AI-assisted fraud detection – how it works and 5 key benefits

2023-05-19T15:21:57+02:00

Despite the advancements in ecommerce Fraud remains a persistent problem that plagues businesses and customers and causes huge financial losses for all parties in the process. In 2022, ecommerce's online fraud losses globally reach $ 41 billion. That figure is expected to reach $ 48 billion by 2023, representing an increase of 17%. As the technologie progresses, fraudsters have more access to more sophisticated and complex methods to carry out their criminal activities. Therefore, ecommerce brands must respond by leveraging the power of AI-assisted fraud detection to combat the growing threat of these fraudsters.

WHAT IS AI-ASSISTED FRAUD DETECTION?

AI-assisted fraud detection is the use of artificial intelligence algorithms and machine learning techniques to detect and prevent fraudulent activity. It involves the analysis of large amounts of relevant data to identify patterns in real time that are highly suggestive of various types of suspicious and fraudulent actions. It represents a significant improvement over alternative methods of fraud detection, which have become obsolete due to reduced data processing capabilities, slower detection time, and other limiting characteristics.

Using AI and ML, ecommerce companies can discover many types of online fraud. This includes payment fraud involving lost, stolen or counterfeit cards. AI can also track ongoing real-time transactions and block fraudulent orders from processing. Additionally, this powerful resource can expose attempts to use stolen identities to complete purchases or process loans. Consequently, the adoption of faster and more efficient AI-assisted fraud detection will improve the security of online transactions and help businesses gain the trust of their customers.

HOW DOES AI-ASSISTED FRAUD DETECTION WORK?

Key artificial intelligence features that set the technologie apart include the ability to analyze massive amounts of data in seconds, identify patterns that people and other software often overlook, and improve itself by learning from historical data . All of these properties apply very well to detecting fraudulent activity. AI models can block or flag suspicious transactions for further investigation when detecting potential fraud. The models can also assess the likelihood that pending transactions are illegal.

Different algorithms and techniques are used in isolation or in combination to uncover ecommerce fraud and avoid huge losses. The main ones used in AI-assisted fraud detection are:

MACHINE LEARNING

Machine learning is the AI subset most commonly applied in fraud detection. ML is trained with historical data to learn patterns. It can then screen transactions and online payments with knowledge of patterns and deviations and determine the legitimacy. Various machine learning algorithms are used in this process. Decision trees are perhaps the most popular algorithms because they are easy to interpret and versatile in handling different types of data. Logistic regressions, used for binary classification tasks, can easily classify activities into fraudulent and legitimate, making them easy to implement. Other algorithms, such as random forests, supporting vector machines, and deep learning algorithms, are useful for even more complex analysis and accurate fraud detection.

NATURAL LANGUAGE PROCESSING

Another AI subset applicable in fraud detection is natural language processing, which focuses on interactions between computers and human language. Text analysis is an example of an NLP technique and can be applied to the analysis of emails, social media posts, chats and other forms of text data to detect keywords or phrases commonly associated with online fraud. NLP can also recognize fraudulent documents such as IDs, invoices and passports. It does this by analyzing the text and metadata of documents for anomalies and inconsistencies. Entity Recognitions can extract names, addresses, contact details and other relevant data and identify potential mismatches based on mismatches. For example, it can detect addresses that don't match known customer addresses and instantly flag such transactions.

PREDICTIVE MODELS

Predictive modeling involves using statistical algorithms to analyze historical data and then make predictions. This can be applied to AI-assisted fraud detection to predict future attacks so companies can take definitive steps to block fraudulent activity and protect themselves and their customers. To use this technique, companies must first collect historical data, including past transactions and fraud attacks. The data is analyzed to identify predictive patterns which are then used to develop predictive models. Ongoing training for this model is done until it can reliably predict possible illegitimate transactions.

CLUSTERING AND ANOMAL DETECTION

Clustering and anomaly detection are two important techniques useful for AI fraud detection. While data is clustered based on similarities, anomaly detection identifies datasets that deviate markedly from the norm. In the context of fraud detection, clustering can be used to group transactions with similar locations, times and amounts into clusters for further analysis. This is particularly useful in the case of coordinated attacks where multiple fraudulent activities occur at the same time or at the same time. On the other hand, anomaly detection is useful for identifying transactional data that is unusually different from previous data and quickly drawing attention to such unexpected patterns. These two algorithms are best used in conjunction with other AI techniques to improve accuracy and early detection.

WHAT ARE THE BENEFITS OF FRAUD DETECTION WITH AI FUNCTION?

Fraud is often a cause of significant financial losses for ecommerce brands, requiring an efficient method to identify and block such activity. In many ways, AI-assisted fraud detection is the answer to the problem faced by business owners in various fields, including ecommerce, are confronted.

Here are five key benefits of entrusting AI to help detect fraudulent activity for your business:

IMPROVED ACCURACY

With AI-assisted fraud detection, your business can rely on the accuracy of activities flagged as illegal. This is because AI uses complex and robust algorithms that analyze massive amounts of data, from structured to unstructured. In addition, AI learns from the data, which means it improves as it is fed with more data. This machine learning capability also makes such fraud recognition systems adaptable enough to detect and respond to new future strategies that fraudsters might use.

REAL-TIME DETECTION

AI-assisted fraud detection can detect illegal actions while they are still in progress, giving businesses the necessary information to take immediate action to protect themselves. This is especially important because some fraudulent activity is difficult to reverse. With AI, your company can stop such activities during the provisioning process and avoid catastrophic losses.

SCALABILITY

Because AI isn't limited to any amount of data, brands don't have to worry about upgrading their systems as businesses expand. AI-assisted fraud detection maintains its accuracy as your business grows and processes more transactions and larger amounts of data.

COST EFFICIENCY

Using AI to detect wrongful transactions contributes to cost savings in several ways. For example, it reduces the need for human input, eliminating the cost of manual reviews and surveys. Time saved by automating these tasks can also be spent on marketing and sales activities for higher revenue generation. AI also reduces avoidable costs such as legal fees, fines and refunds of stolen money.

IMPROVED CUSTOMER LOYALTY

It is impossible to put a price on customer loyalty. With AI-based fraud detection systems, companies can better protect their customers and their personal and financial information. This helps improve the customer experience and brand reputation, secure the trust and loyalty of existing customers, and even attract new customers.

In conclusion, AI-assisted fraud detection offers companies a reliable method to combat the persistent fraud problem. Investing in this technologie is crucial as fraudsters are also updating their strategies for defrauding companies and customers. So now is the time to take the power of artificial intelligence and leverage machine learning algorithms to protect your business in the long run.

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