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Why Do You Need Fraud Monitoring Analytics Using Big Data Analytics and Machine Learning?

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Jennifer Moore
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The world became digital. More and more people around the globe turning to online services. Customers use online banking to manage their financial activities and do their transactions via a mobile app, patients share their health data, users share bank accounts while subscribing to the streaming platforms and buyers fill in personal contacts to make a purchase. We all share our data every day. Due to this technological progress, the number of cybercrimes, frauds, hackers attacks, threats, and vulnerabilities of organizations has increased. Thus, not only financial institutions, but every industry must apply comprehensive fraud prevention techniques to protect their customers’ personal data.

An average attack may cost an organization millions. In this article, we are discussing security measures and how business has to conduct fraud management.

Importance of Fraud Monitoring Analytics with Big Data Analytics and Machine Learning

Fraud data analytics is a multidisciplinary approach that combines quantitative sciences to predict and prevent online frauds. Including the use of big data analysis techniques, business intelligence, data mining, machine learning, and AI. This can help organizations in different industries detect suspicious behavior, and apply mitigation of fraudulent activity in real-time.

The process of fraud prevention analytics includes gathering and storing relevant data and mining it for discrepancies. Gained insights that can allow managing potential threats before they occur as well as develop a proactive fraud detection and prevention environment.

There are a couple of reasons why the level of fraud is growing.

  • Businesses don’t pay proper attention to security while moving to the public cloud and increasing IoT devices usage.
  • After the Pandemic started and companies switched to remote mode, people started using their personal devices (laptops, phones) to access corporate information.
  • A Home Wi-Fi network can’t ensure the same level of cybersecurity as an office environment. But also, employees often neglect antivirus, use weak passwords, apply dangerous online tools, etc.

Companies all across the world use data analytics to detect fraud. Deutsche Bank uses big data to identify money laundering, prevent credit card fraud, etc. UK mobile virtual network operator Lebara identified a scam and block SIM cards that were used by fraudsters to call their victims. A healthcare insurance company Santéclair also applies anti-fraud systems and advanced algorithms to detect scams three times more effectively.

Benefits of fraud monitoring analytics

The advantage of using fraud detection analytics is that it can handle a huge amount of data at once. And also it

  • helps you identify the area that can be affected by the fraud the most
  • fixes weaknesses in the system or business flow
  • saves time by automating the repetitive tests
  • searches all transactions automatically for fraud indicators
  • merges and compares data from different systems
  • calculates the impact of fraud
  • uncovers new patterns, trends, fraudulent schemes, and scenarios that traditional approaches can’t detect
  • speed up analysis and eliminates manual work

Methods of fraud monitoring analytics

To create a very powerful fraud detection system, use the combination of the following methods.

Ad-Hoc

Ad-Hoc is the process of discovering fraud by using a hypothesis. You can test software to find out if it’s capable of resizing dangerous actions e.g. fraudulent transactions.

Sampling

It’s one of the mandatory methods of fraud detection. It is most effective where there is a lot of data population involved. On the other hand, sampling may not be able to fully control fraud detection as it takes only a few populations into consideration. Fraudulent transactions do not occur randomly therefore an organization need to test all the transactions to effectively detect fraud.

Competitive or Repetitive Analysis

This method creates and sets up scripts that use Big Data to run against the big volume of data to identify the frauds over a specified time period. Analysis helps to improve the efficiency of companys’ fraud detection processes.

Analytic techniques

These usually identify abnormal frauds by using statistical parameters and classifying the data and transactions. Look at high and low values and find out the anomalies that are most of the time the indicators of scams.

Gaps

This method finds out the missing sequential data. For example, if you have purchase orders which are issued by the company in sequential order and if anything is missing you can easily find out. This is an easy method and it will work out great if used correctly.

Check how we can assist you with secure banking solutions

How to Use Big Data and Machine Learning for Fraud Prevention and Monitoring Analytics

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