Feeling Lost in the AI Jungle? Let’s Untangle RAG Together

Artificial Intelligence has quickly become part of the daily reality in Belgian banking and insurance. But as our 2025 AI Barometer shows, scaling GenAI also brings new challenges: 62% of institutions cite integration complexity as their top obstacle, while 38% highlight compliance concerns.

This is where Retrieval-Augmented Generation (RAG) comes in.
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What RAG Is and Why It Matters?
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Large Language Models (LLMs) are powerful because they’ve been trained on vast amounts of internet data. But that strength is also their weakness: they don’t automatically know your company’s policies, risk frameworks, or product specifics.

RAG fixes this gap in two steps:

1. Retrieval → when asked a question, the model fetches relevant information from your internal sources (e.g., SharePoint, databases, policy libraries, product sheets).
   
   
2. Augmented Generation → it then uses this information to generate a precise, conversational answer tailored to your context.
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Think of it as giving your AI a second brain: one that holds your institution’s confidential knowledge and makes it instantly available to staff.

This isn’t just theoretical. In our AI Barometer 2025 , 76% of Belgian financial institutions ranked RAG as “critical” or “important” for their three-year strategy, placing it alongside cloud AI services, responsible AI, and agentic AI.

Yet, as our 2025 AI Barometer also highlights, scaling AI comes with challenges. 62% of institutions cite the complexity of integrating AI into existing systems as their biggest obstacle, while 38% point to compliance concerns.
RAG sits at the heart of this puzzle: it can make AI more accurate and domain-specific, but it also requires clean, well-governed internal data to work effectively.
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How RAG Creates Value in Banking & Insurance
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RAG can be used in areas where accuracy and context are critical:

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• Credit Risk & Loan Application Consistency: A Branch Manager in Brussels is reviewing a complex commercial real estate loan application. Instead of spending an hour sifting through 100+ pages of the bank's internal Credit Risk Manuals and local Belgian banking regulations (e.g., specific debt-to-income limits for a certain loan type), the RAG system allows them to ask: "What is the maximum allowed LTV (Loan-to-Value) for an office building in a high-flood-risk zone in Flanders, given the applicant's current equity profile?" The system instantly retrieves the exact paragraph from multiple, specific policy documents, citing the source, ensuring fast, compliant, and consistent risk assessment across all branches.
  
  
• Dynamic Regulatory Compliance & Reporting: An Insurance Compliance Officer in Antwerp needs to verify that the disclosures for a new life insurance product comply with recent EU directives (like IDD or AML-related changes) and specific Belgian supervisory body rules (e.g., FSMA circulars). They query the RAG assistant: "What are the mandatory waiting periods and penalty clauses that must be clearly stated for early cancellation of a Type 23 life insurance product sold to a resident under 40, as per the latest FSMA guidance?" The RAG solution pulls this precise, consolidated answer from constantly updated internal and external regulatory repositories, drastically reducing the risk of non-compliance fines and manual error in time-sensitive reporting cycles.
  
  
• Personalized Product Expertise & Cross-Selling: A Contact Center Agent handling a customer call in Ghent needs to transition from a basic query to a cross-selling opportunity. The customer has a basic savings account and an expiring car insurance policy. Instead of placing the customer on hold to navigate siloed internal databases for product specifications, the agent asks the RAG tool: "Based on the client's current profile, what are the three key benefits and eligibility criteria for our 'Green Mobility' loan, and is there a current campaign that bundles it with our premium Home Insurance?" The assistant provides a concise, accurate, and immediately actionable script, enabling on-the-spot, hyper-personalized cross-selling that increases both sales conversion rates and customer satisfaction.

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From Pilots to ROI
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The AI Barometer shows just how quickly the sector is maturing:

• 85% of institutions now have a dedicated AI unit, up from just 37% in 2024.
  
  
• 92% rank productivity gain as their top priority for AI adoption, with process efficiency and cost savings close behind.
  
  
• Nearly half of institutions expect their AI investments to pay for themselves or deliver strong returns this year
  
  

RAG plays directly into these priorities. By grounding AI responses in company-specific data, it reduces the risk of “hallucinations,” accelerates access to internal knowledge, and strengthens compliance turning AI from a generalist tool into a trusted institutional assistant.
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Beyond Technology: The Governance Challenge
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Our work with Belgian banks and insurers shows that RAG projects are only 30% technology and 70% knowledge management. The biggest challenge is ensuring that the knowledge base retrieves from accurate, consistent, and well-governed insights.

• If policies differ between the French and Dutch versions, the assistant will reveal the contradiction.
  
  
• If information is hidden in tables or outdated files, answers will be incomplete or misleading.
  
  

As the Barometer stresses, 92% of institutions prioritize productivity gains from AI. But achieving these gains with RAG requires clean, well-structured content and clear ownership of databases. In other words: before trusting an assistant, institutions must trust the knowledge they feed into it.
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The Augmented Future
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The AI Barometer makes it clear: Belgian financial institutions are already scaling AI, embedding assistants across their workforce, and preparing for what comes next. Alongside agentic AI, RAG is one of the most anticipated technologies for the next three years.

For leaders, this means shifting focus to execution:

• Equip AI with your organization’s knowledge.
  
  
• Govern and structure content for accuracy.
  
  
• Embed assistants where staff need them most  in branches, compliance, risk, HR, and beyond.
  
  

When applied correctly, RAG transforms artificial intelligence from a general-purpose tool into a reliable corporate assistant that reduces risk, accelerates the decision-making process, and builds trust across the organization.
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At Sailpeak, we believe in the power of clarity. As the AI Barometer shows, organizations that make RAG understandable and functional will be the ones shaping Belgium's AI-powered financial future.

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