Financial Services
RAG & Knowledge Retrieval for Financial Services
RAG & Knowledge Retrieval for financial services, built around the constraint that defines the sector: every automated decision must be explainable and reproducible months after the fact.
- Regulations in scope
- 5
- Systems we integrate
- 5
- Typical first release
- 6 weeks
What changes when it is financial services
Permission-aware retrieval is not optional in an enterprise. If a user cannot open a document in SharePoint, the assistant must not quote it. We enforce that at the retrieval layer, not in the prompt.
In financial services, every automated decision must be explainable and reproducible months after the fact. That single fact reshapes how rag & knowledge retrieval has to be built here, the guardrails, the approval points and the evidence trail are design inputs rather than things bolted on before go-live.
The workload we are most often asked to take on first is client communication review, usually integrated against core banking. Integration comes before intelligence. A model that cannot reach your systems of record is a demo with good manners.
Built by engineers who ship production systems, not by a practice that subcontracts the build. Six weeks to something running in production, not six quarters to a strategy document.
The sector constraints we design around
- Defining constraint
- every automated decision must be explainable and reproducible months after the fact
- Regulations in scope
- RBI guidelines · SEBI regulations · DPDP Act 2023 · PMLA and AML rules · IRDAI where insurance applies
- Systems of record
- core banking · trading and OMS · loan origination · SAP and Oracle financials · regulatory reporting platforms
- Where we usually start
- credit memo drafting
RAG & Knowledge Retrieval workloads in financial services
- credit memo drafting
- KYC and onboarding checks
- regulatory report assembly
- reconciliation
- client communication review
What is included
- Ingestion pipeline for your real document formats
- Chunking and embedding strategy tuned to your corpus
- Hybrid keyword plus vector retrieval with reranking
- Citations on every answer, traceable to the source page
- Permission-aware retrieval that respects existing access rules
- Retrieval quality benchmarked against a labelled question set
Questions from this sector
Can we use AI in credit decisions?
With explainability, documented model governance and human review on adverse outcomes, yes. RBI expects you to be able to explain any decision that affects a customer.
How do you handle data residency?
Deployment inside Indian regions or on your own infrastructure, which is the usual requirement for regulated financial data.
RAG or fine-tuning?
RAG for knowledge that changes and must be cited; fine-tuning for style, format and task behaviour. Most production systems use RAG for the facts and light fine-tuning or few-shot prompting for the form.
How accurate will it be?
We build a labelled question set from your domain and report retrieval precision and answer accuracy against it. That number is the deliverable. We do not ship a system whose quality nobody has measured.
Can it respect our existing permissions?
Yes. Retrieval is filtered by the user's actual entitlements, so the assistant can never surface a document the user could not already open.
Other capabilities for financial services
- AI Agent Development for Financial Services
- Agentic Workflow Automation for Financial Services
- LLM Application Development for Financial Services
- Chatbot Development for Financial Services
- WhatsApp Bot Development for Financial Services
- Voice AI Agents for Financial Services
- Document Processing & IDP for Financial Services
- AI Copilot Development for Financial Services
- Predictive Analytics & Forecasting for Financial Services
- Data Engineering for Financial Services
RAG & Knowledge Retrieval for financial services, worth a conversation?
Tell us the workload and the regulation it sits under. We will tell you what is realistic.
Or email bd@dtrasglobal.com · call +91 74118 77878