What is transaction risk and how to reduce declines

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. 

What is 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)

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.

How TRA works

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:

• Location: A sudden change in country or IP address can suggest fraud.
  
  
• Device: New or unusual devices may need extra checks.
  
  
• Transaction amount: Very large or unusual purchases can be flagged.
  
  
• Behaviour patterns: Repeated failed attempts or unusual order timing.
  
  
• Account history: Past disputes, chargebacks, or spending behaviour.

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.

How to automate TRA

Automation ensures that transactions are evaluated efficiently and consistently. Here’s how to do it:

1. Use a dedicated risk engine: Connect your checkout flow to a real-time scoring system that supports rule-based and machine-learning assessments.
   
   
2. Feed in live data: Provide enriched metadata like device signals, location, and account behaviour in real time.
   
   
3. Set scoring thresholds: Define numerical values that determine action paths—e.g., approve if under 50, challenge if 50–80, decline if above 80.
   
   
4. Enable dynamic rule updates: Allow the system to adapt rules as new fraud patterns emerge.
   
   
5. Incorporate feedback loops: Use outcomes of past approvals and declines to retrain your model and refine thresholds.

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.

Risk engines and rule-based systems

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.

How risk engines work

Risk engines assign scores to transactions using pre-set rules and predictive models. They:

• Cross-check against allow/block lists
  
  
• Apply velocity and behaviour checks
  
  
• Use real-time signals to assess fraud risk
  
  
• Trigger reviews for transactions that exceed thresholds

Rule-based vs. machine learning approaches

• Rules are explicit instructions: block transactions from high-risk countries or require CVV for every card payment.
  
  
• Machine learning finds patterns in data without pre-set rules. It might notice, for example, that a specific device/browser combo often signals fraud.

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.

Why transactions fail: beyond the basics

Technical and risk-based declines

Some failures are due to system issues—invalid card data, expired tokens, or insufficient funds. Others are rejected by risk controls:

• Mismatched CVV
  
  
• Suspicious IP
  
  
• Exceeded velocity rules

These fall under transaction failed due to risk rule violation.

Soft vs. hard declines

Understanding the type of decline is key to recovery:

• Soft declines are temporary and often recoverable. Examples: failed authentication or issuer systems being unavailable. Merchants can retry these transactions or prompt customers to verify through another method.
  
  
• Hard declines are permanent. Examples: account closed, card blocked, or reported stolen. Retrying won’t help — the customer must use another payment method.

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.

How to reduce transaction risk

To keep fraud low and approvals high:

• Collect rich metadata: Device, billing, and contact info improve analysis accuracy.
  
  
• Use SCA tools: 3DS, biometrics, and CVV help authenticate users.
  
  
• Set thresholds carefully: Balance between catching fraud and allowing valid transactions.
  
  
• Review issuer insights: Use acquirer and issuer feedback to adjust rules.
  
  
• Track and adjust: Dashboards and logs support ongoing rule refinement.

Frequent monitoring and iterative adjustments are key to maintaining optimal risk settings.

How Antom helps you manage transaction risk

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.