Fraud Prevention

Payment fraud has evolved into a sophisticated, automated, global threat that costs businesses billions annually. For high-risk merchants, fraud attacks are more frequent, more aggressive, and more damaging than for traditional businesses. Fraudsters specifically target high-risk industries because they know these merchants have limited processing options, making them reluctant to be aggressive with fraud prevention for fear of losing legitimate sales. This creates a vicious cycle—fraud losses lead to chargebacks, chargebacks lead to account termination, and merchants feel trapped between accepting fraud or losing their ability to process payments entirely.

Modern payment fraud takes many forms, each requiring different detection and prevention strategies. Card testing involves fraudsters making small purchases with stolen card numbers to verify the cards work before making larger fraudulent purchases elsewhere. Account takeover fraud occurs when criminals gain access to legitimate customer accounts and use saved payment methods or customer data for unauthorized purchases. Friendly fraud happens when legitimate customers make purchases then dispute them with their banks, claiming they never received goods or didn't authorize transactions. Synthetic identity fraud uses fabricated identities combining real and fake information to open accounts and make fraudulent purchases. Each fraud type exhibits distinct patterns that intelligent systems can detect.

The challenge for high-risk businesses is balancing fraud prevention with customer experience and sales velocity. Overly aggressive fraud prevention creates false positives that decline legitimate transactions, frustrating customers, losing sales, and damaging brand reputation. Insufficient fraud prevention results in chargebacks, financial losses, and merchant account problems. Traditional rule-based fraud prevention requires constant manual tuning, creates rigid barriers that fraudsters learn to circumvent, and can't adapt to evolving fraud tactics. Modern AI-powered fraud prevention provides the intelligence and adaptability necessary to stop fraud while approving legitimate transactions, but most payment processors offer only basic fraud tools inadequate for high-risk merchants.

Paysuki's fraud prevention system leverages advanced artificial intelligence and machine learning to provide protection that adapts in real-time to emerging threats. Our system analyzes over 200 variables for every transaction in milliseconds, generating a real-time risk score that determines whether to approve, flag, or decline. Unlike rule-based systems that evaluate static criteria, our AI models understand complex patterns and relationships between variables, detecting subtle fraud indicators that traditional systems miss. The machine learning algorithms continuously train on new transaction data, automatically adapting to evolving fraud tactics without requiring manual rule updates.

Device intelligence forms a critical layer of our fraud prevention approach. When customers initiate transactions, our system captures device fingerprints—unique identifiers based on browser configurations, screen resolutions, installed fonts, IP addresses, connection types, and dozens of other parameters. This fingerprinting allows us to recognize devices associated with previous fraud attempts, detect suspicious patterns like many transactions from different accounts using the same device, and identify anomalies like legitimate customers suddenly using unfamiliar devices. Geolocation analysis adds another dimension, flagging transactions where device location doesn't match billing address, shipping address, or historical purchase locations for that customer.

Behavioral analysis examines customer actions throughout the purchase journey. Our system monitors how customers navigate your site, how quickly they complete forms, how they interact with pages, and whether their behavior matches typical legitimate customer patterns. Fraudsters exhibit distinct behavioral signatures—they move through checkout very quickly, rarely view multiple product pages, seldom return to sites, and often attempt multiple transactions in rapid succession. Our AI models detect these patterns and factor them into risk scoring. For returning customers, the system establishes behavioral baselines and flags transactions that deviate significantly from normal patterns.

You maintain complete control over fraud prevention settings through an intuitive dashboard. Set risk thresholds that determine when transactions are auto-approved, held for manual review, or automatically declined. Create custom rules for specific scenarios—requiring additional verification for high-dollar purchases, blocking transactions from certain countries, automatically declining orders shipping to freight forwarders, or adding verification steps for first-time customers. The system provides real-time fraud analytics showing attack patterns, blocked fraud attempts, false positive rates, and recommendations for optimizing your settings. Our fraud specialists provide ongoing support, conducting regular reviews and helping you tune the system for optimal protection with minimal customer friction.