Can generalist AI models compete with banking categorization solutions?

In this article, I explore whether large language models (LLMs) like GPT-4, Gemini, or DeepSeek can compete with specialized solutions such as COCO {Categorization} —developed by Coinscrap Finance— for critical tasks like analyzing financial transactions.

Here are just a few of the advantages offered by banking categorization:

• Enhances the user experience with clear, organized information.
• Facilitates financial planning by identifying spending and saving habits.
• Fuels personalized marketing through deeper customer insight.
• Supports better decision-making for institutions based on segmented data

With the rapid emergence of AI and the wealth of tools available on the market, the question we’re asking is: Can generalist AI models match the capabilities of specialized fintech solutions?

Methodology: Comparing generalist AI vs. COCO {Categorization}

To answer this question, Coinscrap Finance conducted a thorough study comparing our specialized engine, COCO {Categorization}, with a selection of 20 leading LLMs. These included:

• OpenAI: GPT-4.5 Preview, GPT-4o Mini, GPT-4o.
• Anthropic: Claude Sonnet 3.7, Claude Sonnet 3.5.
• DeepSeek: DeepSeek R1 Fast, DeepSeek R1, DeepSeek V3.1.
• Google: Gemini 2.0 Pro, Gemini 2.0 Flash, Gemini 2.0 Flash (Image Gen), Gemini 2.5 Pro.
• xAI: Grok.
• Meta: Llama-3.1 405B.
• Mistral/Qwen: QwQ 32B.
• Abacus.AI: Smaug.

We evaluated each model using a representative set of 50,000 real bank transactions, grouped into 30 financial sector-specific categories. We measured accuracy, processing speed (transactions per second), and analyzed factors such as data privacy, regulatory compliance, and cost.

Results: COCO vs. LLM Performance

Operational efficiency: COCO leads with 96% categorization accuracy in categorizing transactional data

One of the most notable results from the study was the accuracy achieved by our proprietary AI engine. COCO reached 96% accuracy, excelling especially in cases involving complex transactions:

• Irregular recurring payments.
• International transfers.
• One-off or atypical income.

In contrast, LLMs—while powerful—averaged 48% accuracy. Generalist models struggled with financial-specific contexts where language nuance and transaction structure require deep domain knowledge.

These models often returned blanks, hallucinated results, or produced confusing information—issues that might seem minor elsewhere but are major problems in banking. When it comes to money, precision is not optional.

🔎 Let me show you…

… how data analysis provides a better understanding of spending habits, economic priorities, and financial needs of your customer.

Speed and cost reduction: COCO’s responsiveness translates to savings for banks

COCO was designed specifically for demanding environments. Its lightweight architecture enables the processing of thousands of transactions in seconds. This makes it an ideal solution for high-demand scenarios, where agility not only improves user experience but also reduces infrastructure costs.

Processing speed is critical for banks, neobanks, and fintechs. Their apps, financial assistants, and fraud detection tools require instant responses due to the sheer volume of incoming requests. Slower processing leads to higher operating costs.

Generalist LLMs—unspecialized for banking—are at a disadvantage here: they are heavy, resource-intensive, and slow to respond. Ultimately, this makes them expensive and impractical for contexts that demand constant speed and efficiency.

Privacy and compliance: The advantage of On-Premise solutions over data sovereignty

COCO {Categorization} can operate locally if required by the client. Our tool ensures compliance with regulations like GDPR and PSD2, which is essential when handling sensitive financial data. We prioritize security and privacy, holding ISO 27001 certification.

Working with top-tier banks demands the highest standards of security and data encryption. Sensitive information is never exposed to third parties—unlike with many large language models.

LLMs are typically cloud-hosted and require additional security layers and in-depth legal reviews to comply with European and other international regulations. Notably, DeepSeek recently suffered a “dramatic security breach”, as reported by multiple sources.

Any data leak, unauthorized access, or misuse of sensitive information can result in regulatory penalties, failed audits, or even lawsuits. COCO ensures strict control over data traceability and usage, minimizing such risks.

Beyond the numbers: Additional benefits of COCO {Categorization}

Our categorization engine stands out not only in the four main areas above. It’s tailored to the financial sector, allowing us to offer functionalities that generalist models simply can’t match.

Data Enrichment

In addition to categorizing, COCO enriches transactional data with insights such as merchant geolocation, duplicate expense identification, or seasonal consumption pattern recognition.

Simple API Integration

COCO integrates via a single API, making implementation easy—no need for extra licenses or lengthy, costly integration processes. PSD2 licenses are also not required.

ISO 27001 Certification

Our tool is ISO 27001 certified, ensuring the highest standards of information security. Your clients’ data is protected by multiple layers of encryption.

Why generalist models aren’t ready to replace specialized solutions

While models like GPT-4, Claude, or Gemini boast impressive capabilities, their general-purpose nature works against them in scenarios that demand deep domain expertise.

The errors they make aren’t trivial: misclassifying income as expenses, or mistaking account transfers for purchases, can seriously impact customer experience and banking decisions.

They also show high variability and low consistency in ambiguous situations, whereas specialized models like COCO deliver reliable, stable performance.

Specialization is the key to making AI a competitive advantage

Generalist AI models are a major breakthrough, but in highly regulated, specialized environments like banking, specialization remains the best option. COCO {Categorization} has shown that combining AI with deep industry expertise is the key to delivering:

• Greater accuracy in output
• Faster banking data processing
• Extreme compliance and security
• Strategic value through upselling and cross-selling

These are the reasons why leading banks trust us to implement categorization, enrichment, and financial insights modules. If you’d like to learn more about our developments, request a personalized study of your case.

Next steps: Fintechs and big techs create hybrid models

At Coinscrap Finance, we are already exploring hybrid models, where generalist LLMs complement our specialized solutions. The goal is to leverage the best of both worlds: the versatility of LLMs combined with the efficiency of engines trained specifically for banking.

In short, we let LLMs do what they do best—to keep improving our proprietary AI engine. COCO handles the core: categorization, enrichment, and analysis of banking transactions with the kind of reliability that only comes from years of specialization.

Our integrations enhance engagement with digital platforms while guaranteeing the best possible user experience. Without a doubt, this is the ultimate recipe for retaining customers and attracting new business.

Are you ready to harness the full potential of AI?