Put AI to work without giving up control.
Financial institutions operate on information, judgment and trust.
Margins builds AI into financial workflows to help teams process information faster, automate repetitive work, identify what deserves attention and make institutional knowledge more accessible — while designing around the security, infrastructure and oversight requirements of the business.
More intelligence. Less operational friction. Clear human control.
Financial services already runs on intelligence. AI changes how much information people can work with.
Around sophisticated systems, processes and controls sits enormous amounts of work involving people:
- Reading
- Comparing
- Checking
- Searching
- Classifying
- Summarizing
- Reconciling
- Investigating
- Documenting
- Escalating
AI is most valuable when it expands human capacity without removing human accountability.
Some of the most important financial information doesn’t arrive as a database field.
- Contracts
- Financial statements
- Emails
- Invoices
- Applications
- Reports
- Policies
- Customer correspondence
- Regulatory documents
- Supporting evidence
- PDFs
- Spreadsheets
- Notes
- 01Unstructured information
- 02Understand
- 03Extract
- 04Classify
- 05Validate
- 06Structured process
- 07Human exception
Turn information people have to read into information systems can work with.
Not every step requires judgment.
Many financial workflows contain both. AI and automation can increasingly handle the repetitive information work while keeping people focused on the parts requiring expertise.
Preparation.
- Collect information.
- Check fields.
- Extract values.
- Compare records.
- Apply known rules.
- Route documents.
- Prepare reports.
Expertise.
- Interpret ambiguity.
- Assess unusual situations.
- Approve exceptions.
- Make consequential decisions.
Automate the preparation. Preserve judgment where judgment matters.
Reduce the distance between receiving information and acting on it.
The goal isn’t to eliminate review. It’s to eliminate the work that shouldn’t require review in the first place.
- Receive
- Open
- Read
- Find information
- Enter data
- Check
- Route
- Review
- Receive
- AI understands
- Data extracted
- Rules applied
- Exceptions identified
- Human review
Let people work the exceptions instead of processing every case equally.
Traditional workflows often require a person to touch every item. AI can process information, apply approved logic and identify uncertainty — routing routine cases through the workflow and exceptions to human expertise.
The objective isn’t maximum automation. It’s putting human attention where it creates the most value.
People shouldn’t have to find the signal manually.
Machine learning can help identify patterns that differ from expected behaviour and surface them for investigation. Potential applications:
AI doesn’t have to make the risk decision to materially improve how quickly the right cases reach the right people.
- 01Everything
- 02Detection
- 03Prioritization
- 04Investigation
- 05Decision
Investigators need better signals, not more alerts.
- Too much information.
- Too many alerts.
- Too many documents.
- Too many manual checks.
- Information extraction
- Case summarization
- Entity / context retrieval
- Alert enrichment
- Document analysis
- Pattern detection
- Investigation preparation
- Case prioritization
The value isn’t generating another alert. It’s giving an investigator better context around the alert that matters.
Don’t make investigators reconstruct every case from scratch.
An alert may be only the beginning. AI can help assemble customer information, transaction history, related entities, previous cases, documents, policies and risk indicators into a structured investigation workspace.
Reduce time spent assembling the case. Increase time spent understanding it.
The answer shouldn’t depend on finding the person who remembers the policy.
Don’t make employees search the knowledge base. Let the knowledge base answer them.
Explore Enterprise Knowledge & RAG- “Which procedure applies to this exception?”
- “What documentation is required for this case?”
- “What changed in the latest version of this policy?”
- “Which internal control applies here?”
- “Show me the source for this answer.”
In financial services, a plausible answer isn’t enough.
Enterprise AI should be designed to answer a different question: where did this answer come from? Depending on the use case:
Trust improves when people can inspect the information behind the answer.
What documentation is required for this case?
Answer · illustrativeFor this case type, three documents are required: proof of identity, proof of address and a source-of-funds declaration.1,2
User verifies ✓
Some of your most valuable financial knowledge isn’t documented.
- 01
An experienced analyst recognizes something unusual immediately.
- 02
A compliance specialist knows which detail changes the interpretation of a case.
- 03
A senior accountant knows which exception requires investigation.
- 04
A relationship manager understands context that doesn’t exist in the CRM.
- 05
An operations leader knows why the documented process works differently in reality.
Policies explain how the organization should work. Experienced people understand how it actually works. AI becomes more useful when it can work with both.
Explore CaptaGive experts more capacity without removing responsibility.
AI copilots can potentially assist professionals with:
Use AI to expand expert capacity, not to obscure who is accountable.
- 01AI prepares
- 02Professional reviews
- 03Professional decides
- 04System records
Move repetitive processing toward exception-based operations.
Margins’ work with Unija automates document processing and operational workflows across payroll and accounting, including ERP integration, portal automation and CRM workflows.
- 01Documents + portals + email + data
- 02AI + automation
- 03Routine processing
- 04Exceptions
- 05Specialist
People should spend less time moving information and more time handling the cases where their expertise matters.
Read the Unija case studyDecision support, not decision theatre.
Executives and analysts already have reports, dashboards, KPIs, spreadsheets and forecasts. What AI can add is the ability to investigate information differently.
- 01What changed materially this week?
- 02Which accounts explain most of this variance?
- 03Where are we seeing unusual behaviour?
- 04Which exceptions deserve attention?
- 05Summarize the drivers behind this change.
- 06What information supports this conclusion?
The goal isn’t another dashboard. It’s reducing the distance between a question and an informed decision.
Your most valuable financial intelligence shouldn’t require giving up control of your data.
Useful financial AI may need access to- Customer information
- Transactions
- Financial records
- Contracts
- Internal policies
- Risk information
- Employee knowledge
- Proprietary processes
- Historical cases
- 01Customer-controlled cloud
- 02Private infrastructure
- 03On-premise deployment
- 04Private models
- 05Open-source models
- 06Selective external models
- 07Hybrid architectures
Private AI isn’t about disconnecting from innovation. It’s about deciding intentionally what stays inside, what can leave and why.
Not every financial task should send data to the same model.
A highly sensitive internal workflow may require one architecture. A low-risk public-information task may justify another. A predictive use case may not need an LLM at all.
Model selection should be an architecture decision, not a company-wide default.
AI should operate inside the rules of the organization.
Connecting an AI assistant to enterprise knowledge doesn’t mean every employee should suddenly have access to everything the AI can retrieve. Architecture should account for:
Explore Security & TrustAI should respect the same organizational boundaries as the people using it.
The more AI can do, the more important control becomes.
An AI assistant retrieves and prepares information. An AI agent can potentially interact with systems and take actions — which requires explicit boundaries.
- →Retrieve a case.
- →Collect supporting information.
- →Check required documentation.
- →Prepare an analysis.
- →Create a task.
- →Update an approved field.
- →Request human approval.
- →Route the case.
- 01What it can see
Data and context boundaries.
- 02What it can do
Tools and approved actions.
- 03When it must ask
Human approval points.
- 04What gets recorded
Traceability of every step.
Autonomy should increase with evidence, not ambition.
AI should fit into the financial architecture, not sit beside it.
Relevant information may live across core financial systems, ERP, CRM, document management, data warehouses, accounting systems, risk platforms, customer portals, internal applications and identity systems.
The goal isn’t another AI platform employees need to visit. It’s intelligence inside the processes they already run.
Explore IntegrationsIf AI affects a financial process, you need to understand what happened.
What entered the workflow, which sources were retrieved, which component processed it, what it produced, what action followed, whether a person reviewed it — and what happened afterwards.
- 01Input
- 02Context
- 03AI
- 04Output
- 05Review
- 06Action
- 07Outcome
A system you can’t observe is a system you can’t responsibly operate.
Production is where trust has to survive.
- Models change.
- Data changes.
- Documents change.
- Users change how they interact.
- Costs change.
- New edge cases appear.
- Performance can drift.
Healthy infrastructure does not necessarily mean healthy AI.
Explore Managed AI- System performance
- AI quality
- Reliability
- Usage
- Cost
- Exceptions
- Business impact
See processes, not AI products.
Hover or tap any part of the institution to see the AI opportunities worth investigating there.
- Exception intelligence
- Information synthesis
- Decision support
- Request classification
- Employee copilots
- Information retrieval
- Document processing
- Information extraction
- Case preparation
- Exception handling
- Workflow automation
- Reconciliation support
- Document intelligence
- Email processing
- Anomaly detection
- Case enrichment
- Investigation support
- Knowledge retrieval
- Reporting support
- Information synthesis
- Policies
- Procedures
- Regulations
- Institutional expertise
- Document processing
- Reconciliation
- Reporting support
- Case enrichment
- Prioritization
- Investigation workspace
- Private AI
- Model orchestration
- Enterprise integrations
- Managed AI
Start with the workflow where information is expensive to process.
Look for processes where:
- People read large amounts of information.
- The same checks happen repeatedly.
- Cases move through several systems.
- Experts spend time preparing rather than deciding.
- Exceptions are difficult to identify.
- Employees repeatedly search for knowledge.
- Important signals are buried in high volumes of activity.
- Manual work creates operational bottlenecks.
- 01Business problem
- 02Process
- 03Information + systems
- 04Risk / control requirements
- 05AI opportunity
- 06Value × Feasibility
- 07First implementation
The best AI opportunity is where automation creates value without removing the control the process requires.
Start controlled. Expand with evidence.
Financial AI doesn’t need to begin with enterprise-wide autonomy.
Search
Find approved information faster.
Assist
Prepare work for employees.
Analyze
Detect patterns and exceptions.
Recommend
Support decisions.
Automate
Handle defined routine workflows.
Agentic
Allow controlled actions.
Increase autonomy as confidence, controls and evidence increase.
Human oversight should be designed, not added later.
Different decisions require different levels of autonomy.
- AssistAI retrieves information.
- PrepareAI prepares an output.
- RecommendAI proposes a decision.
- Act + reviewAI acts within boundaries; a person verifies.
- AutomateAI handles proven, controlled workflows.
Human oversight isn’t evidence that AI failed. In many financial workflows, it’s part of the correct architecture.
Build the intelligence and the controls around it.
Business first
Start with the financial or operational outcome rather than the model.
AI + software engineering
Build complete production systems rather than isolated AI demonstrations.
Private architecture
Design around sensitive information and customer-controlled environments.
Enterprise integration
Connect AI to existing financial systems and workflows.
Multiple AI approaches
Use LLMs, RAG, predictive ML, automation and other techniques according to the problem.
Production ownership
Forward Deployed Engineering and Managed AI keep the system close to operational reality after deployment.
Relevance, credibility, architecture.
AI automation for accounting and payroll operations.
Document processing, ERP integration, portal automation and CRM workflows connected into an AI-enabled operational layer — reducing the repetitive work that has to be processed manually.
Read case study →Related capabilities
Intelligent Automation
Move repetitive financial workflows toward exception-based operations.
Explore Intelligent Automation →Enterprise Knowledge & RAG
Give employees trusted access to institutional knowledge.
Explore Enterprise Knowledge →Private & On-Premise AI
Build AI around sensitive financial information without unnecessarily giving up control.
Explore Private AI →AI Agents
Introduce controlled AI actions into enterprise workflows.
Explore AI Agents →Managed AI
Operate and monitor AI after it enters production.
Explore Managed AI →
Where is information consuming more expert time than judgment?
We’ll work with your leadership, operational and technology teams to identify where AI can materially reduce repetitive work, improve access to information or strengthen decision support — while designing around the controls the business requires.
Explore AI opportunitiesStart with the workflow. Define the controls. Prove the value.