Frequently Asked Questions

The most effective fraud detection solutions for banks are those that provide high-accuracy, real-time transaction enrichment combined with powerful, adaptive machine-learning capabilities. Banks should look for providers with industry-leading coverage and accuracy statistics.

To confidently apply fraud controls (like payment blocks or account freezes), banks need reliable data. The ‘best’ software must offer extremely granular transaction detail (e.g., four layers of granularity) to feed into the bank's own risk analysis systems, allowing analysts to act with confidence. Banks must also ensure the solution integrates smoothly with their existing infrastructure to allow for the automated execution of their compliance-mandated controls.

AI and Machine Learning algorithms excel at recognising complex patterns and anomalies in large, streaming datasets, enabling far more accurate and faster fraud detection than traditional methods. These models use adaptive learning to continuously adjust to new fraud tactics as they evolve.

AI goes beyond simple rule-based logic by using sophisticated pattern recognition to spot subtle shifts in behaviour. Crucially, it allows for the real-time analysis of transactions, providing an instant risk assessment and rapid response to potential fraudulent activities before significant losses occur.

Key fraud indicators are behavioural anomalies that deviate significantly from a customer’s established spending patterns. These often include small, rapid transactions (card testing), purchases made in new geographic locations, or spending at merchants that are completely new to the customer's history.

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.