Fraud Detection: How Machine Learning Systems Help Reveal Scams in Fintech, Healthcare and eCommerce

Vishal Patel

With the unprecedented rise in the use of smart devices, user-friendly mobile applications and progressive data/information management tools, a lot of days today tasks have become outstandingly easier. For instance, if you want to send money to someone sitting thousands of miles away from you, it can be done in a jiffy. But, with the greater ease of doing things in the digital world, there has been a significant rise in cases involving digital fraud.

Cyber Security has detected numerous cases of information theft and digital fraud that it has become more important than ever to deploy countermeasures to reduce such incidents whilst making fraud detection efficient.

Technology experts believe that AI and Machine Learning can be leveraged for fraud detection and reducing scams in the Healthcare, eCommerce and Finance sectors.

What is Cyber Fraud?

Cyber Fraud is a term that is widely used for any fraudulent activity that is conducted in digital space and involves hacking/stealing or illegitimately attaining someone’s personal or financial information.

These days when a large percentage of financial transactions are conducted over the World Wide Web, activities involving unauthorized access to the financial or personal information of users have become quite common. Hackers and cyber criminals from across the globe use vulnerabilities in the digital systems storing the user’s data to get access and acquire sensitive information.

One of the widely known cases of Cyber Fraud happened in 2015 when the US Office of Personnel Management got compromised and hackers from China were able to get access to the personal information of over 20 million people including fingerprints. Many such occurrences have occurred in recent years where hackers stole the banking or credit card information of users.

This is why it is imperative to design highly secure systems for storing data and carrying out transactions on digital platforms. In order to develop robust systems, organizations across the globe hire ML developer or machine learning/AI developers to create systems that can detect frauds/scams proactively.

How Does Machine Learning Help In Revealing and Proactively Detecting Scams?

With the steep rise in “Cyber Frauds and Scams”, technology experts are coming up with innovative ideas to reveal, proactively detect and effectively control such activities. One of the technologies that has proven to be quite helpful in revealing the scams in healthcare, FinTech and eCommerce industries is Machine Learning.


How Machine Learning Detects Fraud?

Machine Learning based algorithms in fraud detection systems work by sorting colossal amounts of data to create the behaviour profiles of consumers. Each transaction made by every customer is analysed against the normal spending behaviour of that user. If the transaction appears to be mismatched with the usual customer behaviour, it gets flagged for review. These reviews are usually completed manually by the analysts to establish the authenticity of that transaction before it can be processed.

Machine Learning based algorithms are designed to replicate the decision-making by humans, but it analyses a large volume of data quite fast and makes thousands of decisions in a minute.

One of the features that make ML-based systems outstanding is their capability to self-learn. As these systems process more & more data, they self-learn and thus, improve themselves after each data processing. Gradually, algorithms start becoming more accurate, efficient and better equipped with time.

At present, there are two types of fraud detection that can be performed by ML based systems:

  1. Supervised
  2. Unsupervised

While supervised fraud detection requires feeding the historical data related to fraudulent and non-fraudulent transactions to equip the ML-based system to understand the difference between the two and analyze the data on this basis. On the other hand, unsupervised ML is just fed colossal data where it figures out the data anomalies on its own. Both systems are fully capable of detecting fraud and the combination of these two systems gives a highly efficient fraud detection system that can be used in any industry.

Fraud Prevention in Finance Industry

Machine Learning and AI-based applications can detect fraud patterns and deeply analyze the data related to fraud to be equipped with the right tools. The companies can hire ML developers to create fraud detection solutions powered by robust machine learning algorithms that can process colossal datasets with numerous variables to detect the links as well as co-relation between the fraudulent actions and the user behaviour to proactively identify the fraud.

There are some finance institutions that have already started using AI & Machine Learning based solutions to identify fraudulent transactions. For instance, Mastercard uses an AI and ML (Machine Learning) integrated system to monitor and track different variables associated with a transaction such as transaction device, purchase data, location, time, as well as transaction size. With this information, account behaviour is assessed in every operation to provide real-time insights about whether the transaction looks fraudulent. So, if you are looking for solutions that can secure your eCommerce platform or Healthcare institution, it’s the right time to hire a Machine Learning developer.

Fraud Prevention in eCommerce Industry

The eCommerce industry thrives on digital payments and digital shopping platforms and online transactions are quite vulnerable to fraudulent activities. One can hire Machine Learning Developer to create solutions to identify and prevent identity theft and merchant scam instances.

Identity theft is quite common in eCommerce where a scammer, by breaching the user account and editing the personal data tries to get goods or in some cases, money from an online retailer by faking personal information. Machine Learning based behaviour analysis algorithms can be used to detect suspicious activities and locate inconsistencies in personal data. These inconsistencies are then leveraged to detect fraudulent activities in a proactive manner.

Overall, Machine Learning based systems are evolving as progressive tools that can be used against the increasingly intelligent and sophisticated frauds that are happening across the globe. Thus, adopting AI and Machine Learning is one of the best ways to move ahead where information security has become the underlying necessity.

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