Read “The Global State of Fraud and Returns” report
Every merchant knows the pain of seeing a good customer’s order get turned down by their bank. The shopper gets frustrated. You lose the sale. And often, the reason behind the decline isn’t fraud. It’s missing data.
The merchant-issuer data model was designed to help solve that problem. But before we look at what it is or how it works, it’s important to understand what’s missing from the traditional bank authorization process and why it so often fails online businesses.
TL;DR
|
Why the traditional bank authorization falls short in ecommerce
The authorization process was first designed for in-person payments. Physical cards and terminals give real-time signals. These signals include card swipes, taps, chip inserts, or PIN entries. They help confirm the customer’s presence and verify the transaction immediately. In ecommerce, those signals don’t exist since the card isn’t physically present. Because of this, issuing banks have to make authorization decisions using limited digital signals instead of in-person cues that confirm the cardholder’s presence and help distinguish a legitimate purchase from a fraudulent one.
While ecommerce businesses like yours have access to stronger insights like behavioral patterns, device fingerprints and customer order history that can verify trust, that data isn’t shared with issuers during authorization. And because they lack that deeper visibility into the customer’s identity and behavior during online purchases, issuers often err on the side of caution, causing genuine shoppers to get stuck in the fraud net. In fact, according to Signifyd data, banks falsely decline about 15% of good ecommerce orders, causing:
- Lower bank authorization rates, as cautious issuers accidentally reject legitimate shoppers
- Merchants to lose billions in potential revenue globally each year
How many billions? Consider the damage false declines at the authorization stage can do to just one merchant’s top line. For a merchant processing 200,000 ecommerce orders a month with an average order value of $50, that 15% hit works out to:
- 30,000 good orders wrongly declined each month.
- More than $18 million in lost sales over 12 months.
To make matters worse, when these purchases are declined, the issuers typically return generic response codes that offer little insight into why the customer’s order was stopped before reaching you for approval.
All of these blind spots have created a disconnect between merchants and banks. Fortunately, the merchant-issuer data model was built to close that divide.
What is the merchant-issuer data model?
The merchant-issuer data model is a data-sharing approach that improves visibility and alignment between merchants and issuing banks. It lets you send more detailed information alongside the authorization request to give issuers better context as they make authorization decisions. Some of these signals include:
- Customer order history: Past purchases that demonstrate a pattern of trusted behavior
- Secure logins: Confirmation that the shopper verified their identity when they logged into their account
- Order score: A real-time indicator of transaction trustworthiness based on the merchant’s data, including device information and behavior patterns
By enriching each authorization request with verified data, the merchant-issuer data model helps banks approve more good orders and deliver smoother checkout experiences.
How the merchant-issuer data model works
The merchant-issuer data model works by enhancing the authorization process with richer, verified transaction data from the merchant to the issuing bank. Here’s what it looks like after a customer completes their order:
| Step 1. A customer places an order | You collect the customer’s payment information at checkout. Your payment processor or gateway then builds the authorization request using the card details, billing address, purchase amount and your merchant ID. |
| Step 2. You gather trust signals | Your fraud review system analyzes trust signals — i.e. behavioral data, device fingerprints, login method, address consistency and order history — from the customer’s buying journey and transaction. |
| Step 3. You share additional data with the issuer | The positive decision and accompanying score along with additional trust signals travel securely alongside the authorization request through the appropriate card network to the issuing bank. |
| Step 4. The issuer reviews the transaction | The issuing bank evaluates the standard payment data and the additional trust signals you provided. |
| Step 5. The issuer responds with an authorization decision | The issuer responds with an authorization code if the order is approved or a decline code if it’s blocked. |
3 benefits of the merchant-issuer data model
Giving banks access to the deeper information you already have changes everything — from how they make decisions to how many of your good orders get through. And this benefits you in a few key ways:
Fewer false declines
When banks can see the same customer context you do, they stop mistaking trusted shoppers for fraudsters. That shared visibility means fewer orders get caught in the fraud net and more good sales make it through to you for approval.
Higher authorization rates
When issuers consistently receive detailed and reliable transaction data from you, they gain the confidence to loosen their overly cautious risk filters that often block good shoppers. This increased trust means more legitimate purchases get authorized and sent to you for approval the first time, lifting your ecommerce authorization rates.
Stronger shopper loyalty and lifetime value
Every smooth checkout builds trust. When shoppers know their payments will go through without friction, they’re far less likely to abandon their carts or switch to competitors. That confidence in your brand encourages repeat purchases and loyalty, increasing your average customer lifetime value (CLTV).
All of these benefits stem from one thing: better context. And when it’s missing, even loyal shoppers can end up walking away. Let’s look at how that plays out in a real-world example.
The merchant-issuer collaboration is important
Picture this: A loyal customer named Alex just moved into a new apartment and hasn’t updated their billing address yet. While unpacking, Alex realizes their TV screen cracked during the move — and their Wi-Fi router is missing. Alex had been looking forward to relaxing that night and streaming a new movie, so they hop online to replace both.
Alex finds the same TV on sale and a new router from a trusted retailer offering same-day delivery. They log into their account using fingerprint authentication, add both items to their cart, confirm their saved payment method and check out using their new shipping address. From your side, everything looks fine.
But when the authorization request reaches the issuing bank, the billing and shipping addresses don’t match what’s on file. Without access to your behavioral or identity signals, the bank flags the order as risky and declines it. Alex tries again, gets declined a second time and finally gives up — abandoning the cart and driving to a nearby store instead. The merchant loses the sale and Alex’s trust.
Now imagine the same order, but with the merchant-issuer data model in place.
This time, the bank receives not just the standard payment information, but also verified trust signals confirming that Alex logged in with biometrics, is using a recognized device and has a consistent purchase history with the merchant.
That added context shows the order is genuine. The issuer authorizes the purchase instantly, Alex gets their TV and router delivered the same day and the retailer keeps the sale. The checkout feels seamless, trust is reinforced and the next time that Alex needs something fast, they’ll buy from the same merchant again.
How Signifyd helps you put the merchant-issuer data model into action
While the merchant-issuer data model powers smarter authorization decisions, implementing it effectively requires deep connectivity and collaboration between merchants and issuers. Specialized solutions like Signifyd’s Authorization Rate Optimization (ARO) facilitate that connection, helping both sides align.
When using ARO, Signifyd acts as the intelligence layer between your checkout and the issuing bank. The platform analyzes every order in real time, applying advanced machine learning (trained on billions of transactions from the Commerce Network) to determine which are safe and which are fraudulent.
Sending only trusted transactions to the issuing bank
Before your authorization request is sent, the solution filters out risky or fraudulent orders using pre-authorization. That means only clean, trusted orders reach the bank for review — lifting authorization rates by up to 3%.
Enriching authorizations with better context
Signifyd packages each trusted transaction with additional data points — including order score, order decision, IP address, biometrics, geolocation, device ID and more — so issuers can make more accurate decisions.
Strengthening merchant-issuer relationships with reliable signal quality
As banks consistently receive cleaner, high-quality data from your brand, their trust in your signals grows. Over time, that confidence improves how your online orders are treated, driving stronger approval performance across your business.
“I’ve seen a bunch of orders go through that previously we would have been falsely declined. They were approved through Signifyd — 100% legitimate business that we would have walked away from.”
Scott Perry, senior vice president of Digital IT and omnichannel at Jerome’s Furniture
By bridging the gaps between merchants and issuers, ARO empowers you to recover lost revenue, reduce friction for genuine customers and build long-term trust — all while keeping fraud under control.
Ready to help influence smarter issuer authorization decisions with Signifyd? Let’s talk.
FAQs
What is a merchant-issuer?
The term “merchant-issuer” refers to the relationship between an ecommerce business (the merchant) and the issuing bank that authorizes or declines customer transaction attempts. It’s the foundation of the merchant-issuer data model, which helps both sides share data to reduce false declines and increase authorization rates.
How does the merchant-issuer data model benefit both merchants and issuers?
The merchant-issuer data model improves how both sides handle online orders. Banks get the context they need to approve more legitimate orders, while it gives merchants visibility into why transactions are declined. It also helps merchants recover revenue that would have been lost from false declines.
What are the key components of the merchant-issuer data model?
The model works by connecting merchants and issuing banks through three main steps:
- Data enrichment: Merchants send verified trust signals along with the standard payment details.
- Real-time sharing: That data travels securely through the appropriate card network to the issuing bank.
- Smarter authorization decisions: With a fuller picture behind who the customer is and what their intent is, banks can confidently authorize more good transactions and send them to the merchant for approval.
