Frequently Asked Questions

Standing Orders and Direct Debits are traditionally rigid, fixed-amount payments, whereas VRPs are dynamic and can adjust to a customer's fluctuating income and expenses. This intelligence ensures that deposits are only initiated when the data identifies a genuine capacity to save.

Yes, transaction data allows banks to identify capacity for savings across internal and external accounts and move it into their own high-value products. By understanding a customer’s spending habits and life events through data, institutions can proactively offer relevant services like mortgages or investments at the exact moment of customer need.

Banks can use enriched transaction data to identify a customer's real-time discretionary surplus and trigger personalised nudges. This ensures every suggestion to save is based on the customer’s actual financial capacity rather than generic payday guesswork.

Essential features blend seamless core functionality, such as instant balance checking and quick transfers, with robust, visible security measures like biometric login. Users consistently want clear transaction history, real-time transaction notifications and automated spending categorisation.

Consumers struggle because banking apps display complex, raw Merchant IDs instead of recognisable business names. This high cognitive load creates anxiety, often leading the customer to fear fraud and call customer support.

The issue stems from the gap between the bank's raw data and the customer's mental model. Raw data contains strings of digits and abbreviations that hold no meaning for the user, resulting in a perceived risk. By failing to enrich this data with clear merchant names, logos, and locations, the bank forces the customer to do the verification work, creating a frustrating, high-friction experience.

Customer loyalty is built upon Communication, Choice, Control, and Connection. Focusing on proactive updates and transparent data usage will empower customers and foster an emotional link to your brand, and help you influence their money management.

Yes, different branches of the same business will usually have their own merchant account ID, as it helps the business with tracking and analytics. This is, however, typically more complicated for banks and their customers, as it can be confusing when more than one store exists in the same area. 

Banks need to identify the exact store within their merchant ID process. Moneyhub is one of only a few providers to do this as standard, using address data and map visualisations.  

No, there’s no standardised merchant account number list. It’s up to the banks to create their own system with the data, which can be confusing when you have multiple merchant accounts with similar names. It’s why building in-house for this feature is heavily resource-intensive and expensive (in the millions), and most financial institutions prefer to outsource it to a dedicated merchant account provider. 

Merchant Category Codes (MCCs) were introduced by the International Organization for Standardization (ISO), and are now maintained by card payments networks like Visa and Mastercard. They’re a global categorisation system for merchants, using 4-digit codes to signal the type of merchant. 

However, relying solely on merchant category codes in payment processing continues to limit the ability to recognise and analyse transactions, because the merchant name isn’t necessarily provided. This is especially the case for business bank account transfers, where the name of a sole trader might not obviously indicate the merchant services because it doesn't necessarily match the trading name or merchant statement documents. 

Historically, these codes were introduced to help with expense claims, so in categories like airlines or hotels, each provider has their own code. In categories that are less common for expenses, though, such as groceries, every supermarket shares the same code. This limits usefulness and transparency. 

Leading banks and lenders overcome the challenges of silo'd merchant ID number or MCC data by performing the merchant identification process as part of their categorisation and enrichment.

Merchant identification is the process of finding out the name of the business where a transaction took place. Every business has a unique merchant identification number, from ‘Sainsburys’ to ‘Primark’ or even ‘Jennings Bet’. 

At Moneyhub, we convert the merchant identification number to the merchant name, known as merchant detection. This information is then attached to other transaction data like payment amount and time of purchase in the banking apps that we service for ease of recognition and further analysis.  

Not all categorisation and enrichment providers arrive at their merchant identifier in the same way, which means that the outcome of a brand name isn’t guaranteed. When providers can’t identify exact merchant names, it’s more difficult for customers to recognise transactions, and for financial institutions to perform further analysis.

Wait… so a merchant identification number isn’t the same as merchant identification?

The short answer: no, they’re different. 

The longer explanation: a raw 15-digit string of numbers is the merchant ID number. Some banks and lenders will find this data sufficient and include it in its raw form in transaction descriptions, confusing end users and making it harder for employees to analyse. Unfortunately there’s no universal merchant identification number lookup service to bypass this obscure output, either.

However, most categorisation and enrichment systems transform this merchant number into the merchant’s trading name after a customer payment. This process of determining the merchant name from the numbers is the merchant identification (or detection) process.

Financial institutions use agentic AI to automate complex workflows like autonomous fraud response, where agents don't just flag a transaction but independently freeze the account, notify the customer, and initiate a recovery ticket.

ChatGPT is primarily a generative AI, but it exhibits agentic properties when it uses features like Advanced Data Analysis or specialised GPTs to browse the web and run code. As the platform evolves with reasoning models and tool-use capabilities, it is moving further away from simple chat and closer to a fully agentic system.