How Payment Providers Reduce Fraud Without Increasing Checkout Friction

Online merchants face a constant challenge. On one hand, they must ensure payment safety because it’s key to customer trust. On the other, they have to make checkout as smooth as possible to reduce cart abandonment and boost user satisfaction. The two goals often clash – adding security measures protects transactions, but can slow down payment processing or cause legitimate transactions to be declined as suspicious.

Now things are finally changing. Modern payment service providers show that there’s no need to sacrifice checkout speed to reduce fraud. 

Let’s break down how exactly they solve this problem.

Better Security Doesn’t Have to Mean More Friction

Previously, providers often achieved transaction security at the cost of checkout speed. Their anti-fraud mechanisms required customers to manually enter passwords or wait for SMS codes every time. This made checkout longer and customer satisfaction lower. 

Merchants lost significant revenue as a result. According to a study by the Baymard Institute, complicated checkout caused 22% of UK shoppers to abandon their carts before completing a purchase.

Today, payment providers have alternative solutions. The latest technologies let fraud detection run in the background, make payment data unreadable for attackers, or keep it entirely off the Internet. It makes checkout not only much safer, but also faster, allowing customers to pay in just a few clicks.

But what exactly are these technologies, and how do they work in practice?

Machine Learning in Fraud Prevention

Machine learning uses Artificial Intelligence (AI) to analyse data in real time. Unlike older systems that rely on strict rules, machine learning constantly learns from new data and adapts to new attack tactics.

Traditional security systems block transactions that trigger a predefined rule. For example, they might flag a sudden $2000 purchase on an account that usually spends $50, or decline a payment if the card country, IP address, and shipping destination don't match.

These are classic fraud signals, and traditional systems protect well against these basic attacks. But they also cause false positives by rejecting legitimate purchases. If a customer goes on vacation and buys an expensive laptop with delivery to their hotel, this payment may be flagged as suspicious and declined. On top of that, fraud never stands still. Generative and Agentic AI allow attackers to mimic real users and fool traditional mechanisms, so relying only on them isn’t wise. 

Modern machine learning tools are a completely new generation of defence. They are far better at distinguishing between a legitimate customer who behaves ‘unusually’ and a bot impersonating a human buyer.

This lets payment service providers reduce false positives and make checkout smoother for users. There’s no longer a need to add extra security checks for every purchase – AI analyses all the data in the background.

Machine Learning in Practice: Risk-Based Authentication

Risk-Based Authentication (RBA) evaluates transactions and decides whether to let them through or request additional verification.

The two key components of RBA are real-time data analysis and dynamic risk scoring.

Real-Time Data Analysis

To give transactions a risk level, RBA analyses such data in real time:

• Device fingerprinting. It’s a unique combination of settings, including screen resolution, operating system version, browser type, language, and installed plugins. A normal customer’s device fingerprint matches the account's historical behaviour. If the system finds something suspicious, it can block the payment from this device or send it for additional verification.

• Geolocation. The security mechanism cross-checks the user’s IP against the billing address, shipping destination, and card-issuing country. Mismatches here increase the risk score.

• Behavioural patterns. RBA monitors how the user interacts with the checkout, including how fast they type, move the cursor, or touch the screen. Bots or fraudsters using stolen data complete checkout not like regular payers – for example, they fill out all fields too quickly or simultaneously.

Dynamic Risk Scoring

Based on real-time data analysis, the machine learning model routes the payment through one of two key scenarios:

• Frictionless flow. Low-risk transactions go through immediately without extra verification steps.

• Challenge flow. If the system detects suspicious or high-risk signals, such a payment goes through a challenge flow. A user then needs to do some additional security checks – like biometric authentication, a push notification through a banking app, or a one-time SMS passcode.

It lets most customers complete checkout faster, improving their overall satisfaction with the payment process. Only in rare cases, the system asks for a quick check to ensure the transaction is secure.

Payment Solutions that Reduce Fraud and Streamline Checkout 

Beyond machine learning, the payment technology itself can help balance security and convenience. Some payment solutions protect sensitive data while requiring less effort from the customer.

Tokenisation: Digital Wallets and One-Click Checkout

Tokenisation replaces payers’ card details with unique, randomised codes called tokens. Even if a data breach happens, tokens remain unreadable for fraudsters. 

Digital wallets like Apple Pay and Google Pay use payment tokenisation to protect card information. They also speed up checkout, letting customers confirm payments with biometrics or their device password. 

Tokenisation can also support one-click payments. Instead of storing the customer's actual card details, the payment provider uses tokens for future purchases. This lets returning customers pay without re-entering their payment information.

Account-to-Account Transfers

Account-to-Account (A2A) payments offer another way to make checkout both secure and convenient. Customers pay directly from their bank accounts, without entering card details or sharing them with the merchant. As a result, their sensitive information stays off the website, reducing the risk of data theft. 

Beyond enhanced security, the benefits of A2A transfers also include:

• Faster checkout. Users don’t have to manually enter anything – they only need to quickly approve the payment. This makes the checkout faster and more effortless.

• Lower chargeback risks. Thanks to bank-level authentication, the chances of an unauthorised transaction are very low. For merchants, this means that the chargeback risk is virtually eliminated.

• Real-time settlement. A2A transfers move money between accounts, bypassing card networks entirely. As a result, funds settle in real time, giving merchants faster access to their revenue and better control over cash flow.

How to Choose a PSP that Balances Security and Convenience

Balancing secure payments with a frictionless checkout flow can be a real challenge, but the right payment service provider (PSP) makes it possible.

When selecting one, merchants should consider several key factors. 

First, system updates. Such a provider must constantly study evolving fraud trends and update its security systems to ensure maximum protection.

Second, payment methods available. Are global and local tokenised digital wallets available? What about A2A transfers, such as Pay by Bank? This matters because methods directly affect customer trust and satisfaction – and consequently, merchants’ revenue.

Third, customer support. Although guides and chatbots cover basic inquiries, they can’t always answer non-standard questions. That’s why access to a human account manager is key. A specialist can address issues in real time, resolve them faster, and handle complex cases that guides don’t cover. 

Payop is one example of such a PSP. This provider lets online businesses accept payments globally and securely, without forcing merchants to choose between strong fraud prevention and a fast checkout.

That’s ultimately what a good payment setup should achieve: strong protection in the background and as little friction as possible for the person actually making the payment.