How to improve your ecommerce fraud detection by improving data quality

Data quality is crucial when you are in the ecommerce space, as it is directly related to your customer’s safety. With incomplete and inconsistent data, your fraud detection e-commerce system can’t detect fraud activities in online transactions.

Sometimes you have old or incompetent customer details that make it difficult for the detection system to identify suspicious activity. In this article, we will explain how improving data quality will transform your online ecommerce transactions.

Importance of Data Quality in Fraud Detection

Customer will do transaction online when they feel safe about their privacy and security. It’s up on the fraud prevention platforms, how they develop their system. If the system lacks quality data, which is important to detect real-time fraud activities, there are chances that your ecommerce business will struggle to protect customers’ safety.

Many online businesses, especially e-commerce stores, have strong instructions for developers for security measures. But, the developers do not know much about the quality data, instead they use false or poor data which results in a poor security system to detect frauds.

Impact of Data Quality on Detection Accuracy

High quality data always delivers exceptional results as compared to businesses with a fraud prevention system trained on outdated data.

Ecommerce businesses receive payment transactions from multiple sources. The high quality data ensures that it analyzes each transaction in detail and informs the business owner about any suspicious activity.

Similarly, if the system is trained on AI and big data sets, it will automatically detect risk associated with transactions in advance. As a result, the system reports these types of Fraud attempts to business owners via emails or any particular connection.

Understanding Data Quality Improvement for Fraud Detection

We have already discussed how important the data quality is in analyzing the fraud activities, now we will see its key components that play an important role in qualifying the data needed to stop frauds for your ecommerce business

Key Components of Data Quality

Accuracy: Any system with poor accuracy can’t be successful in Fraud prevention for e-commerce, where multiple transactions are occurring at the same time. The job of these systems is to monitor transaction details including customer ID, credit card details, and previous shopping history. If the system has no accurate data, then there are chances that it may fail in detecting frauds.

Consistency: Consistency means that data remains the same to detect fraud activities from multiple resources. This feature eliminates the need for updating data again and again. However, if there is a major change of business payment model, then the fraud detection system will be modified according to the new setup, but overall it is almost the same for regular transactions.

Timeliness: It is recommended to inform your fraud detection system to timely update the data whenever you implement new security protocols. This way you can update the data with your new security system. In e-commerce, you will notice fraud patterns evolve quickly, and if you don’t take the necessary actions, then the outdated information can lead to missed fraudulent transactions .

Completeness: E-commerce business demands complete information available for accurate fraud detection. If the system has missing or incomplete data, it can create blind spots, making it easier for fraudsters to exploit gaps in the system. For example, if a transaction record lacks details like IP address, the fraud detection system may struggle to verify its legitimacy.

Common Data Quality Issues in Fraud Detection

Duplicate Records

Duplicate records are one of the major issues that the system is unaware of and creating challenges. It happens when the customer or transaction data is stored multiple times due to system errors or integration issues between platforms, then it becomes difficult to track fraudulent activities. In this case, fraud detection systems may miss fraudulent transactions by treating duplicate records as separate entities.

Missing Data

Another issue is missing data which can weaken the accuracy of risk assessments. If the important details like customer location, transaction history, or device information are missing, then how can AI identify patterns? And, fraudsters can easily take advantage of this loophole to hack the algorithm. So, businesses should implement data validation processes that ensure required fields are filled before processing transactions.

Inconsistent Formatting

It is recommended to check the system before implementing it because inconsistent formatting can cause errors in identifying fraudulent activities. If there are any variations in date formats, address structures, then the AI models will create mismatches in the transaction records. The solution is simple by standardizing data entry formats across all systems.

Data Cleaning and Standardization Techniques

Deduplication

Deduplication is the solution to the duplicate records issue, in which it identifies and removes duplicate records in a dataset to improve accuracy and efficiency in fraud detection. Duplicate records are common due to multiple entries of the same transaction, but with deduplication e-commerce businesses can ensure that fraud detection systems analyze clean, unique data.

Address Verification

Address verification works like a filter in the data cleaning technique that ensures the accuracy of customer-provided addresses. In fraud cases, fraudsters use fake or mismatched addresses to conduct fraudulent transactions. So, businesses need to validate address information before processing payments.

Data Validation

Data validation will monitor the entered information and see whether the information meets predefined accuracy, consistency, and format standards or not. In fraud detection, data validation identifies incorrect entries, or suspicious patterns that could indicate fraudulent activities. The process is simple, and the system will set rules and constraints to identify the entered information to see if it follows a specific format or not.

Conclusion

Every e-commerce business nowadays is struggling with the rise of online fraud attempts, although they take basic steps to protect their business, but fraudsters use advanced technology to hack the system.

To protect your business, you have to improve the data quality of your fraud detection system to eliminate the limitations like duplicate or missing data, along with inconsistent format of data. With these data cleaning techniques, your business can stop these fraud attempts.