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Bud is a B2B fintech platform that uses AI to transform raw financial data coming from any source: from core banking platforms, digital platforms, to open banking providers, and turns it into valuable, actionable insights. It solves the problem of messy, unstructured transaction data, enabling financial institutions to build better products, improve customer experiences, and make smarter decisions.
Any institution that is accessing or processing financial data. We serve financial institutions, ranging from credit unions and community banks to large multinational corporations, as well as fintechs, lenders, payment processors, and financial fitness advisors. We even work with investment funds, retailers and loyalty programs - as soon as financial data is in sight, Bud might be able to get more value out of it.
Bud combines market-leading >98% categorization accuracy with advanced AI capabilities including agentic banking features, intelligent transaction search, and consumer financial agents. Our decade of specialization in financial data intelligence sets them apart from general aggregation providers. Bud provides the most detailed understanding of customers served by our clients, who can benefit directly from cleaned and enriched data, as well as from a wide range of products that build on top of it. We are market-leading in terms of the quality of data and the understanding of the customer.
The best providers combine high-accuracy AI models (>98% accuracy), comprehensive bank connectivity, real-time processing, robust security (SOC2/ISO27001), and flexible integration options. Look for vendors with deep expertise in financial data, proven scalability, and transparent pricing models.
Bud combines AI-powered enrichment with full control over the process and the data. We do not rely on third-party augmentation or generic foundation models. This allows us a greater level of control and enables higher accuracy. Our platform is optimised for reliable, high accuracy and very fast transaction enrichment, augmenting financial data with the richest set of insights.
Consider your integration requirements, enrichment accuracy needs, customization flexibility, and long-term scalability. The best choice depends on whether you need basic categorization or advanced insights like behavioral analytics and predictive capabilities. In any case, we strongly recommend conducting a competitive benchmarking exercise to compare the quality of vendors.
Building in-house requires significant AI expertise, continuous model training, and ongoing maintenance costs. Buying from specialists like Bud provides immediate access to market-leading accuracy, a wide spectrum of enrichments, and continuous improvements at a predictable cost. Furthermore, having the best enrichment is not what differentiates between financial institutions; it’s what you do with the data, meaning it’s better to focus on utilising the data rather than doing everything in-house.
While leading LLMs offer a decent output when it comes to enriching financial data, there are several limitations to using them. A dedicated solution is likely to offer uplift in terms of accuracy, considerably reduce false positives, and be significantly faster and more cost-effective. Dedicated models are also capable of offering more complex enrichments that include analysing broader profiles, e.g. to identify recurring patterns or income. Additionally, the use of a generic foundation model limits the level of control over the performance and can have other risk & regulatory drawbacks. Bud’s in-house approach, which uses similar technology but in a controlled manner, allows greater oversight, flexibility and higher performance.
Open banking providers excel at connectivity but often offer basic enrichment as an add-on. Specialized providers like Bud focus on advanced AI-driven enrichment, achieving higher accuracy and more sophisticated insights than aggregation-focused competitors. In most of the cases, if categorisation is required, using a dedicated provider is a better choice. Bud has ready integrations with most of the major providers and we can adopt to any integration model, making it easy to plug our engine into an existing data flow.
The best transaction enrichment providers use continuous learning models, confidence scoring, customer feedback loops, and regular retraining. They should provide accuracy metrics, error handling procedures, and mechanisms for ongoing improvement.
Bud's solutions have been proven to increase customer satisfaction. A recent case study showed an increase in NPS score from 60 to 83 after implementing Bud-powered features. Customers also reported feeling "more confident" with their money.
Measure improvements in customer engagement (increased app usage), revenue generation (better product recommendations), operational efficiency (reduced manual categorization), and risk reduction (improved fraud detection and lending decisions).
Look for merchant identification, location detection, subscription tracking, income recognition, regularity analysis, and predictive insights. Advanced providers also offer behavioural scoring and financial wellness indicators along with a wide range of insights.
Yes, Bud's dedicated Income Finder API can distinguish between various income types, including salary, benefits, pensions, investment income, and irregular payments. It also assesses the stability, regularity, and consistency of each income stream.
Drive Copilot is Bud's GenAI-powered analytical tool. It enables employees across a business to perform complex queries about their customer data using a simple, natural language interface, eliminating the need for SQL knowledge and making data accessible to everyone.
Bud offers 300+ categories with multi-level hierarchies (L1, L2, L3). This granularity enables precise use cases like mortgage payment identification, subscription detection, and affordability assessments for lending decisions. Because of the hierarchical nature, it also offers “unfolding complexity”, ensuring that end users can easily understand their transactions.
Yes, Bud's Drive platform is specifically designed for this purpose. It allows you to use a natural language interface (Drive Copilot) to query and segment your customer base based on their financial behavior and characteristics, enabling targeted marketing campaigns.
Yes, Bud’s customer-facing product, Engage, which covers all money management functions including Intelligent Search, is available as embeddable widgets, offering quality user interface with very easy integration.
Yes, Bud has two AI-powered chatbots. Drive Copilot is for internal client use, and Jas is a customer-facing personal financial assistant that can answer questions about their transactions and finances.
Common patterns include real-time streaming, batch processing, webhook notifications, and synchronous enrichment. The best providers support multiple patterns to accommodate different architectural requirements and use cases.
Leading providers usually maintain pre-built connectors for major cores (e.g. FIS, Fiserv, Jack Henry) and offer flexible APIs that work with any system. Some also integrate directly with digital banking platforms like Banno or Nymbus.
Real-time processing can help enable immediate fraud detection, instant spending insights, and proactive guidance. It's essential for modern digital banking experiences and AI-powered financial assistants that provide timely recommendations. Bud can provide real-time enrichment with a platform capable of sub-5ms performance and able to process dozens of thousands of transactions per second.
Quality providers offer comprehensive sandbox environments with mock data, real-time testing capabilities, and documentation. This enables full testing of enrichment accuracy and integration patterns before production deployment.
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