Klarna has reimagined the challenge of acceptance rate optimization by enhancing the consumer journey, strengthening the underwriting process, and refining the supporting infrastructure. The result is a highly intelligent, secure, and scalable solution designed to support merchants in maximizing conversion while minimizing risk.

At the core of Klarna’s acceptance optimization engine are four key pillars: Supplementary Purchase Data (SPD), advanced underwriting models and real-time risk management. Together, they empower Partners to turn missed opportunities into measurable business growth.

When you combine all of Klarna’s tools, SPD, behavioral scoring, partner-level monitoring, and dispute automation — you get a system that enables best in class customer experience, is decisive, scalable, and responsible while supporting the optimization of acceptance rates with:

• More approvals for trustworthy users
• Fewer false declines
• Strong fraud and dispute mitigation that protects margins

Supplementary Purchase Data refers to additional data points that provide additional context related to a transaction to improve outcomes and customer experience. While traditional payment acceptance decisions rely mostly on minimal inputs, usually a card number, amount, and shipping address, Klarna enables Partners to leverages rich transactional data to offer enhanced customer experiences and make smarter risk and credit decisions, this is what we call.

This data goes beyond the basic transaction information by including optional, but highly impactful data points that supports different use cases and may include data associated to specific industries or business models such as airline, marketplaces, as well as data related to the transaction (line items, subscriptions and on demand transactions) or even custom data; some examples are:

• Line items: Product names, prices, SKUs, categories, and images
• Shipping details: Methods, addresses, and delivery estimates
• Customer profile info: Contact details, account status, loyalty tiers
• Purchase context: Subscription cycles, segment specific data such as travel itineraries and booking metadata

These data points not only supports more accurate credit scoring but also feeds Klarna’s fraud detection models, allowing real-time triangulation checks, phishing detection, and compliance with AML/CTF regulations. With better data, Klarna can say “yes” more often and safely.

The supplementary data shared with Klarna will support multiple use cases and add value in different fronts such as:

• Enhance post purchase experience: Enhance customer’s post-purchase experience with detailed transaction breakdowns, streamlining disputes and returns and reducing support requests as well as improving the existent requests giving the additional info the partner shares.
• Acceptance Rate Improvement: Historical data informs underwriting processes, allowing for increased credit limits and improved acceptance rates.
• Enhancement of solution offering:  Based on historical behavior of the consumer with a brand, Klarna is able to develop enhanced solutions for incentives and to drive particular actions such as reactivate with promotions previous customers that haven't shop in you store for a while.
• Effective Fraud Case Investigation: Detailed transaction insights support thorough investigations, tailoring responses to unique characteristics in high-risk segments as well as for particular verticals such as airlines, lodging.
• Klarna’s Risk Exposure Monitoring: Data points facilitate ongoing monitoring of risk exposure in high-risk segments, ensuring timely identification and control of potential risks.
• Enhance underwriting and fraud assessment processes: enhance the underwriting and fraud assessment for Partners and Klarna can make better-informed decisions in assessing the risk of the transaction. Historical data informs underwriting processes, allowing for increased credit limits and improved acceptance rates.

Line items stand at the crossroads of Klarna's operations and are crucial in enhancing Klarna's fraud detection, underwriting capabilities, and customer experience. By breaking down transactions into individual units, they provide detailed insights that streamline dispute resolution and efficient interaction within the Klarna app, while reducing customer support errands.

The purchase_reference field is a required identifier used throughout the payment process to support various partner and customer-facing activities. This parameter should correspond to the Partners customer-facing order reference. Ensuring this parameter is both consistently applied and understandable to end consumers is helpful in minimizing errands and ensuring customer satisfaction. Below are some key use cases:

• Customer Service: It provides a clear, recognizable identifier that customers can use during disputes, inquiries, or interactions with customer support, improving the overall user experience.
  • In addition, Klarna leverages the purchase_reference in all touchpoints with the consumer, ensuring clear communication and understanding within the Klarna App, email communications, and push notifications regarding order status.
• Dispute Resolution: purchase_reference provides all involved parties with an aligned understanding of the order being discussed, reducing the chances for confusion or human error for both customers and customer service agents.
• Fallback Identifier: When other session identifiers are unavailable, the purchase_reference may act as a fallback to ensure continuity and traceability within the transaction.

Purchase reference plays a key role in supporting visibility and alignment between all parties, and drives increased customer satisfaction.

The following table list the supplementary_purchase_data shared with Klarna will support multiple use cases and add value in different fronts such as:

Klarna’s perspective of the consumer behaviour, including direct access to historical shopping patterns, payment history, and device data, allows for more personalized and adaptive underwriting than traditional banks or card issuers can provide. By analyzing SPD and behavioral data (e.g., loyalty, purchase history), Klarna is able to:

• Approve more legitimate transactions
• Lower the rejection of good customers due to limited information
• Make real-time lending decisions using AI-driven models

These models enable Klarna to extend intelligent credit to the right users at the right moment, whether it’s for a €20 fashion accessory or a $500 electronics purchase, maximizing approval while preserving repayment reliability.

Unlike traditional card networks that outsource risk decisions to issuing banks, Klarna is a licensed bank and full-stack network operator. This gives it direct control over fraud signals, underwriting models, transaction context, consumer behaviour and post-purchase data, all of which feed into its risk engine.

Klarna’s fraud engine operates on:

• Behavioral data: Shopping patterns, device fingerprinting, biometric cues
• Transaction data: MCCs, cart content, shipping preferences, frequency
• Supplementary Purchase Data (SPD): Product-level details, delivery methods, and customer metadata

This intelligence allows Klarna to flag suspicious behavior instantly, even before the transaction is completed, and to distinguish between genuine new users and fraudsters masquerading as first-time buyers.

Fraud isn't the only risk Klarna manages. Disputes, whether from shipping issues, product misrepresentation, or customer confusion, can damage your reputation and hurt approval rates.

To protect Partners:

• Klarna provides automated dispute resolution (93% of cases resolved before escalation)
• Offers real-time visibility into dispute metrics
• Monitors dispute rates under dedicated programs

By minimizing dispute risk and improving operational transparency, Klarna not only protect your business but also gives underwriting systems the confidence to approve more borderline transactions.