When a customer makes a payment, the transaction is also checked for signs of risk. These checks are designed to block fraud and protect both merchants and customers. The challenge is finding the right level of control: too much and genuine transactions may be declined, too little and fraud slips through. Finding the right balance depends on understanding transaction risk.
Transaction risk is the possibility that a payment doesn’t go through as intended — whether that’s because it’s fraudulent, disputed later, or blocked by error. For merchants, this risk shows up in three main ways: fraud attempts like card testing or stolen identities, chargebacks that force refunds after disputes, and payment failures caused by technical issues or overly strict filters. Each carries a cost in lost revenue and customer trust.
To manage these risks, businesses use transaction risk analysis (TRA) to review signals in real time and decide whether to approve, challenge, or decline a transaction.
Transaction risk analysis (TRA) is the process of evaluating these signals to decide whether a payment should be approved, declined, or challenged for extra authentication. In Europe, TRA is a key exemption under PSD2/PSD3, allowing certain transactions to bypass strong customer authentication (SCA) if they meet low-risk thresholds.
TRA compares each transaction to historical data and known fraud behaviour. It assigns a risk score based on how closely a transaction matches characteristics of previously flagged transactions. This score determines whether a transaction is approved, declined, or sent for further review.
TRA works by evaluating a range of risk indicators:
A risk model processes these attributes and assigns weights based on their correlation with fraud outcomes. Higher scores indicate higher fraud probability. Businesses can define thresholds to trigger manual review, request additional verification, or auto-decline.
Automation ensures that transactions are evaluated efficiently and consistently. Here’s how to do it:
Automation doesn’t eliminate risk, but it reduces the chance of oversight and allows human teams to focus on edge cases where intervention adds value.
Transaction risk analysis only works if you have the right systems to apply it at scale. For most merchants, that means using a risk engine to score transactions and rule-based systems to enforce consistent checks. These tools take the theory of TRA and turn it into real-time decisions — approve, decline, or request more verification.
Risk engines assign scores to transactions using pre-set rules and predictive models. They:
Both approaches improve as they process more data — billing addresses, device fingerprints, phone numbers — and many payment systems now combine them to get the best of both worlds.
Some failures are due to system issues—invalid card data, expired tokens, or insufficient funds. Others are rejected by risk controls:
These fall under transaction failed due to risk rule violation.
Understanding the type of decline is key to recovery:
Distinguishing between soft and hard declines helps merchants decide when to retry, when to reroute, and when to ask the customer for something different. This helps reduce unnecessary lost sales while respecting issuer and network rules.
To keep fraud low and approvals high:
Frequent monitoring and iterative adjustments are key to maintaining optimal risk settings.
Antom brings together everything merchants need to balance fraud prevention with customer approvals. Its configurable risk engine scores each payment in real time, while machine learning models spot patterns that rules alone might miss. Merchants gain access to a central dashboard that supports account management, reporting, and monitoring of payment activity across channels.
Whether payments happen online, in-store, or through recurring billing, Antom helps businesses reduce false positives, cut fraud, and maintain strong approval rates in every market.