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

With the unprecedented rise in the use of smart devices, user friendly mobile applications and the progressive data/information management tools, a lot of day 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 the cases involving digital fraud.

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

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

 

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 personal or financial information.

These days when a large percentage of financial transactions are conducted over the World Wide Web, the activities involving unauthorized access to the financial or personal information of users has become quite common. The hackers and cyber criminals from across the globe use vulnerabilities in the digital systems storing the user’s data to get access and acquire the 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 personal information of over 20 million people including the 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, the organizations across the globe hire ML developer India or machine learning/AI developers to create systems that can detect frauds/scams proactively.

 

With the steep rise in the “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.

 

 

Machine Learning based algorithms in the 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 a 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 makes 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 the supervised fraud detection requires feeding the historical data related with 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.

 

Machine Learning and AI based applications can detect the fraud patterns and deeply analyze the data related to frauds to be equipped with the right tools. The companies can hire ML developer India 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 the 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 Machine Learning developer.

 

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 the goods or in some cases, money from an online retailer by faking the personal information. Machine Learning based behaviour analysis algorithms can be used to detect the suspicious activities and locate the inconsistencies in the personal data. These inconsistencies are then leveraged to detect the 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.