers manipulate fares by manually adjusting distances in their favor.

4. Promo Abuse: Users create multiple fake accounts to exploit referral bonuses and promo discounts.

5. Account Takeovers and Identity Fraud: Hackers gain access to legitimate accounts, misusing stored payment information. Fraudsters use fake identities to bypass security measures.

Fraud not only impacts revenue but also creates risks for legitimate users and drivers. Without proper fraud prevention measures, ride-hailing and delivery companies could face serious losses, both financially and operationally.

The Unseen Enemy: Core Challenges in Mobility Fraud
Detection

Traditional fraud detection relies on batch processing and manual rule-based systems. However, these approaches are no longer effective due to the speed and complexity of modern mobile apps with real-time experiences combined with modern fraud schemes.

Key challenges in mobility fraud detection include:

• Fraud occurs in real-time, requiring instant detection and prevention before transactions are completed.
• Millions of events per second must be processed, requiring scalable and efficient systems.
• Fraud patterns constantly evolve, making static rule-based approaches ineffective.
• Platforms operate across hybrid and multi-cloud environments, requiring seamless integration of fraud detection systems.

How Data Streaming with Apache Kafka and Flink Enables Real-Time Fraud Detection

To overcome these challenges, real-time streaming analytics powered by Apache Kafka and Apache Flink provide an effective solution.

Apache Kafka: The Backbone of Event-Driven Fraud Detection

Kafka serves as the core event streaming platform. It captures and processes real-time data from multiple sources such as:

• GPS location data
• Payment transactions
• User and driver behavior analytics
• Device fingerprints and network metadata

Kafka provides:

• High-throughput data streaming, capable of processing millions of events per second to support real-time decision-making.
• An event-driven architecture that enables decoupled, flexible systems—ideal for scalable and maintainable mobility platforms.
• Seamless scalability across hybrid and multi-cloud environments to meet growing demand and regional expansion.
• Always-on reliability, ensuring 24/7 data availability and consistency for mission-critical services such as fraud detection, pricing, and trip orchestration.

An excellent success story about the transition to data streaming comes from DoorDash: Why DoorDash migrated from Cloud-native Amazon SQS and Kinesis to Apache Kafka and Flink.

Apache Flink: Continuous Stream Processing for Fraud Detection in Real-Time

Apache Flink enables real-time fraud detection through advanced event correlation and applied AI:

• Detects anomalies in GPS data, such as sudden jumps, route manipulation, or unrealistic movement patterns.
• Analyzes historical user behavior to surface signs of account takeovers or other forms of identity misuse.
• Joins multiple real-time streams—including payment events, location updates, and account interactions—to generate accurate, low-latency fraud scores.
• Applies machine learning models in-stream, enabling the system to flag and stop suspicious transactions before they are processed.
• Continuously adapts to new fraud patterns, updating models with fresh data in near real-time to reflect evolving user behavior and emerging threats.

With Kafka and Flink, fraud detection can shift from reactive to proactive to stop fraudulent transactions before they are completed.

I already covered various data streaming success stories from financial services companies such as Paypal, Capital One and ING Bank in a dedicated blog post. And a separate case study from about “Fraud Prevention in Under 60 Seconds with Apache Kafka: How A Bank in Thailand is Leading the Charge“.

Real-World Fraud Prevention Stories from Mobility Leaders

Fraud is not just a technical issue—it’s a business-critical challenge that impacts trust, revenue, and operational stability in mobility services. The following real-world examples from industry leaders like FREE NOW (Lyft), Grab, and Uber show how data streaming with advanced stream processing and AI are used around the world to detect and stop fraud in real time, at massive scale.

FREE NOW (Lyft): Detecting Fraudulent Trips in Real Time by Analyzing GPS Data of Cars

FREE NOW operates in more than 150 cities across Europe with 48 million users. It integrates multiple mobility services, including taxis, private vehicles, car-sharing, e-scooters, and bikes.

The company was recently acquired by Lyft, the U.S.-based ride-hailing giant known for its focus on multimodal urban transport and strong presence in North America. This acquisition marks Lyft’s strategic entry into the European mobility ecosystem, expanding its footprint beyond the U.S. and Canada.

Fraud Prevention Approach leveraging Data Streaming (presented at Kafka Summit)

• Uses Kafka Streams and Kafka Connect to analyze GPS trip data in real-time.
• Deploys fraud detection models that identify anomalies in trip routes and fare calculations.
• Operates data streaming on fully managed Confluent Cloud and applications on Kubernetes for scalable fraud detection.

Example: Detecting Fake Rides

1. A driver inputs trip details into the app.
2. Kafka Streams predicts expected trip fare based on distance and duration.
3. GPS anomalies and unexpected route changes are flagged.
4. Fraud alerts are triggered for suspicious transactions.

By implementing real-time fraud detection with Kafka and Flink, FREE NOW (Lyft) has significantly reduced fraudulent trips and improved platform security.

Grab: AI-Powered Fraud Detection for Ride-Hailing and Delivery with Data Streaming and AI/ML

Grab is a leading mobility platform in Southeast Asia, handling millions of transactions daily. Fraud accounts for 1.6 percent of total revenue loss in the region.

To address these significant fraud numbers, Grab developed GrabDefence—an AI-powered fraud detection engine that leverages real-time data and machine learning to detect and block suspicious activity across its platform.

Fraud Detection Approach

• Uses Kafka Streams and machine learning for fraud risk scoring.
• Leverages Flink for feature aggregation and anomaly detection.
• Detects fraudulent transactions before they are completed.

Example: Fake Driver and Passenger Fraud

1. Fraudsters create accounts as both driver and passenger to claim rewards.
2. Kafka ingests device fingerprints, payment transactions, and ride data.
3. Flink aggregates historical fraud behavior and assigns risk scores.
4. High-risk transactions are blocked instantly.

With GrabDefence built with data streaming, Grab reduced fraud rates to 0.2 percent, well below the industry average. Learn more about GrabDefence in the Kafka Summit talk.

Uber: Project RADAR – AI-Powered Fraud Detection with Human Oversight

Uber processes millions of payments per second globally. Fraud detection is complex due to chargebacks and uncollected payments.

To combat this, Uber launched Project RADAR—a hybrid system that combines machine learning with human reviewers to continuously detect, investigate, and adapt to evolving fraud patterns in near real time. Low latency is not required in this scenario. And humans are in the loop of the business process. Hence, Apache Spark is sufficient for Uber.

Fraud Prevention Approach

• Uses Kafka and Spark for multi-layered fraud detection.
• Implements machine learning models to detect chargeback fraud.
• Incorporates human analysts for rule validation.

Example: Chargeback Fraud Detection

1. Kafka collects all ride transactions in real time.
2. Stream processing detects anomalies in payment patterns and disputes.
3. AI-based fraud scoring identifies high-risk transactions.
4. Uber’s RADAR system allows human analysts to validate fraud alerts.

Uber’s combination of AI-driven detection and human oversight has significantly reduced chargeback-related fraud.

Data Streaming with Kafka and Flink Provides Real-Time Fraud Detection in Mobility Services

Fraud in mobility services is a real-time challenge that requires real-time solutions that work 24/7, even at extreme scale for millions of events. Traditional batch processing systems are too slow, and static rule-based approaches cannot keep up with evolving fraud tactics.

By leveraging data streaming with Apache Kafka in conjunction with Kafka Streams or Apache Flink, mobility platforms can:

• Process millions of events per second to detect fraud in real time.
• Prevent fraudulent transactions before they occur.
• Use AI-driven real-time fraud scoring for accurate risk assessment.
• Adapt dynamically through continuous learning to evolving fraud patterns.

Mobility platforms such as Uber, Grab, and FREE NOW (Lyft) are leading the way in using real-time streaming analytics to protect their platforms from fraud. By implementing similar approaches, other mobility businesses can enhance security, reduce financial losses, and maintain customer trust.

Real-time fraud prevention in mobility services is not an option; it is a necessity. The ability to detect and stop fraud in real time will define the future success of ride-hailing, food delivery, and urban mobility platforms.

Stay ahead of the curve! Subscribe to my newsletter for insights into data streaming and connect with me on LinkedIn to continue the conversation. And download my free book about data streaming use cases.

The post Fraud Detection in Mobility Services (Ride-Hailing, Food Delivery) with Data Streaming using Apache Kafka and Flink appeared first on Kai Waehner.

Join the data streaming community and stay informed about new blog posts by subscribing to my newsletter and follow me on LinkedIn or X (former Twitter) to stay in touch. And make sure to download my free book about data streaming use cases across all industries.

Data Streaming in the FinServ Industry

This article builds on FinTechMagazine.com’s “Top 10 Emerging Technologies in Finance“ by mapping each of these innovations to real-time data streaming concepts, possibilities, and real-world success stories.

By leveraging Apache Kafka and Apache Flink, financial institutions can process transactions instantly, detect fraud proactively, and enhance customer experiences with real-time insights. Each emerging technology—whether IoT payment networks, AI-powered banking, or embedded finance—relies on the ability to stream, analyze, and act on data in real time, making data streaming a foundational enabler of the future of finance.

10. IoT Payment Networks: Real-time Processing for Seamless Payments

IoT payment networks enable automated, contactless transactions using connected devices like smartwatches, cars, and home appliances. Whether it’s a fridge restocking groceries or a car paying for tolls, these interactions generate massive real-time data streams that must be processed instantly and securely.

How Data Streaming with Kafka and Flink Adds Value

• Fraud Detection in Milliseconds – Flink analyzes streaming transaction data to detect anomalies, flagging fraudulent activity before payments are approved.
• Reliable Connectivity – Kafka ensures payment events from IoT devices are securely transmitted and processed, preventing dropped or duplicate transactions.
• Dynamic Pricing & Offers – Flink processes sensor and market data to adjust prices dynamically (e.g., surge pricing for EV charging stations) and deliver real-time personalized discounts.
• Edge Processing for Low-Latency Payments – Kafka enables local transaction validation on IoT devices, reducing lag in autonomous vehicle payments and retail checkout systems.
• Compliance & Security – Streaming pipelines support real-time monitoring, encryption, and anomaly detection, ensuring IoT payments meet financial regulations like PSD2 and PCI DSS.

In financial services, don’t make the mistake of only looking inward for lessons—other industries have been solving similar challenges for years. Consumer IoT and Apache Kafka have long been used together in sectors like retail, where real-time data integration is critical for unified commerce, rewards programs, social selling, and many other use cases.

9. Voice-First Banking: Turning Conversations into Transactions

Voice-first banking enables customers to interact with financial services using smart speakers, virtual assistants, and mobile voice recognition. Whether checking an account balance, making a payment, or applying for a loan, these interactions require instant access to multiple backend systems—from core banking and CRM to fraud detection and credit scoring systems.

To make voice banking seamless, fast, and secure, banks must integrate real-time data streaming between AI-powered voice assistants and backend financial systems. This is where Apache Kafka and Apache Flink come in.

How Data Streaming with Kafka and Flink Adds Value

• Seamless Integration Across Banking Systems – Voice assistants need real-time access to core banking (account balances, transactions), CRM (customer history), risk systems (fraud checks), and AI analytics. Kafka acts as a high-speed messaging and integration layer (aka ESB/middleware), ensuring that voice requests are instantly routed to the right backend services (including legacy technologies, such as mainframe) and responses are processed in milliseconds.
• Instant Voice Query Processing – When a customer asks, “What’s my balance?”, Flink streams real-time transaction data from Kafka to retrieve the latest balance, rather than relying on outdated batch data.
• Secure Authentication & Fraud Detection – Streaming pipelines analyze voice patterns in real time to detect fraud and trigger multi-factor authentication (MFA) if needed.
• Personalized & Context-Aware Banking and Advertising – Flink continuously enriches customer profiles by analyzing past transactions, spending habits, and preferences—allowing the system to offer real-time financial insights (e.g., suggesting a savings plan based on spending trends).
• Asynchronous Processing for Long-Running Requests – For complex tasks like loan applications, Kafka handles asynchronous processing—initiating background workflows across multiple systems while keeping the customer engaged.

For instance, Northwestern Mutual presented at Kafka Summit how the bank leverages Apache Kafka as a database for real-time transaction processing.

8. Autonomous Finance Platforms: AI-Driven Financial Decision Making

Autonomous finance platforms use AI, machine learning, and multi-agent systems to optimize savings, investments, and budgeting for consumers. These platforms act as digital financial advisors to make real-time decisions based on market data, user spending habits, and risk models.

How Data Streaming with Kafka and Flink Adds Value

• Multi-Agent AI System Coordination – Autonomous finance platforms use multiple AI agents to handle different aspects of financial decision-making (e.g., portfolio optimization, credit assessment, fraud detection). Kafka streams data between these AI agents, ensuring they can collaborate in real time to refine investment and savings strategies.
• Streaming Market Data Integration – Kafka ingests live stock prices, interest rates, and macroeconomic data, making it instantly available for AI models to adjust financial strategies.
• Real-Time Customer Insights – Flink continuously analyzes customer transactions and spending behavior to enable AI-driven recommendations (e.g., automatically moving surplus funds into an interest-bearing account).
• Predictive Portfolio Management – By combining real-time stock market data with AI-driven risk models, Flink helps adjust portfolio allocations based on current trends, ensuring maximum returns while minimizing exposure.
• Automated Risk Mitigation – Autonomous finance systems must react instantly to market shifts. Flink’s real-time monitoring detects economic downturns or sudden market crashes, triggering immediate adjustments to investment portfolios or loan interest rates.
• Event-Driven Financial Automation – Kafka enables real-time triggers (e.g., an AI agent detecting high inflation can automatically adjust a savings strategy).

7. RegTech 3.0: Automating Compliance and Risk Monitoring

RegTech is modernizing compliance by replacing slow batch audits with continuous real-time monitoring, automated reporting, and proactive fraud detection.

Financial institutions need instant insights into transactions, risk exposure, and regulatory changes—Kafka and Flink make this possible by streaming, analyzing, and automating compliance at scale.

How Data Streaming with Kafka and Flink Adds Value

• Continuous Transaction Monitoring – Kafka streams every transaction in real time, enabling Flink to detect fraud, money laundering, or unusual patterns instantly—ensuring compliance with AML and KYC regulations.
• Automated Regulatory Reporting – Flink processes compliance events as they happen, ensuring regulatory bodies receive up-to-date reports without delays. Kafka integrates compliance data across banking systems for audit-ready records.
• Real-Time Fraud Prevention – Flink analyzes transaction behavior in milliseconds, detecting anomalies and triggering security actions like transaction blocking or multi-factor authentication.
• Event-Driven Compliance Alerts – Kafka ensures instant alerts when regulations change, allowing banks to adapt in real time instead of relying on manual updates.
• Proactive Risk Management – By analyzing live risk factors across transactions, users, and markets, Flink helps financial institutions identify and prevent compliance violations before they occur.

For example, KOR leverages data streaming to revolutionize compliance and regulatory reporting in the derivatives market by enabling on-demand historical reporting and real-time insights that were previously difficult to achieve with traditional batch processing. By using Kafka as a persistent state store, KOR ensures an immutable log of data that allows regulators to track changes over time, reconcile historical corrections, and meet compliance requirements more efficiently than legacy ETL-based big data systems. Read the entire KOR success story in my ebook.

6. Central Bank Digital Currencies (CBDC): The Future of Government-Backed Digital Money

Central Bank Digital Currencies (CBDC) are digital versions of national currencies, designed to enable faster, more secure, and highly scalable financial transactions.

Unlike cryptocurrencies, CBDCs are government-backed, meaning they require robust, real-time infrastructure capable of handling millions of transactions per second. They also need instant settlement, fraud detection, and cross-border interoperability—all of which depend on real-time data streaming.

How Data Streaming with Kafka and Flink Adds Value

• Instant Settlement – Kafka ensures that CBDC transactions are processed and confirmed in real time, eliminating delays in digital payments. This allows central banks to enable 24/7 instant transactions, even in cross-border scenarios.
• Scalability for Nationwide Adoption – Flink dynamically processes millions of transactions per second, ensuring that a CBDC system can handle high demand without bottlenecks or downtime.
• Cross-Border Payments & Exchange Rate Optimization – Flink analyzes foreign exchange markets in real time and ensures optimized B2B data exchange for currency conversion and detecting suspicious cross-border activities for fraud prevention.
• Regulatory Monitoring & Compliance – Kafka continuously streams transaction data to regulatory bodies. This ensures governments have real-time visibility into the movement of digital currencies.

At Kafka Summit Bangalore 2024, Mindgate Solutions presented its successful integration of Central Bank Digital Currency (CBDC) into banking apps, leveraging real-time data streaming to enable seamless digital payments. Mindgate utilized Kafka-based microservices architecture to ensure scalability, security, and reliability, reinforcing its leadership in India’s real-time payments ecosystem while processing over 8 billion transactions per month.

5. Green Fintech Infrastructure: Sustainability and ESG in Finance

Green fintech focuses on tracking carbon footprints, ESG (Environmental, Social, and Governance) investments, and climate risks in real time.

As financial institutions shift towards sustainable investment strategies, they need accurate, real-time data on environmental impact, regulatory compliance, and green investment opportunities.

How Data Streaming with Kafka and Flink Adds Value

• Real-Time Carbon Tracking – Kafka streams emissions and sustainability data from supply chains to enable instant carbon footprint analysis.
• Automated ESG Compliance – Flink analyzes sustainability reports and investment portfolios, automatically flagging non-compliant companies or assets.
• Green Investment Insights – Real-time analytics match investors with eco-friendly projects, funds, and companies, helping financial institutions promote sustainable investments.

More details about optimizing the ESG footprint with data streaming: “Green Data, Clean Insights: How Kafka and Flink Power ESG Transformations“.

4. AI-Powered Personalized Banking: Hyper-Personalized Customer Experiences

AI-driven banking solutions are transforming how customers interact with financial institutions to provide real-time insights, spending recommendations, and fraud alerts based on user behavior.

How Data Streaming with Kafka and Flink Adds Value

• Real-Time Spending Analysis – Flink continuously processes live transaction data, identifying spending patterns to provide instant budgeting recommendations.
• Personalized Alerts & Recommendations – Kafka streams transaction events to banking apps, notifying users of unusual spending, low balances, or savings opportunities.
• Automated Financial Planning – Flink enables AI-driven financial assistance, helping users optimize savings, credit usage, and investments based on real-time insights.

A good example is how Erste Group Bank modernized its mobile banking experience with a hyper-personalized approach to ensure that customers receive tailored financial insights while prioritizing data consistency over real-time updates. By offloading data from expensive mainframes to a cloud-native, microservices-driven architecture, Erste Group Bank reduced costs, maintained compliance, and improved operational efficiency—ensuring a seamless flow of consistent, high-quality data across its legacy and modern banking applications. Read the entire Erste Group Bank success story in my ebook.

3. Decentralized Identity Solutions: Secure Identity Without Central Authorities

Decentralized identity solutions allow users to control their personal data, eliminating the need for centralized databases that are vulnerable to hacks. These systems use blockchain and zero-knowledge proofs for secure, passwordless authentication, but require real-time verification and fraud prevention measures.

How Data Streaming with Kafka and Flink Adds Value

• Cybersecurity in Real Time – Kafka streams biometric and identity verification data to fraud detection engines, ensuring instant risk analysis.
• Passwordless Authentication – Kafka integrates blockchain and biometric authentication to enable real-time identity validation without traditional passwords.
• Secure KYC (Know Your Customer) Processing – Flink processes identity verification requests instantly, ensuring faster onboarding and fraud-proof financial transactions.

2. Quantum-Resistant Cryptography: Securing Financial Data in the Quantum Era

Quantum computing poses a major risk to traditional encryption methods, requiring financial institutions to adopt post-quantum cryptography to secure sensitive financial transactions and user data.

How Data Streaming with Kafka and Flink Adds Value

• Scalable Cryptographic Upgrades – Streaming data pipelines allow banks to deploy cryptographic updates instantly, ensuring financial systems remain secure without downtime.
• Threat Detection & Security Analysis – Flink analyzes live transaction patterns to identify potential vulnerabilities in encryption algorithms before they are exploited.

Nobody knows where quantum computing goes. Frankly, this is the only section of the top 10 finance innovations where I am not sure how much data streaming will be able to help or if completely new paradigms come up.

1. Embedded Finance: Banking Services in Every Digital Experience

Embedded finance integrates banking, payments, lending, and insurance into non-financial platforms, allowing companies like Uber, Shopify, and Apple to offer seamless financial services within their ecosystems.

To function smoothly, embedded finance requires real-time data integration between payment processors, credit scoring systems, fraud detection tools, and regulatory bodies.

How Data Streaming with Kafka and Flink Adds Value

• Instant Payments & Transactions – Kafka streams payment data in real time, enabling seamless in-app purchases and instant money transfers.
• Real-Time Credit Scoring & Lending – Flink analyzes transaction histories to provide instant credit approvals for loans and BNPL (Buy Now, Pay Later) services.
• Fraud Prevention & Compliance – Streaming analytics detect suspicious behavior in real time, ensuring secure embedded financial transactions.

Tech giants like Uber and Shopify have embedded financial services directly into their platforms using event-driven architectures powered by Kafka, enabling real-time payments, lending, and fraud detection. By integrating finance seamlessly into their ecosystems, these companies enhance customer experience, create new revenue streams, and redefine how consumers interact with financial services.

Just like Uber and Shopify use event-driven architectures for real-time payments and financial services, Stripe and many similar FinTech companies power embedded finance for businesses by providing seamless, scalable payment infrastructure. To ensure six-nines (99.9999%) availability, Stripe relies on Apache Kafka as its financial source of truth to enable ultra-reliable transaction processing and real-time financial insights.

The Future of FinServ Is Real-Time: Are You Ready for Data Streaming?

The future of finance is real-time, intelligent, and seamlessly integrated into digital ecosystems. The ability to process massive amounts of financial data instantly is no longer optional—it’s a competitive necessity for operational and analytical use cases.

Data streaming with Apache Kafka and Apache Flink provides the foundation for scalability, security, and real-time analytics that modern financial services demand. By embracing data streaming, financial institutions can deliver:

• Faster transactions
• Proactive fraud prevention
• Better customer experiences
• Regulatory compliance

Finance is evolving from batch processing to real-time intelligence—and the companies that adopt streaming-first architectures will lead the industry into the future.

How do you leverage data streaming with Kafka and Flink in financial services? Let’s discuss on LinkedIn or X (former Twitter). Also join the data streaming community and stay informed about new blog posts by subscribing to my newsletter and to stay in touch. And make sure to download my free book about data streaming use cases across all industries.

The post How Data Streaming with Apache Kafka and Flink Drives the Top 10 Innovations in FinServ appeared first on Kai Waehner.

Fraud Prevention with Data Streaming using Apache Kafka and Flink

Fraud detection has become a critical focus across industries as digital transactions continue to rise, bringing with them increased opportunities for fraudulent activities. Traditional methods of fraud detection, often reliant on batch processing, struggle to keep pace with the speed and sophistication of modern scams. Data streaming offers a transformative solution to enable real-time analysis and immediate response to suspicious activities.

Data streaming technologies such as Apache Kafka and Flink enable businesses to continuously monitor transactions, identify anomalies, and prevent fraud before it affects customers. This shift to real-time fraud detection not only enhances security, but also builds trust and confidence among consumers.

I already explored “Fraud Detection with Apache Kafka, KSQL and Apache Flink” in its own blog post covering case studies across industries from companies such as Paypal, Capital One, ING Bank, Grab, and Kakao Games. And another blog post focusing on “Apache Kafka in Crypto and Financial Services for Cybersecurity and Fraud Detection“.

Kafka is an excellent foundation for fraud prevention and many other use cases across all industries. If you wonder when to choose Apache Flink or Kafka Streams for stream processing, I also got you covered.

Apache Kafka for Fraud Prevention at Krungsri Bank

Krungsri, also known as the Bank of Ayudhya, is one of Thailand’s largest banks. The company offers a range of financial services including personal and business banking, loans, credit cards, insurance, investment solutions, and wealth management.

I had the pleasure to do a panel conversation with Tul Roteseree, Executive Vice President and Head of the Data and Analytics Division from Krungsri at Confluent’s Data in Motion Tour 2024 in Bangkok, Thailand.

One of the most pressing concern for Krungsri is fraud prevention. In today’s digital landscape, scammers often trick consumers into transferring money to mule accounts within a mere 60 seconds. The bank’s data streaming platform allows analyzing payment transactions in real-time, detecting and blocking fraudulent activities before they can affect customers.

While fraud prevention is a primary focus, the bank’s data streaming initiatives encompass a range of use cases that enhance its overall operations. One of the other strategic areas is mainframe offloading. This involves transitioning data from legacy systems to more agile, real-time platforms. This shift not only reduces operational costs but also improves data accessibility and processing speed.

Another critical use case is the enhancement of customer notifications through the bank’s mobile app. By moving from batch processing to real-time data streaming, the bank can provide instant account movement alerts, keeping customers informed and engaged.

The Business Value of Data Streaming with Apache Kafka for Fraud Prevention

Krungsri bank’s decision to adopt data streaming is driven by the need for an event-driven architecture that can handle high-throughput data streams efficiently. Apache Kafka, the leading open source data streaming framework for building real-time data pipelines, was chosen for its scalability and reliability. Kafka’s ability to process vast amounts of data in real-time makes it an ideal choice for the bank’s fraud prevention efforts.

Confluent, a trusted provider of Kafka-based solutions, was selected for its stability and proven track record. The bank valued Confluent’s ability to deliver significant cost savings and speed up project timelines. By leveraging Confluent, the bank reduced its project go-live time from 4-6 months to just 6-8 weeks, ensuring a faster time to market.

Compliance is another critical factor: The bank’s operations are regulated by the Bank of Thailand. The data streaming architecture meets stringent regulatory requirements while ensuring data security and privacy.

From Mainframe to Hybrid Cloud at Krungsri Bank with Change Data Capture (CDC)

The bank’s data streaming architecture is built on a hybrid environment with core banking operations on-premises and mobile applications in the cloud. This setup provides the flexibility needed to adapt to changing business needs and regulatory landscapes.

Data ingestion and transformation occur across various environments, including cloud-to-cloud, cloud-to-on-premise, and on-premise-to-cloud. IBM’s Change Data Capture (CDC) technology is used for data capture. The data streaming platform acts as the intermediary between the mainframe and consumer applications. This “subscribe once, publish many” approach significantly reduces the mainframe’s burden, cutting costs and processing time.

Stream processing is a key component of the bank’s architecture, serving as the primary tool for real-time data transformations and analytics. This capability allows the bank to respond swiftly to emerging trends and threats. The continuous processing of data ensures that fraudulent activities are detected and blocked in under 60 seconds.

The bank’s move to the cloud also facilitates the integration of machine learning and AI models. The cloud transition enables more sophisticated data analysis and personalized services. Events generated through stream processing trigger AI models in the cloud to provide insights that drive decision-making and enhance customer experiences.

Fraud Detection with Stream Processing in Under 60 Seconds

In the fight against fraud, time is of the essence. By leveraging a data streaming platform, one of Thailand’s largest banks is setting a new standard for fraud prevention and ensures that payment transactions are continuously analyzed and blocked in under 60 seconds. With a robust event-driven architecture built on Kafka and Confluent, the bank is not only protecting its customers but also paving the way for a more secure and efficient financial future.

Do you also leverage data streaming for fraud prevention or any other critical use cases? Let’s connect on LinkedIn and discuss it! Stay informed about new blog posts by subscribing to my newsletter.

The post Fraud Prevention in Under 60 Seconds with Apache Kafka: How A Bank in Thailand is Leading the Charge appeared first on Kai Waehner.

Fraud detection and the need for real-time data

Fraud detection and prevention is the adequate response to fraudulent activities in companies (like fraud, embezzlement, and loss of assets because of employee actions).

An anti-fraud management system (AFMS) comprises fraud auditing, prevention, and detection tasks. Larger companies use it as a company-wide system to prevent, detect, and adequately respond to fraudulent activities. These distinct elements are interconnected or exist independently. An integrated solution is usually more effective if the architecture considers the interdependencies during planning.

Real-time data beats slow data across business domains and industries in almost all use cases. But there are few better examples than fraud prevention and fraud detection. It is not helpful to detect fraud in your data warehouse or data lake after hours or even minutes, as the money is already lost. This “too late architecture” increases risk, revenue loss, and lousy customer experience.

It is no surprise that most modern payment platforms and anti-fraud management systems implement real-time capabilities with streaming analytics technologies for these transactional and analytical workloads. The Kappa architecture powered by Apache Kafka became the de facto standard replacing the Lambda architecture.

A stream processing example in payments

Stream processing is the foundation for implementing fraud detection and prevention while the data is in motion (and relevant) instead of just storing data at rest for analytics (too late).

No matter what modern stream processing technology you choose (e.g., Kafka Streams, KSQL, Apache Flink), it enables continuous real-time processing and correlation of different data sets. Often, the combination of real-time and historical data helps find the right insights and correlations to detect fraud with a high probability.

Let’s look at a few examples of stateless and stateful stream processing for real-time data correlation with the Kafka-native tools Kafka Streams and ksqlDB. Similarly, Apache Flink or other stream processing engines can be combined with the Kafka data stream. It always has pros and cons. While Flink might be the better fit for some projects, it is another engine and infrastructure you need to combine with Kafka.

Ensure you understand your end-to-end SLAs and requirements regarding latency, exactly-once semantics, potential data loss, etc. Then use the right combination of tools for the job.

Stateless transaction monitoring with Kafka Streams

A Kafka Streams application, written in Java, processes each payment event in a stateless fashion one by one:

Stateful anomaly detection with Kafka and KSQL

A ksqlDB application, written with SQL code, continuously analyses the transactions of the last hour per customer ID to identify malicious behavior:

Kafka and Machine Learning with TensorFlow for real-time scoring for fraud detection

A KSQL UDF (user-defined function) embeds an analytic model trained with TensorFlow for real-time fraud prevention:

Case studies across industries

Several case studies exist for fraud detection with Kafka. It is usually combined with stream processing technologies, such as Kafka Streams, KSQL, and Apache Flink. Here are a few real-world deployments across industries, including financial services, gaming, and mobility services:

– Paypal processes billions of messages with Kafka for fraud detection.

– Capital One looks at events as running its entire business (powered by Confluent), where stream processing prevents $150 of fraud per customer on average per year by preventing personally identifiable information (PII) violations of in-flight transactions.

– ING Bank started many years ago by implementing real-time fraud detection with Kafka, Flink, and embedded analytic models

– Grab is a mobility service in Asia that leverages fully managed Confluent Cloud, Kafka Streams, and ML for stateful stream processing in its internal GrabDefence SaaS service.

– Kakao Games, a South-Korean gaming company uses data streaming to detect and operate anomalies with 300+ patterns through KSQL

Let’s explore the latter case study in more detail.

Deep dive into fraud prevention with Kafka and KSQL in mobile gaming

Kakao Games is a South Korea-based global video game publisher specializing in games across various genres for PC, mobile, and VR platforms. The company presented at Current 2022 – The Next Generation of Kafka Summit in Austin, Texas.

Here is a detailed summary of their compelling use case and architecture for fraud detection with Kafka and KSQL.

Use case: Detect malicious behavior by gamers in real-time

The challenge is evident when you understand the company’s history: Kakao Games has many outsourced games purchased via third-party game studios. Each game has its unique log with its standard structure and message format. Reliable real-time data integration at scale is required as a foundation for analytical business processes like fraud detection.

The goal is to analyze game logs and telemetry data in real-time. This capability is critical for preventing and remediating threats or suspicious actions from users.

Architecture: Change data capture and streaming analytics for fraud prevention

The Confluent-powered event streaming platform supports game log standardization. ksqlDB analyzes the incoming telemetry data for in-game abuse and anomaly detection.

Implementation: SQL recipes for data streaming with KSQL

Kakao Games detects anomalies and prevents fraud with 300+ patterns through KSQL. Use cases include bonus abuse, multiple account usage, account takeover, chargeback fraud, and affiliate fraud.

Here are a few code examples written with SQL code using KSQL:

Results: Reduced risk and improved customer experience

Kakao Games can do real-time data tracking and analysis at scale. Business benefits are faster time to market, increased active users, and more revenue thanks to a better gaming experience.

Fraud detection only works in real-time

Ingesting data with Kafka into a data warehouse or a data lake is only part of a good enterprise architecture. Tools like Apache Spark, Databricks, Snowflake, or Google BigQuery enable finding insights within historical data. But real-time fraud prevention is only possible if you act while the data is in motion. Otherwise, the fraud already happened when you detect it.

Stream processing provides a scalable and reliable infrastructure for real-time fraud prevention. The choice of the right technology is essential. However, all major frameworks, like Kafka Streams, KSQL, or Apache Flink, are very good. Hence, the case studies of Paypal, Capital One, ING Bank, Grab, and Kakao Games look different. Still, they have the same foundation with data streaming powered by the de facto standard Apache Kafka to reduce risk, increase revenue, and improve customer experience.

If you want to learn more about streaming analytics with the Kafka ecosystem, check out how Apache Kafka helps in cybersecurity to create situational awareness and threat intelligence and how to learn from a concrete fraud detection example with Apache Kafka in the crypto and NFT space.

How do you leverage data streaming for fraud prevention and detection? What does your architecture look like? What technologies do you combine? Let’s connect on LinkedIn and discuss it! Stay informed about new blog posts by subscribing to my newsletter.

The post Fraud Detection with Apache Kafka, KSQL and Apache Flink appeared first on Kai Waehner.

The Insane Growth of Crypto and FinTech Markets

The crypto and fintech markets are growing like crazy. Not every new crypto coin or blockchain is successful. Only a few fintech like Robinhood in the US or Trade Republic in Europe are successful. In the last months, the crypto market has been a bear market (writing this in April 2022).

Nevertheless, the overall global interest, investment, and growth in this market are unbelievable. Here is just one of many impressive statistics:

This survey came from NBC News. But you can find similar information in many other new severe portals across the globe.

The Threat is Real: Data Breaches, Hacks, Stolen Crypto!

With the growth of cryptocurrencies, blockchains, crypto + NFT markets in conjunction with very intuitive crypto trading mobile apps, and popular “normal” trading apps adding crypto support, cyberattacks are more dangerous than ever before.

Let’s look at two of the many recent successful cyberattacks against crypto markets to steal cryptocurrencies and explain why any crypto marketplace or trading app can be the next victim.

Supply Chain Attacks for Cyberattacks

While it feels more secure in trusting a well-known and trusted crypto shop (say Binance, Coinbase, or Crypto.com), appearances are deceiving. Many successful cyberattacks these days in the crypto and non-crypto world happen via supply chain attacks:

A supply chain attack means even if your infrastructure and applications are secure, attackers still get in via your certified B2B partners (like your CRM system or 3rd party payment integration). If your software or hardware partner gets hacked, the attacker gains access to you.

Hence, a continuous internal cybersecurity strategy with real-time data processing and a zero-trust approach is the only suitable option to provide your customers with a trustworthy and secure environment.

Examples of Successful Crypto Cyberattacks

There are so many successful hacks in the crypto space. Many don’t even make it into the prominent newspapers, even though coins worth millions of dollars are usually stolen.

Let’s look at two examples of successful supply chain attacks:

• Hubspot CRM was hacked. Consequently, the crypto companies BlockFi, Swan Bitcoin, and Pantera had to advise users on how to stay safe. (source: Crypto News)
• A MailChimp “insider” had carried out the phishing attack by sending malicious links to users of the multimedia platform. This included a successful phishing attack to steal funds stored in Trezor, a popular cryptocurrency wallet company. (source: Crypto Potato)

Obviously, this is not just a problem for crypto and fintech enterprises. Any other customer of a hacked software needs to act the same way. For the context, I choose crypto companies in the above examples.

Cybersecurity: Situational Awareness and Threat Intelligence with Apache Kafka

Cybersecurity in real-time is mandatory to fight successfully against cyberattacks. I wrote a blog series about how data streaming with Apache Kafka helps secure any infrastructure. Learn about use cases,  architectures, and reference deployments for Kafka in the cybersecurity space:

• Part 1: Data in Motion as cybersecurity backbone
• Part 2: Situational awareness
• Part 3: Threat intelligence
• Part 4: Forensics
• Part 5: Air-gapped and zero trust environments
• Part 6: SIEM / SOAR modernization

Cybersecurity with Apache Kafka for Crypto Markets

Many crypto markets today use data streaming with Apache Kafka for various use cases. If done right, Kafka provides a secure, tamper-proof, encrypted data hub for processing events in real-time and for doing analytics of historical events with one scalable infrastructure:

If you want to learn more about “Kafka and Crypto” use cases, architectures, and success stories, check out this blog: Apache Kafka as Data Hub for Crypto, DeFi, NFT, Metaverse – Beyond the Buzz.

Kafka Architecture for Real-Time Cybersecurity in a Crypto Infrastructure

Let’s now look at a concrete example for integrating, correlating, and applying transactional and analytical information in a crypto environment with the power of the Kafka ecosystem. Here is the overview:

Data Producers from Blockchains, Crypto Markets, and the CRM system

Data comes from various sources:

• Back-end applications include internal payment processors, fraud applications, customer platforms, and loyalty apps.
• Third-party crypto and trading marketplaces like Coinbase, Binance, and Robinhood and direct transaction data from blockchains like Bitcoin or Ethereum.
• External data and customer SaaS such as Salesforce or Snowflake.

The data includes business information, transactional workloads, and technical logs at different volumes and are integrated via various technologies, communication paradigms, and APIs:

Streaming ETL at any scale is a vital strength of the Kafka ecosystem and is often the first choice in data integration, ETL, and iPaaS evaluations. It is also widespread to combine transactional and analytical workloads within Kafka as the event data hub.

Real-Time Data Processing for Crypto Threat Intelligence with Machine Learning

The key benefit is not sending data from A to B in real-time but correlating the data from different sources. This enables detecting suspicious events that might be the consequence of a cyberattack:

AI and Machine Learning help build more advanced use cases and are very common in the Kafka world.

Data Consumers for Alerting and Regulatory Reporting

Real-time situational awareness and threat intelligence are the most crucial application of data streaming in the cybersecurity space. Additionally, many other data sinks consume the data, for instance, for compliance, regulatory reporting, and batch analytics in a data lake or lakehouse:

Kafka enables a Kappa architecture that simplifies real-time AND batch architectures compared to the much more complex and costly Lambda architecture.

Data Streaming with Kafka to Fight Cyberattacks in the Crypto and FinTech Space

Supply chain attacks require not just a secure environment but continuous threat intelligence. Data streaming with the Apache Kafka ecosystem builds the foundation. The example architecture showed how to integrate with internal systems and external blockchains, and crypto markets to correlate data in motion.

Kafka is not a silver bullet but the backbone to provide a scalable real-time data hub for your mission-critical cybersecurity infrastructure. If you deploy cloud-native applications (like most fintech and crypto companies), check out serverless data architectures around Kafka and Data Lakes and compare Kafka alternatives in the cloud, like Amazon MSK, Confluent Cloud, or Azure Event Hubs.

How do you use Apache Kafka with cryptocurrencies, blockchain, or other fintech applications? Do you deploy in the public cloud and leverage a serverless Kafka SaaS offering? What other technologies do you combine with Kafka? Let’s connect on LinkedIn and discuss it! Stay informed about new blog posts by subscribing to my newsletter.

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