Enhancing Payment Authorisation Efficiency

In the realm of e-commerce, a merchant’s ultimate aim is to ensure a seamless and satisfying customer journey, beginning with the first visit to their website and culminating in the delivery of goods or services. From the merchant's perspective, this journey is only complete once the payment has been received in full, free from any fraudulent activity or chargebacks.

Given the critical nature of the payment process in the customer journey, merchants often deploy specialised teams in payments product development, engineering, and data science. These teams work diligently to ensure that the final step of the customer experience is as smooth and rewarding as possible. Their efforts focus on monitoring and enhancing various key payment metrics, such as authentication rates, authorisation rates, chargeback rates, and fraud rates. These metrics are interconnected, balancing fraud prevention with an optimal customer experience to achieve the highest possible number of approved legitimate transactions.

 The Importance of Authorisation Rates

The user interface (UI) and user experience (UX) are crucial for both customer satisfaction and business growth. However, ensuring a seamless payment process is indispensable. Failure to authorise payments promptly can hinder customers from completing transactions, potentially leading to revenue loss, reputational damage, and a decline in customer retention for the merchant.

Payment authorisations may fail because the issuer flags the transaction as fraudulent. Sometimes, this occurs due to the issuer's machine learning (ML) models incorrectly identifying a legitimate transaction as fraudulent. Therefore, merchants must refine their internal ML systems to accurately detect and block fraudulent transactions before they reach the issuer's authorisation stage. Additionally, ensuring that the transaction payload contains accurate information during authorisation is crucial to avoid incorrect data influencing the issuer’s decision.

Strategies for Optimising Authorisation Rates

 Several technical solutions can be re-engineered to enhance payment authorisation rates, creating beneficial outcomes for both merchants and their customers.

Account Updater: Merchants who store customer credit or debit card information for future purchases or recurring transactions can face issues when these cards expire or are replaced. Many payment card issuers, such as Visa, MasterCard, and American Express, offer account updater services to keep merchants' records up to date. This service ensures seamless transactions, maintains high authorisation rates, and reduces unnecessary transaction processing fees.

Merchant Internal Risk-Based AI/ML Models: Merchants, being the first point of contact in the payment process, can develop AI/ML models based on customer purchasing behaviour. These models assess various factors like geolocation, transaction size, and merchant type to identify and block fraudulent transactions early. This approach lowers transaction processing fees, reduces chargebacks, and increases authorisation rates by filtering out risky transactions before they reach the issuer.

3-D Secure (3-DS): This protocol adds an extra security layer by allowing merchants to send authentication requests to the card network’s directory server and the issuer’s access control server. It provides issuers with additional data such as IP addresses and device information, enhancing fraud detection and increasing authorisation rates. Using 3-DS can also shift the fraud liability to the issuer if the transaction is authenticated.

Tokenisation: Tokenisation replaces sensitive card information with random numbers (tokens) that can be used throughout the payment process. This enhances security by preventing fraudsters from accessing card details and ensures continuous usage even when cards expire. Tokenisation not only leads to higher approval rates but also improves issuer decision-making by providing additional security features.

ML/AI Authorisation Retry Models: Advanced AI/ML models can predict the optimal times to retry declined transactions, especially for subscription-based services. These models, trained on vast historical data, help avoid payment failures and reduce passive churn by timing retries effectively, thereby minimising margin loss.

MID and MCC Optimisation: Merchants can improve authorisation rates by using multiple merchant identification numbers (MIDs) and merchant category codes (MCCs) to categorise transactions by risk level. Less risky transactions processed on a specific MID can build a positive transaction history, leading issuers to view these MIDs as lower risk and thereby increasing authorisation rates.

 Investing in Customer Experiences

 Investing in these solutions is essential for enhancing customer experience, reducing fraud, and achieving financial objectives. Product, engineering, and data science teams must collaborate to develop a comprehensive payment authorisation strategy. This process begins with assessing the current authorisation rate against regional benchmarks, identifying areas for improvement, and deploying relevant solutions.

 MIDs and MCCs provide a straightforward method for categorising transactions and enhancing processing efficiency. Building an internal risk AI/ML engine is also crucial for mitigating upfront transaction risks. Additional strategies, such as A/B testing, tokenisation, and 3-DS deployment, can be implemented based on the risk assessment of each transaction.

By adopting these strategies, merchants can significantly improve their payment authorisation rates, ensuring a secure and seamless payment process that benefits both their business and their customers.