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  • Derrick Wong

Combat Fraud with Data Analytics


While fraud is not a new phenomenon, the current financial crisis has shown that fraud is increasingly becoming common. With this in mind, managers should start a series of anti-fraud measures that can minimise this. Fraud generally involves a significant financial crisis that may affect the financial stability and the image of an organization.


In these new circumstances in which the IT systems play an important role, the amount of processed data has really grown. With fraudsters becoming more sophisticated and data increasing at an alarming rate, data analytics plays a very important in dealing with increasing levels of fraud.


Recent research has shown that fraud is costing businesses $2.1 trillion globally. It’s, therefore, a fact that fraud schemes have become sophisticated. With fraudsters constantly looking for new ways to manipulate technology, to combat fraud businesses must invest in the best technologies and resources.


A recent survey by PwC has found that up to 44% of businesses plan to increase their spending on economic crime and fraud prevention. Frauds are characterized by many forms including:

  • Money laundering

  • Electronic crime

  • Bribery and corruption

  • Insider dealings and market abuse

  • Information security

How Data Analytics Can Help In Fraud Detection

Fraud not only takes many forms but also affects almost every industry. While it doesn’t affect them in equal measure, all of them end up losing huge amounts of money. To get to the bottom of the when and why it happens, the departments that deal with it apply various techniques. One of these techniques is data analytics.


The main advantage of data analytics is that it can handle huge amounts of data at once. They typically learn how to spot what’s normal and what’s not in a collection of data. While data analytics does not replace the need for humans to scrutinize the findings and content, they can be used to track down problems and trends much faster than humans can.


Why Data Analytics is important

The amount of data produced globally is growing at a high rate and there is no sign that it will slow down soon. Because of this, it has become really hard to uncover any fraud indicators. As a matter of fact, employees are increasingly learning more sophisticated ways to by-pass the system. This means internal controls are no longer enough.


Manually reviewing all this data is not only expensive but also time-consuming. This is especially true for big businesses. Thankfully, with data analytics, you can get a quick overview of what’s happening and easily go through the details. This makes examinations faster and comprehensive. Additional benefits of data analytics include:


Data Integration

By combining data from different sources, data analytics helps simplify the process of integrating and analyzing data.


Identify the Hidden Patterns

As compared to the traditional methods of identifying patterns, scenarios, and trends in fraudulent activities, data analytics is much more superior.


Boost existing Efforts

Instead of data analytics doing away with the traditional methods, it enhances them so as to provide accurate results.


Improving Performance

Since every business has different systems and processes, there is no uniform solution for all of them. Data analytics, therefore, helps to identify the most suitable solution for an organization.


Harnessing Unstructured Data

While data is stored in unstructured form, it’s within this form that most fraudulent activities occur. With the use of data analytics, unstructured data can be harnessed to detect and prevent any occurrence from taking place.


Sampling Cannot Help Any More

Just like other traditional methods, sampling has many shortcomings. While testing a sample is a good approach, it’s not quite effective when it comes to fraud detection. This is because fraudulent activities do not take place randomly.


To effectively test and review internal controls, businesses should analyze all the relevant transactions. Unless they incorporate data analytics and automation, this is practically impossible.


Types of Data Analytics

There are different types of data analytics used for fraud detection. All these depend on the processes and systems followed. The most common techniques include:


Ad-Hoc

This method involves testing hypotheses to determine whether fraudulent activities have occurred. Based on the results gotten, further tests can be carried out. The goal is to get an answer to a particular problem.


Repetitive

Commonly known as competitive analysis, repetitive analysis involves writing scripts that go through large volumes of data to check whether fraudulent activities have occurred. While the scripts are continuous, they can be set to provide notifications thus making the process more efficient and consistent.


Analytics Techniques

Analytics Techniques involve identifying anomalies to check out whether fraud has taken place. It includes checking if there are values that exceed the standard deviation. Data can also be grouped based on specific criteria such as the geographical location of events.


How to Build Fraud Detection Solutions

A good fraud detection framework should include:


· Performing a SWOT analysis. Organizations should analyze all the strengths and weakness of the solution

· Build a dedicated management team. The business should have a dedicated management team that works on detecting and preventing fraud.

· Outline the rules. The company should set up clear rules. This will help them to build a robust solution.

· Clean data. The existing data should be cleaned.


Engaging in Continuous Monitoring

Once a particular fraud has been detected, the analysis should be repeated on a regular basis. However, in some areas, you may only need to carry out analysis on a weekly or monthly basis. If issues of data access, preparation, and validation have been taken care of and the tests confirmed to be effective, the organization should move on automating the test


Final Thoughts

While every organization knows the importance of having a robust fraud detection and prevention system it needs to be efficient so as to achieve its goal. The use of machine learning and fraud detection models can help in more accurate detection of fraudulent activities so as to reduce and combat them.


Plus data analytics can help an organization to identify fraud before it happens and focus on all suspicious transactions. The company can also gain insight into how the internal controls are working.


If you will like to know more about how to combat fraud with data analytics without the complexities, you can reach out to our consultants or you can join us in the coming live webinar by Arbutus Analytics.

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