Apache Kafka can be rightly called a cornerstone technology that is helping enterprises with data pipelines become digital corporations. Kafka was created first in the tech labs of LinkedIn – the world’s biggest network of professionals.
Apache Kafka can be summarized in a single sentence as, “a distributed data streaming platform that is horizontally-scalable, fault-tolerant and is swift in action.”
Apache Kafka is so good for real-time data streaming that it even used by giant corporations like Twitter, Uber, Pinterest, Netflix, Tumblr, PayPal and many others. It’s real-time data streaming abilities also makes Apache Kafka an ideal choice for fraud detection.
Unlike a few years ago, there is a massive amount of data that is being created on a real-time basis today. Connected devices, social networks, and websites create several petabytes of data. Every single byte of this information which is shared across the Internet is being tracked and recorded in some way or the other. For a business, this data could be a gold mine of insights. It can help indicate how the business is performing, where it is heading and how it can be made better.
As a bonus, real-time data also helps spot anomalies – that is peculiar and abnormal instances that deviate from the usual way of functioning. They help in identifying instances of fraud or error that should be curtailed with immediate effect.
But, there are certain challenges en route real-time fraud detection.
Challenges in Real-time Fraud Detection
Juniper Research estimates online FDP (fraud detection and prevention) spending is all set to touch $9.3 billion by 2022. Fraud detection and prevention has become a high priority for all data-driven enterprises.
Traditional FDP systems relied heavily on historical data based pattern and signature matching. But, in the current scenario, it is proving to be redundant as data is created near real-time.
Also, there are several other challenges in traditional FDP systems, like:
Obsolete Algorithms
Enterprises, especially banking and financial institutions create algorithms for FDP based on frauds that are already detected. These algorithms are deployed to cross-check every current transaction. Although this method helps in spotting recurring instances of the same fraudulent activity, it leaves new forms of fraud unattended.
Algorithms are not updated
Due to the dependency on historical data and the cost involved to tweak the algorithm to changing scenarios, enterprises postpone the process of updating the algorithm. The updates happen every quarter or bi-annually, or in extreme cases, once a year. By then, a large chunk of fraudulent transactions would have gone undetected.
Isolating fraudulent transactions from genuine ones
Most fraudulent transactions and genuine transactions are identical in nature, excluding the fact the former has a malicious intention. Fraud-detection algorithms need to be smart enough to spot the malicious intent based on preset parameters or through continuous learning enabled by Machine learning.
How Apache Kafka helps in real-time fraud detection
Considering the above challenges, Apache Kafka poses as an ideal choice that will empower organizations for real-time fraud detection.
Apache Kafka helps in large volume data ingestion. It is easily scalable which helps manage any volume of data for real-time fraud detection. A producer and consumer based messaging systems form the backbone of Kafka’s delivery architecture. Being a distributed system, it runs in clusters made up of several nodes which are referred to as a Kafka Broker.
Kafka can read volumes of data from many sources, like financial transactions, user interactions, database records, analytics and so on.
Apache Kafka is a wonderful piece of technology that simplifies your task of extracting data from diverse sources. It converges data pipelines from multiple sources helping business develop insights out of large volumes of data.
However, building a real-time data pipeline that can use Apache Kafka to handle large volumes of data is quite a challenge. A purpose-built tool that can assure scalability, security and is also flexible to meet changing business requirements is hard to come by. Such a tool must also be capable of ingesting high-velocity and high-volume data. Based on the data ingestion and subsequent processes of analyzing it, the tool should be capable of churning out real-time predictions for fraud detection.
