AI is at the top of trending technologies these days.
When it comes to AI, we usually think of its association with robots or human intelligence in machines. Let’s be clear to you it is not just about making automated systems. It has a vast impact on fraud detection and prevention.
No doubt, the advancement of technology has raised the chances of fraud in the online space. Rather than physically, it has become easy to commit fraud online.
According to a report, 74% of people who had been victims of online shopping scams since 2021. In India,42% of consumers have encountered online fraud in the last three years.
Detecting and Preventing fraud has become a matter of concern for every industry. The techniques used by fraudsters are more advanced than the prevention methods used by companies.
According to the source, the occurrence of scam activities rose by more than 28% after the spread of the COVID-19 pandemic.
To tackle fraudsters, Businesses are gearing towards building advanced AI-driven fraud prevention frameworks with the help of software development companies.
Did you know?
Around 42% of small businesses are more liable to fraud due to a lack of anti-fraud measures and controls, and 25% of larger organizations follow such mistakes.
If you are an enterprise owner, it becomes crucial for you to maintain anti-scam measures by implementing advanced Artificial Intelligence technology into your system.
If you are unaware of how AI aids in fraud detection and prevention, this blog is for you. In this blog, we will discuss all those AI solutions and strategies to help your business prevent from hoax.
Ways how AI solutions can detect and prevent fraud:
(a) IP Analysis:
The first way will analyze the IP address of the user who wants to purchase. Artificial Intelligence enables businesses to find out a person’s area. Top of that, it allows trades to match the location with the billing address.
(b) Device Analysis:
Device analysis identifies the device type, operating system, browser, and other prominent parameters. AI development solutions can help companies recognize devices if it is used in the past to engage in fraudulent payments. Fraudsters may use multiple devices to commit fraud, like a laptop, mobile or tablet that could be stolen or even shoplifted. The ability to identify different models and make these devices through AI can help businesses understand whether a device is new or used before.
(c) Phone analysis:
Artificial Intelligence-based solutions can help businesses to authenticate a customer’s phone number in real-time. It is crucial because a fraudster may use a Voice over Internet Protocol (VoIP) numeral or other sophisticated methods to commit fraud. With AI, businesses can understand whether the number is a VoIP or an authentication.
In addition, artificial Intelligence can assist businesses with analyzing call data records, as well as analyzing all the incoming/outgoing calls and identifying patterns. For example, if a fraudster is using the same fake number to log into different e-commerce sites and proceeding to commit fraud.
(d) Email analysis:
AI implementation can help businesses automatically analyze email addresses to detect and prevent fraudulent activities. By analyzing the email addresses, enterprises can understand whether the mail address is real or fake, including its location and other crucial details.
(e) Billing address analysis:
Fraudsters often target e-commerce businesses by using fake invoices to collect payments. However, deploying artificial intelligence (AI)–powered fraud detection can prevent fraud from occurring in the first place. AI can carefully examine customer information, payment details, invoice details, and other relevant data before a payment. It thoroughly analyses historical data of both valid and fraudulent invoices and tries to map out any repeated patterns that indicate possible fraud.
(f) Credit Card analysis:
In today’s world, there are multiple uses of Artificial Intelligence, and one of those ways is in the credit card industry. AI is used to determine the type of credit card, the issuing bank, and the country of origin. It is done by automatically reviewing a customer’s credit card details. With AI technologies, companies can identify whether the credit card is lost, real or fake. Moreover, AI can detect if the credit card is from a high-risk country or location where fraud is frequent.
(g) Social Media analysis:
It is a way of analyzing the customer’s social media profiles to understand their identity. AI-powered services can help businesses automatically scrutinize their social media profiles. With AI, companies can understand the users’ names, ages, gender, interests, and other vital details. Moreover, AI can help businesses to understand consumers’ social media behaviour.
Strategies to follow:
(a) Putting behavioural analytics into practice:
Behavioural analytics use AI to examine the users’ behaviour to determine if the transaction is legitimate or fraudulent.
It can be used across all channels, including online, physical stores, and mail-order catalogues. Behavioural analytics looks at several factors to determine if a customer is legitimate, including the time between orders, the age of the users’ account and the number of items purchased relative to the average order size.
Behavioural analytics allows us to spot money laundering, identity theft, and other forms of fraud.
For example, if a customer attempts to use a brand new account to place an order for a large number of items that are priced at a much higher level than average, the system may flag the transaction as potentially fraudulent.
Behavioural analytics can also be used to identify customers who are likely to purchase higher-priced items.
For example, if a customer has purchased several lower-priced items with a certain frequency and amount, but then purchases an item that is priced much higher, the system may flag that as a potential warning sign.
(b) Using Supervised and Unsupervised AI Models Together:
Supervised and unsupervised machine learning algorithms are often used together in fraud detection frameworks. Supervised models learn to identify the relationship between different variables while unsupervised algorithms discover hidden patterns in data. Combining these two methods results in highly accurate predictive models. For example, a supervised model can determine which items are purchased together. An unsupervised model can then determine which items are purchased together most frequently relative to the average rate of purchase for each item. That information can then be used with other data to determine if a transaction is fraudulent or legitimate.
(c) Developing Models with Large Datasets
Fraudsters are constantly discovering and exploiting new weaknesses in systems. To stay one step ahead, fraud detection algorithms must be able to adapt quickly to new types of fraudulent activity. One way to ensure that fraud detection models can quickly adapt to new types of fraud is to increase the size of the dataset used to train the model. A large dataset will have more examples of normal and fraudulent behaviour, which will enable the model to become more accurate. Additionally, the dataset can be segmented to identify different types of fraudulent behaviour, which will allow the model to become more accurate than a model that only has one type of fraudulent behaviour in the dataset.
(d) Self-Learning AI and Adaptive Analytics:
The most advanced fraud detection models use self-learning algorithms and adaptive analytics. Self-learning algorithms use supervised machine learning to determine which features in the data are important and how they should be weighted. For example, a self-learning algorithm may determine that the dollar amount of each transaction and the dollar amount of each item in the transaction are important features. Adaptive analytics also use supervised machine learning to determine how the model should respond to new data. The model can change the weights of the features and it can also change how it responds to new data.
How can we help you?
As a digital transformation company, we have expertise in every aspect of development. Top of that, We have an excellent team for intelligent automation services like big data, machine learning, Artificial Intelligence, the Internet of Things, and others.
If you are a business owner, running a retail or financial service, and want to implement advanced intelligence technology to prevent fraud in your business, we can assist in the best way.
Along with the development, we will assist you with the marketing strategies for your business.
Final Thoughts:
There is a clear need for AI in the fraud detection and prevention arena.
As the world inches ever closer to becoming a cashless society, there will be even more opportunities for digital fraudsters to slip through the cracks and wreak havoc in all sorts of new ways.
With enough data and unsupervised machine learning capabilities, however, businesses stand to benefit from an AI solution designed to detect and prevent digital fraud.
In the end, it’s important not to forget about the customer experience. Businesses must strike a delicate balance between fraud prevention and customer experience if they want their AI-powered solutions to pay off!