Impact of AI on Fraud Detection and Prevention

Impact of AI on Fraud Detection and Prevention

AI is one of the trending technologies that are related to human intelligence or robots in machines. Besides building automated systems, it has a significant impact on fraud detection and prevention. Technological advancements have increased the chances of fraud in the digital space. Committing online fraud has become easier than ever.

Fraud detection and prevention have grown to be a big concern for all industries. Fraudsters use more advanced techniques than general prevention methods implemented by companies. As an enterprise owner, you should take anti-scam measures by using AI-based systems.

Want to know how AI helps in fraud detection and prevention? Here, you will learn the most relevant things about the impact of AI on fraud detection and prevention. Let's dive in.

How AI Solutions Can Detect and Prevent Fraud

Here are the ways AI solutions help in identifying and preventing fraud:

IP Analysis

Analyzing the IP address of a user interested to purchase helps companies to identify fraud. AI helps businesses to identify a person's area. Plus, it helps trades to suit the location with a billing address.

Device Analysis

Device analysis finds out the type of device, OS, browser, and other key parameters. AI development solutions can help companies identify devices if they are used earlier to engage in fraudulent payments. Cyberattackers may use several devices, such as tablets, mobile phones, or laptops, to commit fraud.

The capabilities of identifying various models and building such devices via AI can help brands understand if a device is new/used before.

Phone Analysis

AI-based solutions can help brands to authenticate customers' phone numbers in real time. A fraudster may use a VoIP numeral or other advanced methods to do fraud. With the help of AI, businesses can understand if the number is a VoIP/an authentication. 

Plus, AI can help businesses in analyzing call data records, incoming or outgoing calls, and understand patterns. You can see if a fraudster is using a similar fake number to log into various eCommerce websites and do fraud.

Email Analysis

AI implementation can help brands automatically analyze email addresses to detect and prevent fraudulent activities. Companies can understand if the email address is real/fake, including its location and other important details.

Billing Address Analysis

Fraudsters tend to target online businesses by using fake invoices to receive payments. AI can examine customer details, payment information, invoice, and other related data before making a payment. It thoroughly analyzes historical data of both fraudulent and valid invoices and attempts to map out repeated patterns that indicate possible cyberattacks. 

Credit Card Analysis 

Nowadays, AI is used in the credit card industry to find the type of credit card, the country of origin, and the issuing bank, by automatically checking customers' credit card details. AI helps companies to identify if the credit card is lost, real, or fake. Plus, AI can detect if the credit card belongs to a high-risk location/country with frequent fraud cases. 

Social Media Analysis

AI-powered systems can help companies automatically scrutinize customers' social media profiles. Companies use AI to understand users’ names, ages, interests, and other relevant details. Plus, AI helps businesses to understand consumers’ social media behavior.

AI-Based Fraud Detection and Prevention Strategies

Here are the strategies you can follow:

Apply Behavioural Analytics

Behavioral analytics use AI to monitor users’ behavior and understand if the transaction is genuine/fraudulent, including the time gap between orders, users' account age, and total items bought relative to average order size.

The analysis can be done across all channels, including physical and online stores and mail-order catalogs. Behavioral analytics help to identify customers who may buy higher-priced products.

Use Both Supervised and Unsupervised AI Models Together

Supervised and unsupervised ML algorithms are mostly used together to detect fraud. Supervised models can identify the relationship between variables and unsupervised algorithms find hidden data patterns. The combination of these two methods leads to precise predictive models.

A supervised model can determine the items bought together. An unsupervised model can identify the items purchased together most frequently as compared to the average rate of purchase for every item. The information can then be used with other data to understand if a transaction is legal or fraudulent.

Build Models with Big Datasets

Fraudsters are consistently finding and exploring the weak points in systems. Fraud detection algorithms should be able to quickly adapt to new types of fraud activities. Increase the dataset size used to train the model to ensure fraud detection models quickly adapt to new kinds of fraud.

A big dataset will have more typical and fraudulent behavior, which enables the model to get more accurate. Plus, the dataset may be segmented to identify fraudulent behavior which allows the model to get more accurate than a model with a single type of fraud behavior in the dataset.

Self-Learning AI and Adaptive Analytics

The cutting-edge fraud detection models use adaptive analytics and self-learning algorithms. Adaptive analytics use supervised machine learning to find how the model must respond to new data. The model can adjust the weights of features. It can also change its reactions to new data.

Self-learning algorithms use supervised ML to determine which data features are important and how to weigh them. For instance, a self-learning algorithm may determine that the dollar amount of every transaction and item in the transaction are crucial features.

The Takeaway

AI is dominant in fraud detection and prevention activities. As the world has almost become a cashless society, digital fraudsters will get more opportunities to slip through the cracks and create new havoc. With sufficient data and unsupervised ML capabilities, businesses tend to benefit from an AI solution that can detect and prevent digital fraud.

Ensure you never forget about the CX. Businesses should strike the perfect balance between fraud prevention and CX  if they want AI-powered solutions to deliver the desired results.

When it comes to AI solutions for fraud detection and prevention, it takes a deep understanding and expertise in the development and deployment of such systems. Therefore, it is always recommended to hire a professional technology consulting partner like Kulsys which has extensive knowledge, experience, and resources to develop customize solutions that can address your specific business needs. A trusted AI services provider like Kulsys makes sure to deliver the best AI solutions for fraud detection and prevention so that your business can mitigate the risk of fraud, boost compliance efforts, and keep your customers safe from any harm.