Decoding ML and LLMs: A Guide for Financial Firms

As financial institutions struggle with growing volumes of unstructured document data, AI tools have emerged to help. But choosing the right solution has become increasingly complex.With increasing pressure to automate operations and extract greater value from the data, the conversation around artificial intelligence is evolving. Once dominated by traditional machine learning (ML), the spotlight has shifted to large language models (LLMs).

While both technologies share the common goal of extracting valuable data with precision, their methodologies come with distinct advantages and trade-offs. Effectiveness often depends on factors such as document structure, data variability, and broader business objectives like cost, speed, and flexibility. Additionally, the rise of open-source and closed-source LLMs presents new decision points for organizations.

These are powerful tools that are reshaping how firms process and interpret information, but what’s the difference, and which is better suited to meet the sophisticated demands of financial Institutions? This guide outlines how to evaluate ML and LLMs for unstructured document processing, when to use each, how they compare, and how they can work together to deliver maximum impact.

Understanding ML VS LLM

Traditional ML: Powerful, But Purpose-Built

Machine learning models have long powered key financial services workflows such as document classification, data extraction, anomaly detection, and forecasting. ML models are trained on labeled datasets to learn the relationships between document content and specific data elements of interest. When built to generalize effectively, these models can perform well even when encountering new layouts not seen during training. This makes ML a powerful approach for extracting key information from documents that share consistent data patterns, even when the visual formatting varies significantly.

Best For: High-accuracy extraction across structurally consistent document types with some variability in format like Capital Notices, Capital Account Statements and Loan Agent Notices

Strengths: Fast inference, high precision, efficient scaling across document variants, and lower compute costs compared to LLMs.

Limitations: Task-specific and inflexible to format changes, labor-intensive to train and maintain and limited adaptability across document types or asset classes.

Enter LLMs: Contextual Understanding at Scale

Large language models like GPT and DeepSeek represent a new era of AI. Unlike traditional ML, LLMs aren’t trained for a single task. Instead, they use deep contextual understanding to process natural language, recognize patterns, and perform reasoning. Trained on massive datasets of unstructured text, LLMs excel at interpreting language in context. They are highly adaptable and can handle unfamiliar formats, ambiguous language, and cases that traditional approaches may struggle with.

A well-trained LLM can handle variation across document formats, respond to nuance, and extract meaning even from loosely structured content. This has powerful implications for alternative investments, where firms process thousands of diverse documents.

Best For: Complex, unstructured, or narrative-heavy documents where understanding context and intent is key like ESG reports and pitch books

Strengths: Flexibility, semantic understanding, and the ability to reason across diverse and unfamiliar document types.

Limitations: High compute cost and longer latency, LLMs requires careful prompt engineering and testing.

Open-Source vs. Closed-Source LLMs

Once an LLM is selected, organizations must decide whether to adopt an open-source or closed-source model. This choice often reflects priorities around control, cost, compliance, and operational complexity.

Open-Source LLMS but Privately Secured

Open-Source LLMs make their architecture and pretrained weights publicly available (i.e. LLaMA, Mistral, BLOOM). They can be downloaded, modified, fine-tuned, and deployed on private infrastructure, offering full control over data privacy, customization, and cost.

Advantages:

• No licensing fees
• Full control and transparency
• On-premise deployment for privacy and data residency
• Avoids vendor lock-in

Challenges:

• Requires in-house ML/infra expertise
• Responsibility for updates, security, and optimization

Closed Source LLMS

Closed-Source LLMs are proprietary models accessed via proprietary APIs (e.g., OpenAI GPT-4, Claude, Google Gemini). Their internal design and weights are not disclosed. While easier to deploy and maintain, they offer less transparency, limited customization, and create vendor dependency.

Advantages:

• Plug-and-play via API
• Best-in-class performance on many general tasks
• Infrastructure and scaling managed by the provider
• Vendor support and updates included

Challenges:

• Usage-based pricing can be expensive at scale
• Limited transparency and model control
• Regulatory or privacy concerns with cloud-based inference

At Alkymi, we leverage LLMs in a secure, enterprise-grade environment through integrations with Google Cloud’s Gemini AI, AWS, and other private cloud infrastructures. These integrations ensures that client data remains private, protected, compliant, and siloed from public models, enabling financial institutions to securely transform data workflows and meet stringent data security, privacy, and compliance requirements. You can read more about our Google Cloud's Gemini AI integration.  

Key Takeaways

• Nature of the Task: Use ML for defined field extraction across consistent document types; use LLMs for complex, language-driven interpretation.
• Cost and Speed: ML offers efficient performance and lower compute costs for high-volume extraction tasks. LLMs require more resources but handle ambiguity and variability better.
• Flexibility: LLMs excel at interpreting diverse, unstructured content. ML delivers consistent accuracy across structurally similar documents, even when format varies.
• Customization and Control: ML models can be deeply customized and trained for generalization. LLMs are easier to fine-tune but offer less control over internal behavior.
• Deployment Strategy: Open-source LLMs provide privacy and flexibility through on-premise use; closed-source models offer ease of integration and vendor-managed infrastructure.

It’s Not ML vs. LLM - It’s ML and LLM

Selecting between ML and LLMs isn’t a binary decision, but it does require clarity on the task at hand. You shouldn’t default to one approach over the other without first understanding the requirements, complexity, and goals of your workflow. ML offers speed, consistency, and generalization for extracting structured data across similar document types, even when formats vary. LLMs provide the flexibility and contextual understanding needed to interpret unstructured or highly variable content. The best results come from aligning model capabilities with the specific demands of each document workflow.

At Alkymi, our platform supports both ML- and LLM-based workflows. We work to ensure that both output quality and data security meet the reliability and compliance standards expected by our clients.

What This Means for Investment Operations

Whether you're processing capital activity notices or managing document-heavy private credit workflows, the right model, or combination of models can reduce manual work, accelerate turnaround times, and improve data accuracy.

As AI capabilities evolve, the real differentiator will be how firms integrate them into workflows. With a thoughtful strategy, ML and LLMs can work together to unlock competitive advantage.

Ready to move beyond legacy data workflows?

At Alkymi, we’ve worked with financial institutions since 2017 to transform how they process and act on unstructured data. As innovation accelerates, understanding the capabilities and limitations of ML and LLMs is critical to designing the right data automation strategy. 

Want to learn how Alkymi can transform your operations? Connect with us