Popular Fraud Detection and Protection Updates to Catch in 2021-2022Mayleen Meñez
Cases of identity theft and fraud are still widespread. The intensity of damages they bring to victims is becoming worse, with small businesses and clueless civilians being their primary 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 by millions, the damages incurred managed to reach $1.7 billion. The amount accounts to double the number 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 the UK. The organisation gathered 7,000 respondents who revealed:
- Around 49 per cent of the global organisation respondents claimed to be a victim of fraud. Cases like these are 13 per cent higher than the recorded instances in 2016, accounting for 36 per cent. This value doesn’t even include organisations 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 would have incurred up to $1 million in losses.
- Unfortunately, 52% of all detected frauds were initiated by internal members of the attacked organisation.
These statistics only suggest that scammers are becoming more competent in their strategies. They know who the easy targets are and how to attack them. 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 high-tech-driven, and even cybercriminals leverage innovations to make fraud undetectable. Society and different firms then need a stringent fraud detection system to at least minimise 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 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 independently improving this learning. In the same fashion, machine learning is used in fraud detection to constantly dig into the methods used by cybercriminals in committing fraud.
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 on any fraud attempts in the system. The machine compares all transactions with what is recorded as normal behaviour, and when a suspicious change happens, it alerts its owners of possible fraud.
Fraud attempts aren’t new, though. In the past years, machine learning has been used by insurance companies and similar financial firms to protect them from any modus. However, the difference comes with the analysis and judgment of fraud attempts.
In the past, Machine Learning flags incidents for review by humans. These days, it has grown smarter to replicate human decision-making through an algorithm called Neural Networks. Amazingly, Machine Learning can now make logical decisions, sometimes better than the judgment of humans.
How does this benefit the company?
Machine Learning has already finished detecting, assessing, and halting the crime before humans even notice it. So the only option for a criminal to continue committing fraud is to outwit Machine Learning.
Big Analytics in Improving Fraud Detection
Big Analytics, like Machine Learning, is used in analysing data. However, Big Analytics is only used to see trends and can’t do its decision-making.
However, companies can use this technology in fraud prevention and detection.
Fraud prevention could be one of the advantages of your big data analytics. Big data analytics uses complex applications like what-if analysis and predictive models, and it is pretty hard to trick the system. The data gathered then serves as a reference for you to detect unusual activities, and from there, point out fraud attempts.
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 detection capabilities, big data analytics can broaden its scope further, making it highly accurate in gathering trends.
However, these advanced technologies are far from the reach of everyday society. They aren’t available for individual use, and investing in them requires some severe funds.
So, how can they recognise and defend themselves against fraud attempts?
Prevention Before Detection Techniques
As mentioned earlier, small businesses, as well as civilians, are easy targets of 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 these kinds of modus, many people fall into traps set by fraudsters, and it’s already too late when they realise that they have been robbed.
Most antivirus companies have already developed anti-fraud software, including AVG CloudCalre, Avast Business, and Eset Endpoint Security. This fraud prevention software promises to deliver at least 99.7 per cent stronger protection compared to regular antiviruses.
Their functions include detecting new phishing trends that are usually the start of identity theft and fraud. A content filtering option 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, has 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.
Consumers are becoming more confident in biometric authentication.
Consumers have a high level of confidence in biometric authentication (64 per cent). Consumers are comfortable with the conventional usage of passwords for authentication, but their trust in the security of this approach is eroding as time goes on. As password approvals fell by 10 per cent in 2020, the focus has turned to biometric authentication as a more secure option.
The use of fingerprint scanning (66 per cent), facial recognition (63 per cent), and iris scanning (62 per cent) are considered the most effective methods of verifying identity. The use of these methods increases as consumers become more aware that passwords alone are insufficient to effectively secure accounts and as support for these technologies grows.
Businesses may inspire more trust in their consumers by utilising continuous biometric authentication to decrease fraud risk and increase revenue. These fraud security measures may be implemented from the beginning to the finish of a transaction across all digital platforms, assisting in the ongoing authentication of transactions. Utilising device trust, identity, and behavioural analytics can prevent fraudsters from gaining access to a system through inadequate password management.
Higher client turnover from poor fraud resolution experience
When businesses fail to offer a positive customer experience, they risk losing consumer trust, brand loyalty, and, ultimately, revenue. This situation is factual across all sectors. There are no exceptions for financial institutions, especially when it comes to settling account takeover fraud.
In reality, just one-third of victims of identity fraud was happy with the way their financial service provider addressed their identity fraud problems. It is estimated that the average cost of identity fraud per customer will grow by $3 (USD) in 2020 and that consumers would spend on average 12 hours attempting to address their problems.
Due to a negative resolution experience, 38 per cent of identity theft victims closed the bank accounts where the fraud had occurred.
Communication is essential to improve the customer experience in the areas of fraud resolution and fraudulent behaviour. Businesses may be more honest with their consumers about how their identity fraud is handled if they take the following steps.
Companies that provide financial services to their customers can provide material on their customer-facing channels, including remediation advice and information about what to expect when fraud claims are processed.
Additionally, a client notification procedure may be established using current account alert technologies like email and SMS/text messaging. These steps will assist in setting expectations and avoiding assumptions about what is expected of both parties to reach the most efficient and successful settlement possible.
Fraud risk in digital wallets and peer-to-peer payments has increased.
The use of digital wallets and peer-to-peer (P2P) payments is a third fraud management trend to observe keenly. Approximately 17.5 million digital wallet and peer-to-peer fraud victims have fallen victim to identity theft frauds.
Victims of stimulus-refund-related fraud, jobless money mule scams, and identity fraud scams were all in possession of digital wallets such as Apple Pay and Samsung Pay, as well as peer-to-peer payment services such as PayPal, Square, and Zelle, among other things.
According to the Financial Times, increased P2P fraud assaults are attributed to a lack of investment by financial institutions in payment fraud management systems and a misunderstanding of who is accountable for P2P fraud.
Securing the future of this fraud trend will need a combination of more robust user identity validation, anti-money laundering (AML) procedures, and greater transparency into the users and monies that are being transferred between networks.
Additionally, many users use mobile banking applications accessible through a web browser to conduct sensitive transactions. Financial institutions should make a concerted effort to educate consumers on using only branded mobile banking applications protected against fraudulent transactions by advanced encryption technology.
Conduct frequent site security audits
By conducting frequent site security audits, eCommerce merchants may identify and address weaknesses in their security architecture before thieves and fraudsters become aware of them and attack them. If such audits are carried out regularly, they will help to ensure the following:
- Inspect and make sure that the shopping cart software and plugins are up to date
- Locate both active and inactive plugins to take additional action against dormant plugins.
- Verify that the SSL certificate is functioning correctly.
- Check to see if the eCommerce store is PCI-DSS certified before purchasing anything.
- Check the regularity with which the online store’s backups are performed.
- Check if the passwords used for admin accounts, hosting dashboards, content management systems, databases, and FTP access are strong enough to protect the site.
- Confirm that the website is being checked for malware regularly.
- Determine whether or whether the communication between the store and its customers and suppliers is adequately encrypted.
Data is the primary weapon used by fraudsters in carrying out crimes. So apart from investing in fraud detection technologies, businesses should also focus on strengthening their preventive measures. All of these solutions may appear to be extremely expensive in today’s culture, but antivirus software is only a few dollars.
Ensure that firewalls and strong antivirus well protect the devices where you store information and save your financial account credentials
It pays to have a tool in finding the root of fraud attacks, but it pays more to prevent them even before they happen.