Even though fraud isn’t a new concept, recently a lot of buzzes have been created pertaining to fraud detection and prevention. It could be due to the profusely rising data and the unfettered access to the same that the intensity and frequency of fraudulent acts have increased.
According to the survey, it is seen that organizations lose nearly 5% of their profit share to fraud every year. And this number is rising at a huge pace.
From where the industry stands today, it is not tough to ascertain that technology is the key here. Owing to the rise in technological trends, more and more organizations, move to the cloud. They generate an enormous amount of data. For once it helps businesses improve their line of operations and further, enhances their productivity.
However, the power of technology isn’t confined and is used by criminals to better their take over fraudulent acts. No more are such criminals reliant on old school methods for theft, instead, they too make use of sophisticated tools and technologies to manipulate the system and commit fraud.
Owing to the above, organizations need to develop a defense mechanism to circumvent the impact of such fraudulent situations. It is the dire need of the hour to improve the detection and fraud prevention techniques to deal with the same.
Data Analytics – Preventing Fraud
According to a report by Association of Certified Fraud Examiners’ 2016 Report to the Nations, around 54% of the organizations that put their data to use, are more successful in combating fraud and getting rid of them.
Data is produced at an incredibly higher rate. Detecting loopholes and security gaps in this pool of data is nothing less than finding a needle in a haystack.
Where the traditional fraud detection techniques fail to detect smarter frauds, the tools and software built using present-day technology are far more competent. They deploy a mechanism known as Fraud Detection Analytics. Employing the method of data analytics, scouring over terabytes of data is no longer a daunting task.
The fact that analytics comes with a tinge of intelligence, comparing data, the existing one from the older version help detect anomalies which might indicate fraud. Also, such a process is largely automated and eliminates the need for manual search and pry.
What Does Traditional Methods Lack?
Knowing that data analytics holds tremendous potential in terms of detecting threats and mitigating them, it is worth outlining what the conventional system lacks. As we know, the entire system is tested against attacks and fraud activities. However, the age-old method used isn’t capable of detecting patterns, loops, and anomalies in data, which today are the most common reason behind a fraud.
In order to ensure a comprehensive and fully sophisticated approach, organizations need to invest in data analytics techniques.
How does Data Analytics help?
Where the traditional systems stop, the modern ones arise. Equipped in examining loads of data, finding patterns, uncovering relationships and anomalies, data analytics is best suited for preventing fraud. The major components include:
Fraud Pattern Detection
The first of this kind was used by the US SEC to detect frauds and thefts. A computerized program named, Robocop was designed to scan through devices and locate potential threats. The program was based on the techniques of data analytics and was capable of scrutinizing an enormous amount of data. Once through the system, the program automatically identified malicious activities that needed an inspection.
Also, such a system can successfully trace the system to find thefts holding a similar pattern. It could follow a similar route or originate from the same source. This information is extremely helpful when it comes to organizations that lose billions in fraud acts.
Besides detecting a pattern, data analytics is used to find anomalies is data. What this suggests is that at times, there could be an event that occurred abnormally or the path traced isn’t the expected one. This showcases an anomaly and requires attention. Where the conventional systems often fail to scan inch by inch, missing some information, data analytics is hardcoded to perform rigorous testing.
These tests when conducted shed light on the prevalence of suspicious acts and the organization, can then move ahead to deal with the problem.
One instance could be the placement of an order every day at 1400 HRS. This could be a coincidence once or twice but not every day. Data analytics can sense an anomaly here and notify the department about the same. They can further verify the legitimacy of the transaction to see if it’s true or not.
Apart from the above, data analytics can be applied to merge data from across all departments before performing a scan. These systems are largely efficient and have 100% accuracy. They not only detect fraud and save your organization from losing a huge amount of money but also frees up your manual workforce to emphasize important tasks.
In case, you are worried about your organization’s data and plan to prevent fraud, you can reach out to Claptek. They are one of the successful risk assessment companies that help an organization detect and prevent fraud.
Ways to Detect Fraud
It is obvious that data analytics help in the prevention of fraud but the question remains how?
How do these tools detect patterns of anomalies?
The answer to this is testing. As a matter of fact, there are two different tests conducted to detect fraud.
Ad-hoc testing is done on a need to need basis. What this means is that only when there arises a problem related to the business, will experts perform ad-hoc testing. These are primarily related to the solutions posed in response to an existing issue.
After the tests are executed safely, they come up with reports and insights on the data in question to further, propose potential threats. One drawback of such testing is the fact that it requires human assistance and would consume a lot of time.
An alternative for the above, continuous testing is automated scripts that run through the system in continuum and have the potential to detect threats. They occur in the loop and perform an in-depth scanning, to locate data anomalies. It is proven that automated testing not only saves the time and effort needed to detect fraud but also increases the efficiency and productivity of the organization.
Also, the fact that the system is being scanned repeatedly followed by detailed monitoring, ensures that an anomaly never misses the eye of the system. It further enables the timely execution of measures to mitigate the impact of fraud.
Suggesting data analytics for fraud detection and prevention is not a vague approach. Instead, it has been tested and verified. Traditionally, the time taken to detect fraud could extend up to eighteen months. Today, this scenario has changed. Organizations have proactively invested in data analytics tools and methods to detect the occurrence of fraud activities which is then treated to prevent the reoccurrence.