Articles
Industry news, trends and the topics we’re all talking about.
Explore the differences, use cases and full feature list to compare transaction categorisation and enrichment providers: Moneyhub vs Bud.
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9 reasons customers don't recognise transactions in their banking app. Learn how vague merchant data, missing context, and timing gaps drive avoidable disputes and bank opex costs.
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The need-to-know cost drivers behind false chargeback fraud and the practical enrichment tools that provide the insights you need to build a robust dispute defence for your bank.
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Open banking has been talked about in lending circles for years, but it's rare to hear a lender walk through exactly what changed when they used it. Here's what stood out from our conversation with Admiral Money.
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Cut support costs with 3 banking app features: transaction enrichment, smarter fraud detection, and AI chatbots that know when to escalate.
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Spot card cracking by enriching transaction data, scrutinising merchants, paying attention to tiny transactions and using risk scoring. Feed confirmed attacks back into your model to improve card cracking detection over time.
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Advanced fraud detection in real time is the gold standard that most banks are trying to reach. But current banking systems need real-time transaction insights, better accuracy and deeper segmentation.
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Prevent the operational costs of customer transaction confusion by upgrading your categorisation and enrichment.
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Modern fraud moves faster than batch process systems were designed to handle. Compare batch processing transaction enrichment versus real-time categorisation and enrichment to see where the opportunity lies for banks to reduce fraud.
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Are false positives a necessary inconvenience or a costly, brand-damaging feature of poor categorisation and enrichment? We explore how devastating false positives can be on operational efficiency, budgets and customer sentiment.
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High false positive rates are not inevitable. With proper structure in place, they can help reduce alert fatigue for your fraud team. But three factors usually drive them: static rules applied to dynamic behaviour, overly sensitive tuning, and incomplete or siloed data.
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With remortgage applications surging by 46% in early 2026, lenders must shift their focus from interest rate competition to providing frictionless, data-led customer journeys. By leveraging Moneyhub’s AI-powered categorisation engine and Open Banking data, firms can automate income verification, slash underwriting times, and proactively support vulnerable borrowers.
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