Financial Automation with AI
RPA and AI (RAG) — knowledge base
An advanced solution combining Python RPA with generative AI. The system uses RAG for precise queries over complex knowledge bases, streamlining the flow of financial decisions.
IMPACT METRICS
85%+
minimum confidence
RAG
contextual retrieval
Less
manual triage
- Technologies
- PythonRAGGenerative AI
THE PROJECT
Python RPA automation of financial operations — classification, reconciliation, validations and integrations — complemented by an AI layer based on RAG (Retrieval-Augmented Generation).
RAG retrieves from the knowledge base the context the AI model needs to interpret each task description and decide which action the robot should take. When the decision's confidence falls below the defined threshold, the case automatically goes to human review.
STRATEGIC IMPACT
- Drastically less triage
- Greater document accuracy
- Full traceability
- Continuous learning of the AI engine
HOW IT WORKS
Architecture
01Automation (Python RPA)
Runs the financial flows: classification, reconciliation, validations and integrations between systems.
02Intelligence (AI with RAG)
Retrieves the relevant context from the knowledge base; with it, the model interprets the task and sets the robot's action.
03Governance and audit
Tracks decisions, stores the logs and triggers human review when needed.
Step by step
- 1The task description arrives.
- 2The text is pre-processed and normalised.
- 3Relevant documents are retrieved from the knowledge base.
- 4The context is enriched with what was retrieved.
- 5The AI model analyses the context and sets the robot's action.
- 6The decision's confidence level is calculated.
- 7At 85% confidence or more, the RPA flow runs automatically.
- 8Below 85%, the case always goes to human triage and review.
- 9Logs and feedback are recorded for continuous learning.
KEY CHALLENGES
- Ambiguous task descriptionsHow we solved it: Contextual retrieval and semantic similarity analysis.
- Unstructured dataHow we solved it: Normalisation pipelines, structured extraction and validation rules.
- Need for auditabilityHow we solved it: Structured decision logs, versioning of the knowledge base and full traceability.
- Operational risk controlHow we solved it: Human review whenever the automated decision is uncertain.
BUSINESS IMPACT
- Significantly less manual triage.
- More accurate classification and routing decisions.
- Full traceability for audit and compliance.
- Shorter processing time for financial operations.
- Continuous learning: reviewers validate uncertain cases and update the knowledge base, so accuracy improves over time.
