Fraudulent activities are a significant threat to businesses in today's digital world. They can cause financial losses and security breaches, affecting both companies and their customers. That's why real-time fraud detection is crucial for modern transaction systems.
The challenge we face is processing large amounts of transactions instantly and accurately identifying suspicious patterns. Traditional methods of batch processing aren't effective in detecting fraud as it happens, which leads to delayed responses and potential financial damages.
Fortunately, there are powerful solutions available: AWS Kinesis and Apache Flink. These technologies offer a robust fraud detection mechanism that can:
AWS Kinesis is a scalable streaming platform that captures and processes large amounts of data in real-time. When combined with Apache Flink's advanced stream processing capabilities, you have a sophisticated system that can detect fraudulent patterns as transactions occur.
This architecture brings several benefits to businesses:
By integrating these technologies, we can create a comprehensive fraud detection system that adapts to new threats while efficiently processing legitimate transactions.
AWS Kinesis is Amazon's solution for real-time data streaming and analytics. This fully managed service enables you to collect, process, and analyze streaming data at any scale, making it a crucial tool for modern data-driven applications.
AWS Kinesis shines in scenarios requiring immediate data processing. The service can handle various data types:
The architecture of AWS Kinesis follows a producer-consumer model. Data producers send records to Kinesis streams, while consumers retrieve and process these records. Each stream maintains a sequence of data records organized in shards, providing ordered data delivery and dedicated throughput per shard.
Kinesis offers built-in resilience through automatic data replication across multiple AWS availability zones. The service manages the underlying infrastructure, eliminating the need for manual server provisioning or cluster management.
The pricing model follows a pay-as-you-go structure based on the amount of data ingested, stored, and processed. This flexibility allows you to scale your streaming applications cost- effectively while maintaining high performance and reliability.
A robust fraud detection system requires seamless integration of multiple AWS services working in harmony. Let's explore the architecture that powers real-time fraud detection using AWS services.
The architecture follows a streamlined data flow where transaction data enters through Kinesis Data Streams. These streams act as the primary pipeline, directing incoming data to Apache Flink applications for real-time processing and analysis.
The system scales automatically based on incoming transaction volume. AWS Managed Apache Flink handles the complex task of maintaining processing state and ensuring exactly-once processing semantics.
Amazon Kinesis Data Streams (KDS) is a powerful service for ingesting real-time data at scale. But to truly leverage its power, you need producers—the components responsible for pushing data into your Kinesis stream.
So who—or what—are these producers? Let’s break it down.
🛠️ 1. Kinesis Agent
The Kinesis Agent is a standalone Java application you install on your server. It's perfect for collecting and sending data like log files or metrics from on-premise or EC2 servers directly into your Kinesis Data Stream.
🧰 2. Kinesis Producer Library (KPL)
The KPL offers a higher-level abstraction over the lower-level Kinesis API. It simplifies the process of batching, queuing, and retrying records.
If you're building custom applications, you can use AWS SDKs available in multiple
🌐 Languages Supported: Java, Node.js, Python, Ruby, Go, and more
✍️ APIs: Use PutRecord or PutRecords to write data
🔧 Use case: Real-time analytics, custom dashboards, event-driven apps
💻 4. AWS CLI
Sometimes, you just want something quick and dirty—enter the AWS Command Line Interface.
🧪 Best for: Testing, scripting, and ad-hoc record insertion
⚡ Fast and simple: Fire and forget test data from your terminal
Certain AWS services can push data straight into Kinesis—no code, no hassle.
🔌 Services that integrate directly:
Amazon API Gateway (proxy integration)
AWS Lambda (as a proxy)
AWS IoT Core, EventBridge, and others
☁️ Use case: Serverless pipelines with minimal operational overhead
Here's how you can implement the data producer using Python AWS-SDK:
const AWS = require('aws-sdk');
// Configure AWS SDK
AWS.config.update({
region: 'us-east-1' // change if needed
});
const kinesis = new AWS.Kinesis();
const STREAM_NAME = 'transaction-stream';
// Helper: generate a random transaction
function generateTransaction() {
const userId = 'user' + Math.floor(Math.random() * 5 + 1); // user1 to user5
const transactionId = 'tx' + Math.floor(Math.random() * 1000000);
const amount = Math.random() < 0.2 ? 15000 : Math.floor(Math.random() * 1000); // occasionally trigger fraud
const timestamp = Date.now();
const location = ['NY', 'CA', 'TX', 'FL', 'WA'][Math.floor(Math.random() * 5)];
return {
transactionId,
userId,
amount,
timestamp,
location
};
}
// Send transaction to Kinesis
function sendTransaction() {
const txn = generateTransaction();
const payload = JSON.stringify(txn);
const params = {
Data: payload,
PartitionKey: txn.userId, // ensures same user lands in same shard
StreamName: STREAM_NAME
};
kinesis.putRecord(params, (err, data) => {
if (err) {
console.error('Error sending to Kinesis:', err);
} else {
console.log('Sent:', payload);
}
});
}
// Send a new transaction every second
setInterval(sendTransaction, 1000);
The above code simulates real time transactions using Node.js This script:
To begin processing transactions in real time, we need a Kinesis Data Stream—a highly scalable and durable service for real-time data ingestion.
This stream acts as the entry point for all transaction records that our producer (Node.js script or any backend app) will send. Each incoming record is temporarily stored across shards, which determine the stream’s throughput capacity.
Setting up Kinesis Data Streams requires careful planning and implementation to ensure efficient data capture for your fraud detection system. Here's a detailed guide to configure your stream:
Open the AWS Management Console and:
Once the stream is created, it may take a few seconds to become active
⚙️ 1. Throughput Planning
Before you dive in, make sure your stream is sized for success.
📊 Shard Limits:
Each shard supports 1 MB/sec write and 2 MB/sec read throughput.
🧮 Estimate Before You Build:
Calculate the average record size and records per second to determine the number of shards you need.
🔁 Enhanced Fan-Out for Multiple Consumers:
If you have multiple downstream consumers (e.g., Apache Flink + Firehose), enable Enhanced Fan-Out. This gives each consumer a dedicated 2 MB/sec read pipe, reducing contention.
📈 2. Monitoring & Scaling
Kinesis offers great tools to help you watch and adapt in real time.
📉 CloudWatch Metrics to Monitor:
IncomingBytes
ReadProvisionedThroughputExceeded
These help detect when you're hitting shard limits.
📡 Set Up CloudWatch Alarms:
Alert on usage thresholds or sudden spikes in traffic.
🔐 3. Security & Access Control
Keep your data safe and your access policies tight.
🔑 Use IAM Policies:
Define who can read from and write to your streams.
🛡️ Enable Server-Side Encryption:
Use AWS KMS to protect data in transit and at rest.
📦 4. Data Management & Replay
Make your data work harder for longer.
🕒 Data Retention:
Adjust from the default 24 hours up to 7 days if you need more time for reprocessing or compliance.
⏪ Replay with TRIM_HORIZON or AT_TIMESTAMP:
Useful for replaying data into Flink or other consumers for backfills or failure recovery.
🧯 Backup with Firehose:
For long-term storage, attach Kinesis Data Firehose to send data to Amazon S3 automatically.
By following these best practices, you ensure your Kinesis data pipeline is resilient, efficient, and scalable—ready to handle real-time workloads like fraud detection, log analytics, and IoT telemetry.
Apache Flink's event processing capabilities shine in fraud detection scenarios through its ability to analyze streaming data in real-time. AWS Managed Apache Flink simplifies this process by handling infrastructure management, letting you focus on building effective fraud detection logic.
Apache Flink uses several powerful techniques to detect fraudulent patterns:
Here's a practical example of implementing fraud detection logic in Flink using JAVA Maven Project:
Project Structure:
Dependencies:
package com.example.fraud.model;
public class Transaction {
public String transactionId;
public String userId;
public double amount;
public long timestamp;
public String location;
public Transaction() {}
@Override
public String toString() {
return String.format("Transaction[id=%s, user=%s, amount=%.2f, time=%d, location=%s]",
transactionId, userId, amount, timestamp, location);
}
}
SnsPublisher.java
package com.example.fraud.util;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.sns.SnsClient;
import software.amazon.awssdk.services.sns.model.PublishRequest;
public class SnsPublisher {
private static final SnsClient snsClient = SnsClient.builder()
.region(Region.US_EAST_1) // Change region if needed
.build();
private static final String TOPIC_ARN = "arn:aws:sns:us-east-1:011528266190:FraudAlerts"; // Replace with your SNS topic ARN
public static void publishAlert(String message) {
PublishRequest request = PublishRequest.builder()
.topicArn(TOPIC_ARN)
.message(message)
.subject("FRAUD ALERT")
.build();
snsClient.publish(request);
}
}
FraudDetectionJob.java
package com.example.fraud;
import java.time.Duration;
import java.util.ArrayList;
import java.util.List;
import java.util.Properties;
import org.apache.flink.api.common.eventtime.WatermarkStrategy;
import org.apache.flink.api.common.serialization.SimpleStringSchema;
import org.apache.flink.api.common.state.ListState;
import org.apache.flink.api.common.state.ListStateDescriptor;
import org.apache.flink.api.common.time.Time;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.KeyedProcessFunction;
import org.apache.flink.streaming.connectors.kinesis.FlinkKinesisConsumer;
import org.apache.flink.util.Collector;
import com.example.fraud.model.Transaction;
import com.example.fraud.util.SnsPublisher;
import com.fasterxml.jackson.databind.ObjectMapper;
public class FraudDetectionJob {
private static final ObjectMapper objectMapper = new ObjectMapper();
public static void main(String[] args) throws Exception {
final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
Properties consumerConfig = new Properties();
consumerConfig.setProperty("aws.region", "us-east-1");
consumerConfig.setProperty("stream.initial.position", "LATEST");
FlinkKinesisConsumer<String> kinesisConsumer =
new FlinkKinesisConsumer<>("transaction-stream", new SimpleStringSchema(), consumerConfig);
env.getConfig().setAutoWatermarkInterval(1000);
env.addSource(kinesisConsumer)
.map(json -> objectMapper.readValue(json, Transaction.class))
.assignTimestampsAndWatermarks(WatermarkStrategy.<Transaction>forBoundedOutOfOrderness(Duration.ofSeconds(5))
.withTimestampAssigner((txn, ts) -> txn.timestamp))
.keyBy(txn -> txn.userId)
.process(new FraudDetector())
.map(Transaction::toString)
.print();
env.execute("Real-Time Fraud Detection Job");
}
public static class FraudDetector extends KeyedProcessFunction<String, Transaction, Transaction> {
private transient ListState<Long> timestampState;
@Override
public void open(Configuration parameters) {
ListStateDescriptor<Long> descriptor = new ListStateDescriptor<>("timestamps", Long.class);
timestampState = getRuntimeContext().getListState(descriptor);
}
@Override
public void processElement(Transaction txn, Context ctx, Collector<Transaction> out) throws Exception {
boolean isFraud = false;
// Rule 1: High amount
if (txn.amount > 10000) {
isFraud = true;
}
// Rule 2: High velocity
long oneMinuteAgo = txn.timestamp - Time.minutes(1).toMilliseconds();
List<Long> timestamps = new ArrayList<>();
System.out.println(timestampState.get());
for (Long ts : timestampState.get()) {
if (ts >= oneMinuteAgo) {
timestamps.add(ts);
}
}
timestamps.add(txn.timestamp);
timestampState.update(timestamps);
if (timestamps.size() > 5) {
isFraud = true;
}
if (isFraud) {
String message = "FRAUD DETECTED: " + txn.toString();
SnsPublisher.publishAlert(message);
out.collect(txn);
}
}
}
}
You can implement various fraud detection patterns using Flink:
1. Velocity Checks
2. Amount Pattern Analysis
Let’s break down the core components of the Flink job:
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
Properties consumerConfig = new Properties(); consumerConfig.setProperty("aws.region", "us-east-1"); consumerConfig.setProperty("stream.initial.position", "LATEST");
FlinkKinesisConsumer<String> kinesisConsumer =
new FlinkKinesisConsumer<>("transaction-stream", new SimpleStringSchema(), consumerConfig);
.assignTimestampsAndWatermarks(WatermarkStrategy .forBoundedOutOf Orderness(Duration.ofSeconds(5)) .withTimestampAssigner((txn,ts) -> txn.timestamp))
.keyBy(txn -> txn.userId) .process(new FraudDetector())
if (txn.amount > 10000 || transactionsWithin1Min > 5) {
SnsPublisher.publishAlert(message);
out.collect(txn);
}
ListState timestampState = getRuntimeContext().getListState(...);
.map(Transaction::toString).print();
This code forms the real-time brain of your fraud detection pipeline—processing, detecting, and alerting all in milliseconds.
AWS Managed Flink allows you to run Apache Flink applications without managing the infrastructure. We'll use it to consume transaction data from Kinesis, apply fraud detection logic, and trigger SNS alerts if suspicious activity is detected.
Navigate to your project root and run the following command:
mvn clean package
This will generate a JAR in the target/ directory, e.g., fraud-detection-app-new-0.0.1.jar.
Upload the JAR File to S3
s3://flink-fraud-detection-jars/fraud-detection-app-new-0.0.1.jar
Create AWS Managed Apache Flink in console:
2. Click Create streaming application
Once Apache Flink identifies suspicious patterns, you'll need a reliable system to notify relevant stakeholders immediately. Amazon Simple Notification Service (SNS) provides a powerful solution for sending real-time alerts about potential fraud.
You can find the full source code for this project on GitHub:
JAVA(Apache Flink) - https://github.com/ShalniGerald/aws-kinesis-apache-flink.git
Producer Node.js - https://github.com/ShalniGerald/aws-kinesis-data-stream-producer.git
Kinesis Data Firehose adds a valuable layer to your fraud detection system by enabling seamless data storage and historical analysis capabilities. This service automatically delivers your streaming data to Amazon S3, creating a robust archive for future reference and analysis.
Data Storage Patterns for S3:
The stored transaction data in S3 enables powerful analytical capabilities through various AWS services:
Storage Configuration Best Practices:
This historical data repository becomes invaluable for identifying long-term fraud patterns and improving detection algorithms. The combination of real-time processing and historical analysis creates a comprehensive fraud detection strategy that adapts to emerging threats.
The integration of AWS Kinesis and Apache Flink represents a powerful approach to modern fraud detection. This combination delivers real-time processing capabilities essential for identifying and preventing fraudulent activities in today's fast-paced digital landscape.
The architecture we've explored offers distinct advantages:
Your fraud detection system gains adaptability and scalability through these AWS services. The platform evolves with your needs, handling increasing transaction volumes while maintaining performance. Machine learning capabilities enable the system to recognize new fraud patterns, strengthening your security posture.
The future of fraud detection lies in intelligent, automated systems that learn and adapt. AWS services provide the foundation for building such systems, offering:
Your organization can stay ahead of fraudulent activities by implementing this AWS-based fraud detection mechanism. The combination of real-time processing, intelligent analysis, and immediate alerting creates a robust defense against financial threats.
As the scale and complexity of fraud detection systems grow, it's essential to ensure that the underlying architecture can keep up with performance demands—especially when every millisecond counts.
One powerful feature in Amazon Kinesis Data Streams that helps take this to the next level is Enhanced Fan-Out (EFO).
With EFO, each consumer gets its own dedicated 2 MB/second read throughput, allowing multiple applications—like analytics, monitoring, alerting, or storage—to read from the same stream in parallel and without interference. This is a game-changer compared to shared throughput models, where consumers compete for bandwidth.
I have illustrated the difference between Standard and Enhanced Fan-Out Architecture below:
⚡ Lower latency: Near-instant delivery of data to Flink applications, improving fraud detection speed.
🚀 High scalability: Easily add new consumers (e.g., alerting systems, audit logs, AI model feedback loops) without re-architecting.
🔄 Independent processing: Different consumers can process the same data in real time, independently and concurrently.
Imagine a scenario where:
One Flink app flags suspicious transactions.
Another app logs all events into a data lake.
A third app triggers real-time SMS/email alerts via SNS.
With Enhanced Fan-Out, all of these systems can run simultaneously, without slowing each other down—delivering a true real-time, multi-layered fraud prevention strategy.
While this blog focused on building a real-time fraud detection pipeline using AWS Kinesis Data Streams and Apache Flink, implementing Enhanced Fan-Out unlocks next-level performance and flexibility. For organizations handling high-volume, latency-sensitive data streams, it's a strategic upgrade that brings both technical robustness and business agility.