A guide to leveraging data analytics in fraud management

The cost of online fraud rises every year for small businesses in the US1. Retailers in particular are losing $100 billion per year to return fraud and bots2, according to data gathered by Riskify. Preventing, detecting, and investigating fraud is therefore critical to protecting organizations from financial and reputational damage.

Data analytics is a powerful tool for fraud prevention, helping stakeholders review automatically detected anomalies in their digital environment and build models trained to address the origins of online fraud. The market for fraud analytics market is growing, almost doubling in value in just two years, from $34.6 billion in 2021 to $63.5 billion in 20233.

Understanding fraud in small businesses

38% of global businesses with less than $100 million in annual revenue experienced online fraud between 2020 and 20224.Phishing, data breaches, denial of service (DoS), malware, and ransomware are some of the most common fraud types4.For merchants and ecommerce businesses handling payments, chargeback fraud, card testing, and identity theft are also widespread fraud risks5.

Understanding fraud is key for successful fraud prevention. Businesses need to proactively protect customer data, identify early warnings, and prepare thorough data audits. Otherwise, small business fraud can lead to fines from regulators, lost revenue, and reputational damage.

The role of data analytics in fraud management

Data analytics tools allow businesses to automate fraud detection methods, giving teams the power to identify fraud patterns and mitigate risk in large data sets.

Data analytics software uses multiple methods simultaneously to speed up fraud detection, analyze vast amounts of data, and help small businesses stay ahead of hackers. Machine learning and automation can adapt to evolving threats, prioritizing the most effective detection methods.

Automated fraud prevention strategies and traditional, manual anti-fraud measures can complement each other effectively. Analysis conducted by internal and external auditors can uncover issues that can be incorporated into the data modeling used to define the variables used in detection. Additionally, data analytics can inform staff training by showing team members what phishing and payment fraud attacks look like.

Collecting relevant data for analysis

Customer profiles and payment information, transactions, device information, and geolocations can all be analyzed by fraud prevention software. These data points can help identify suspicious behavior, detect relationships between accounts and transactions, and compare this across channels. The more relevant and consistent the data sets, the more accurate fraud detection can be.

Implementing data analytics tools

There are a variety of data analytics tools for fraud prevention available:

  • SEON
  • Fingerprint
  • Fraud.net
  • Ravelin
  • Kount
  • Riskified

Before choosing one, consider your business’s priorities and the features available. An ecommerce business may achieve greater accuracy with data analytics software designed specifically for online merchants. Other businesses might benefit more from fraud detection tools that also optimize customer conversions or access open data from social media.

A high-quality data analytics tool will also integrate seamlessly with a business’s environment and existing databases.

Data analysis for fraud detection

  • Analyze user behavior. Fraud detection automation will observe normal use behavior on a business’s app or website and compare it to suspicious users and bots. Real-time monitoring will consider transaction type, volume, and frequency, as well as individual user information from registered social media profiles.
  • Detect anomalies. Small businesses can uncover fraud patterns as they emerge with machine learning and monitoring. As well as analyzing individual users and customers, fraud detection techniques can review website traffic, payment data, and customer locations. For example, if a customer based in one country makes a transaction on another continent, data analysis tools can block this transaction before it becomes “card not present” (CNP) fraud. 73% of all card payment fraud in 2023 was CNP fraud, losing $9.49 billion for global businesses6.
  • Provide seamless customer experience. Machine learning for fraud prevention can also reduce friction at checkout, speeding up transactions and maintaining customer trust with efficient two-factor authentication. This can increase conversion rates and reduce costly chargebacks. Chargebacks are increasing by 20% each year and are predicted to cost businesses $117 billion in 20237.

Benefits of data analytics in fraud management

53% of online merchants surveyed planned to prioritize data analytics in 2023, while 41% will be improving automated detection accuracy5.

Improved accuracy results in greater early fraud detection, thereby preventing incidents that damage customer confidence and bottom lines. Cost-effective fraud prevention can also help businesses avoid regulatory fines and audits, reputational damage, and lost revenue.

The benefits of fraud analytics are clear: the percentage of ecommerce revenue lost to payment fraud globally has reduced from approximately 3.6% in 2022 to 2.9% in 20235.

Challenges and considerations

Implementing data analytics for risk management can highlight resource constraints. Some businesses may not have the technology, time, or expertise to effectively manage information generated by some fraud detection tools. There may also not be enough data in a business’s environment to accurately inform AI-based data analytics tools. That makes it essential to ensure businesses only deploy systems they can effectively manage and use.

Similarly, new tools that are still learning detection rules may use incorrect information to identify suspected fraudulent customers. This could affect customer experience and lead to normal shoppers being flagged as suspicious. Rapidly changing consumer behavior can also take time for machine learning to accurately detect.

Using a third-party fraud detection tool may expose a business to fraud through a data leak. 98% of businesses worldwide are reportedly connected to a breached third-party vendor8.

Fraud prevention best practices

  • Implement the most appropriate fraud detection solution. Different tools are designed for different sectors, internal teams, and business sizes. Choosing the most appropriate software can protect a business from the most likely causes of fraud and can keep managing long-term risk effectively. For example, an ecommerce business trying to cut down on CNP fraud should look for an AI fraud detection tool that prioritizes online payment fraud. Businesses requiring real-time user behavior analysis will benefit more from a sessional system that collects all user data.
  • Train staff to utilize fraud detection software. The majority of SMBs plan to maintain some methods of manual fraud detection in 2023 alongside data analytics implementation5. Staff training will help teams respond appropriately to suspicious behavior flagged by AI and train machine learning software to profile users accurately.
  • Continually audit and monitor threats. AI is helping fraudsters to develop more sophisticated methods and disguise activity. As well as flagging risk and responding appropriately, proactive fraud prevention strategies must continually improve and adapt to the changing fraud landscape. Understanding emerging threats and training fraud detection tools to identify them can significantly reduce disruption.

Using PayPal for fraud protection

PayPal’s fraud protection tools use risk intelligence and machine learning technology to manage risk 24/7.

Informed by data from 1 billion monthly transactions and a global consumer network, PayPal’s fraud detection technology gets smarter with each transaction. Businesses of all sizes can benefit from seamless dispute resolution, chargeback protection, and reduced false declines – with no extra charges.

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