5 Efficient Ways AI-driven Verification Tools Prevent Fraud
Online fraud has been around for as long as the internet itself. Bad actors are keen on finding loopholes in the system, either trying to make a quick buck or disrupting entire systems and businesses. Online frauds include phishing emails, identity theft, credit card scams, and spyware/ransomware attacks, to name a few. Frauds are obviously dangerous and disruptive, but what makes it worse is that the responsibility of defending against these falls almost exclusively on end users and business owners.
And businesses suffer the most when it comes to online fraud. Frauds cost businesses and consumers billions of dollars in penalties and repayments. The most common type of fraud comes in the form of synthetic identity thefts or when scammers pose as imposters to individuals. Synthetic identity theft is among the fastest growing online frauds where attackers fabricate identities using a mix of real and fabricated data (most notably social security numbers). Some reports suggest that synthetic identity frauds alone cost financial institutions a whopping $20 billion in 2020.
Ultimately avoiding online fraud comes down to institutions and businesses investing and relying on fraud prevention services and products. The fraud prevention and detection market is expected to grow to $190 Billion by 2030 with a CAGR of 23%. That indicates that fraud detection and prevention is an urgent need, and businesses and institutions are willing to pour in the capital to fulfill it.
So how do we address online fraud? Traditionally, this has been a cat and mouse game between scammers and fraud prevention professionals. Fraudsters have consistently found newer, more clever ways of working around the system. With the advent of modern tech that makes techniques like data mining and automation possible, fraudsters certainly have the edge over fraud prevention professionals and cyber security companies. So how do we address the growing number of attacks and scans that threaten to destabilize our businesses? The answer, again, is technology.
How AI is Powering Verification Tools for Fraud Detection
Artificial intelligence is emerging as a potent solution for addressing the problem of growing online fraud. In today’s fast-paced, hyper-connected world, the traditional ‘retrospective review’ approach to fraud detection simply isn’t practical. Attackers are too quick, and real-time manual reviews are certainly out of the question.
Although, all isn’t lost since technology can step in when humans begin to struggle. It is now possible to train AI models that can accurately predict anomalous behavior and stop a scam in its tracks even before it occurs. It is a real-time, scalable solution that holds the potential to transform the fraud prevention sector forever. So how does it work?
AI is best deployed in fraud detection at the level of behavioral analysis. This works by deploying machine learning algorithms that can detect anomalous behavior even before any transactions are made. These ML algorithms are trained (either supervised or unsupervised) based on tons of available consumer and institutional data.
Implementing machine learning in fraud detection offers a considerable advantage because it makes the algorithm a lot more flexible than traditional rule-based algorithms without compromising accuracy or speed. To further understand how AI is transforming fraud detection, let’s take a look at the five ways AI-based verification tools prevent fraud.
1. AI Tools can Secure Bank Transfers
When speaking of online frauds, banks are obviously a big target. Fraudsters that manage to get their hands on a user’s bank details are sure to cash out as soon as possible. Financial institutions, therefore, need to be quick with fraud detection. Banks also need to operate on a large scale, processing thousands of transactions every hour.
So how can banks use AI to detect fraud within their system? As mentioned, AI can scan and track large amounts of user data to look for anomalous behavior. Essentially, the system works by profiling users based on their transaction history, payment and bill schedules, purchase history, device and demographic information, etc. All of this comes together to create a user profile, which helps the bank’s AI predict the type of transaction one is likely to make.
On top of that, banking frauds like unauthorized transfers or identity theft rarely look like your ordinary transactions on your bank’s books, playing further into the hands of fraud detection AI. Using AI, financial institutions can assign a fraud score to each transaction based on several factors like historical translation data, user’s IP address, transaction amount, etc., making online fraud easy to track, stop and investigate.
Banks opting for digital Know you customer (KYC) methods can leverage AI to quickly verify and secure users as well.
2. Securing E-Commerce Sellers and Online Retailers
In the digital era, being able to scale a business is critical. E-commerce retailers and virtually any business that sells online intend to enable digital payments precisely so they can scale their operations and trade globally. Selling online might bring in a ton of revenue and opportunities, but it isn’t free of risks and dangers.
Online retailers have a huge target on their back, often directly proportional to the scale and volume of their business. Once again, like banks, retailers cannot monitor every order and transaction placed on their platform. This is where AI can be deployed for real-time scanning and fraud detection.
Like banks, businesses rely on the vast amount of data available to them to predict potential fraud. The algorithms make predictions based on several factors, like shipping address, card details, user purchase history, order size, etc.
Juniper Research predicts that online merchants will lose more than $343 billion in the coming five years thanks to online payment frauds. Therefore, businesses need to invest in AI-based fraud prevention technologies.
3. AI Can Also Help Secure Consumers Against Frauds
When defending against online fraud, it is as important to secure your customers as it is to defend the business/enterprise side of things. Consumer devices are constantly at risk from dangers like phishing emails, man-in-the-middle attacks, unsolicited downloads, etc. It is incredibly difficult these days to safeguard your sensitive information online. This is also why identity theft and account takeover (ATO) incidents are rapidly growing.
Once aging, AI can help us here in a number of ways. Machine learning algorithms can be deployed, this time on the consumer side, to protect against such attempts. Take phishing, for instance. Dedicated phishing detection models are trained by feeding the algorithms large amounts of email data from legit and fraudulent sources.
The algorithm eventually learns to identify the difference by recognizing patterns that would have otherwise been invisible to us. The beauty of the system is that often these algorithms rely on a type of self-learning (more on this below) which makes fraud detection significantly easier.
Similar implementations can also work for identity theft and credit card fraud on the consumer side. This is achieved by deploying algorithms that closely monitor user behavior using algorithms trained on historical user data.
4. AI powers Robust and flexible solutions
Let us take the time to discuss what makes AI so powerful in fraud detection in the first place. As mentioned, attackers these days are quick and rely on modern tech to evade detection mechanisms, which also implies that manual fraud detection will be impossible. This requires a solution that is both dependable and flexible.
AI can achieve this balance by combining supervised and unsupervised learning. Supervised learning refers to training algorithms based on ‘labeled’ data. They are fed data sets that clearly differentiate data associated with legitimate transactions from fraudulent ones. This helps the algorithm learn patterns that are typically characteristic of online frauds.
Unsupervised training takes a different approach where the data isn’t labeled or segregated. The algorithm here conducts a type of ‘self-learning’ and begins to identify anomalous fraud patterns without supervision. This approach comes especially handy when available data is thin or unstructured.
Both of these styles of training algorithms have their unique advantages, and combining the two produces the best results in the long term. Supervised learning, for instance, gives AI developers direct command over the algorithm. They can specify the risk rules and even manage false positive rates. With supervised learning, more data generally equates to better accuracy. On the other hand, unsupervised learning helps detect outliers and unique anomalies, making them excellent tools for detecting newer, never-seen-before scams.
Combined together, supervised and unsupervised learning helps us train fraud detection algorithms that are not just fast and reliable but also flexible enough to learn new patterns and detect scams that might not have been a part of the original training set.
5. AI is a cost effective and scalable solution
The final and perhaps the most important way in which AI is helping out with fraud detection is by making fraud detection tools more accessible. Think about it for a second, without scalable and automated fraud detection tools; the review process would be completely manual. As a consequence, most businesses and organizations simply wouldn’t be able to afford fraud detection teams; and the ones who could, would certainly be overworked.
Even fraud detection programs that manually need to be programmed with risk rules are only as good at detecting fraud as their programmers. For a problem as complex as fraud detection, we need solutions that can evolve and scale at once, and AI fits the bill perfectly here.
Institutions and businesses can hire Artificial intelligence companies to build custom solutions specifically designed to counter the type of fraud that threatens them the most. If not, there are always prebuilt tools and programs available that can do the job for you. The broader point here is that AI-based fraud detection tools have made fraud prevention really accessible, cost-effective and scalable.
To Conclude
Fraud detection and prevention is a growing challenge that needs to be addressed effectively and efficiently. With the advent of AI, cyber security can finally keep up with the rapid growth and scale of online frauds. Businesses and institutions can finally secure their platforms, and end users can finally breathe a sigh of relief being guarded by cutting-edge algorithms that evolve as fast as attackers themselves. How do you think AI will transform fraud detection? Will it be the ultimate weapon to address online fraud, or will fraudsters continue to find clever ways of cheating the system?
