Top 3 Must-Know Fraud Detection and Prevention Advances

December 2, 2019 @ 6:44 AM By Donna Marie

Cases of identity theft and fraud are still at a widespread. In fact, the intensity of damages they bring about to victims are becoming graver, now with small businesses and clueless civilians being their main targets.

 

In a recent Identity Fraud Study conducted by Javelin, victims of fraud hit a record high of 16.7 million in 2017 but decreased to 14.4 million later in 2018. The catch here, though, is that while the number of victims fell down by millions, the damages incurred managed to reach $1.7 billion. This accounts to double the amount of losses recorded in 2016.

 

Not frightening enough?

 

Then how about these findings in the Global Economic Crime and Fraud Survey done by PwC in UK. The organization gathered 7,000 respondents who revealed:

    • Around 49 percent of the global organization respondents claimed to be a victim of fraud. This is 13 percent higher than the recorded instances in 2016 which accounts to 36 percent. This value doesn’t even include organizations that aren’t aware that they already fell victim to such crime.

       

    • At least 64% of the respondents said that the most disruptive fraud that happened to them could’ve incurred up to $1 million in losses.

       

    • Unfortunately, 52% of all detected frauds were initiated by internal members of the attacked organization.

       

These statistics only mean that fraudsters are becoming wiser in their methods. They know who the easy targets are and how to attack them. In fact, recent reports pointed out how they shifted focus from big enterprises to smaller financial accounts like rewards programs. They know very well that these accounts are rarely wrapped with sophisticated cybersecurity tech and can be easily infiltrated.

 

The present generation is highly tech driven and even cybercriminals leverage innovations to make fraud undetectable. The society and different firms then need a stringent fraud detection system to, at least, minimize the attacks.

 

The world isn’t going down without a fight. The battle against this crime has evolved as new advances in fraud detection technologies arise. More controls are being implemented to detect fraud in its earliest stages rather than after the attack has been done.

 

Employment of Machine Learning in Fraud Detection

 

Machine learning is a form of artificial intelligence that most enterprises use for continuously learning data, and improving this learning in an autonomous way. In the same fashion, machine learning is used in fraud detection to continuously dig into the methods used by cybercriminals in committing fraud. Hence, anyone using this tech becomes updated whenever a new trend in fraud comes up. Consequently, they are able to revise their preventive strategies to counter these newer trends.

 

What’s good about Machine Learning is that it never stops working. It digs into data day and night, making it almost impossible to miss out any attempts of fraud in the system. The machine compares all transactions with what it recorded as normal behavior, and when a suspicious change happens, it alerts its owners of a possible fraud.

 

This isn’t new, though. Machine learning has been used in the past years by insurance companies and similar financial firms to protect them from any modus. However, the difference comes with the analyzation and judgment of fraud attempts.

 

In the past, Machine Learning flags incidents for review by human. These days, it has grown smarter that it can replicate human decision making through an algorithm called Neural Networks. Amazingly, Machine Learning is now able to make logical decisions, sometimes better than the judgment of humans.

 

How does this benefit firms?

 

Even before humans feel the damage caused by fraud, Machine Learning has already finished detecting, reviewing, and stopping the crime. So the only way for a criminal to carry on fraud is for him to outsmart Machine Learning.

 

Big Analytics in Improving Fraud Detection

 

Big Analytics, like Machine Learning is used in analyzing data. However, Big Analytics is only used to see trends and can’t do its own decision making.

 

However, this technology can be used in fraud prevention and detection.

 

One form of fraud usually committed in shopping centers is taking advantage of their return programs.

 

One benefit of your big data analytics can be fraud prevention. As big data analytics uses complex applications like what-if analysis and predictive models, it is quite hard to trick the system. The data it gathers then serves as a reference for you to detect unusual activities, and from there point out attempts of fraud.

 

Another way to up your fraud detection game using big analytics is by connecting it as an integral part of your machine learning system. While machine learning already has its own detection capabilities, its scope can be broaden further through big data analytics, making it highly accurate in gathering trends.

 

However, these advance technologies are far from the reach of the common society. They aren’t available for individual use and investing on them requires some serious funds.

 

So how can they detect and protect themselves from fraud attempts?

 

Prevention Before Detection Techniques

 

As mentioned earlier, small businesses as well as civilians are easy targets in fraud. Most of them neglect the availability of anti-fraud and fraud detection technologies, making them highly vulnerable to attacks.

 

A simple antivirus no longer works in blocking cybercriminals who plan phishing URLs online and on documents as well as text messages. There’s even a new form of fraud called deepfake audio where criminals use deepfake technology to alter their voice, making them sound like they are an executive of a company. Being unaware of the presence of these kinds of modus, a lot of people fall into the traps set by fraudsters and it’s already too late when they realize that they have been robbed.

 

Most antivirus companies already developed anti-fraud software including AVG CloudCalre, Avast Business, and Eset Endpoint Security. These fraud prevention software promise to deliver at least 99.7 percent stronger protection compared to normal antiviruses.

 

Their functions include detecting new phishing trends that are usually the start of identity theft and fraud. There’s also a content filtering option which allows systems to track and block websites being opened on computers. Spam monitoring activities have been doubled too. The most advanced anti-fraud software, though, have data encryption functions as well as data loss prevention.

 

Detection features are also present. However, instead of pinpointing fraud attempts, they alert users instead about suspicious websites, links, and attachments that can open a window of opportunity for criminals to steal data and use them to carry out fraud.

 

Final Words

 

Data is the main weapon used by fraudsters in carrying out crimes. So apart from investing on fraud detection technologies, businesses should also focus on strengthening their preventive measures. All these solutions may sound expensive for the common society, but antivirus software are just worth a few dollars.

 

Make sure that the devices where you store information and save your financial account credentials are well protected by firewalls and strong antivirus.

 

It pays to have a tool in finding the root of fraud attacks, but it pays more to prevent them even before they happen.

 

About the author:

Donna Marie Padua

Engineer turned Writer. Donna found a deeper interest in expressing her thoughts through words rather than numbers. With four years of experience, she has now mastered different writing styles. Writing Technology Blogs for the computer software shop Softvire Australia is her latest stint. After work, she morphs into an anime and TV series fan.

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