What is fraud analytics?

Fraud analytics can help companies stem payment fraud losses by detecting fraudulent patterns in the data online businesses collect.

Does data science provide a competitive advantage for online fraud prevention?

Data 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. Data analytics also plays a vital role when companies choose an identity verification partner. Robust verification match rates lead to fast onboarding and help companies avoid frustrated customers, higher transaction costs, strain on support and lost revenue.

Payment Fraud Analytics Can Provide a Competitive Advantage

Analytics can form the foundation for fighting payment fraud.

Fraud can significantly reduce profits for ecommerce and other online operations. For instance, a 2023 report estimated that 2.9% 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.

Types of Payment Fraud

An Association for Financial Professionals survey found that 80% of organizations were targets of payment fraud in 2023, an increase of 15% from 2022. By understanding fraudsters’ techniques, organizations can develop defense strategies that don’t diminish customer experiences.

Fraudsters have many different attack vectors.

Card-Not-Present (CNP) Fraud

CNP fraud occurs when someone does not present a card to a merchant, such as during an online purchase, and performs a fraudulent transaction.

New Account Fraud

New account fraud typically occurs within 90 days of opening an account. It’s also referred to as application or account origination fraud.

Identity Fraud

Identity fraud occurs when a person uses another person’s personal data without authorization to deceive or defraud.

Synthetic Identity Fraud

Synthetic identity fraud involves fake identities that are created by combining fake information with real ID data.

Account Takeover Fraud

Account takeover fraud involves fraudulently taking control of an account to access funds, perform unauthorized transactions or gain entry to other accounts.

Chargeback Fraud

Chargeback fraud occurs when a customer asks a credit card company to reverse a charge even though the customer received the purchased item.

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, data warehouses and data lakes can streamline collection from disparate sources. Those sources include account verification processes, transaction monitoring systems, mobile data and suspicious activity reports.

That data can keep businesses secure 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 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.

The 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 an identity verification partner. Robust verification match rates lead to fast onboarding and help companies avoid frustrated customers, higher transaction costs, strain on 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 thresholds.

AI techniques that use large data sets can also create more robust identity checks, such as through biometrics that detect presentation attacks and spoofing attempts.

When companies evaluate identity verification partners, 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 AI and machine learning algorithms
AI and machine learning insights from the data
Data science expertise

Beyond the data analytics, it’s also critical to understand how the processes comply with payments privacy and security regulations, such as those established by the PCI Security Standards Council. Compliance requirements can significantly affect the design and deployment of fraud analytics.

As identity verification 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.