Analytics can form the foundation for fighting payment fraud, mitigating risk and limiting costs.
Fraud can take a significant chunk out of profits for eCommerce and other online operations. For instance, a 2022 report estimated that 3.6% of global revenue was lost to payment fraud.
But fraud costs go beyond the direct losses to include fees, interest, replacement or redistribution, manual reviews, and operational expenses. That same report found that 10% of eCommerce revenue globally goes toward managing fraud.
The fraud fallout extends to intangible, yet crucial, costs, such as reputational damage for companies and lost customer trust.
Analytics can help companies stem those losses by detecting fraudulent patterns in the data online businesses collect.
Gathering Data at Scale
Collecting the right data is the first step toward establishing analytics that can detect and limit fraud.
Data collection systems, regulatory technology solutions, data warehouses and data lakes can streamline collection from disparate sources. Those include account verification processes, transaction monitoring systems, mobile data and suspicious activity reports.
That data can keep businesses secure and on track in fast-moving, complex global markets. But businesses first must overcome the challenges of analyzing the data and gathering actionable insights.
The response to those challenges is in strategies and operations that integrate the different data sources and databases into one model that provides clarity, consistency and improved data quality. Companies can restructure the data to align with analytics requirements, add value to business applications and enable better queries.
The Competitive Advantage of Data Science
Data scientists create, operate and optimize data warehouses for analysis and insights. That work entails analyzing statistics, programming, understanding business requirements and spotting patterns.
Those scientists analyze massive data sets and gather insights that provide the most value to organizations. Spotting anomalies or trends can present new business opportunities and identify fraud threats.
Using Identity Data for Payment Fraud Detection and Prevention
Data analytics plays a vital role when companies choose their identity verification platform. Robust, efficient identity match rates lead to quick, smooth customer onboarding and help companies avoid frustrated customers, higher transaction costs, strain on customer support and lost revenue.
Data analytics also help spot fraudsters and unusual patterns that require further investigation. Ensuring identity data is accurate can stop fraudsters before they create accounts. Companies can also leverage data for ongoing monitoring, such as for transactions that exceed amount thresholds.
When companies evaluate identity platforms, there are key data analytics factors to consider:
- Number of data sources
- Accuracy of data sources
- Variety of data sources
- Data-handling procedures
- Use of artificial intelligence (AI) and machine learning (ML) algorithms
- AI and ML insights from the data
- Data science expertise
As identity technology and data analytics advance, the organizations that gain the most insight into customer activities can build a competitive edge. Those insights can drive performance while closing the door on fraud and protecting customers and the business.