What is this course about?
Portfolio analytics is computation plus narrative, and the two must be kept strictly apart. This course covers where AI genuinely helps — attribution narratives, benchmark commentary, risk reporting — while enforcing that every number comes from a deterministic calculation with lineage traceable from source system to client statement.
Who is this course for?
- Performance and attribution analysts
- Portfolio reporting teams
- Investment operations
- Client reporting and communications staff
What do I need before starting?
- Performance measurement or reporting experience
- Familiarity with your reporting systems
What will I be able to do afterwards?
- Separate deterministic computation from generated narrative architecturally
- Generate attribution narratives from computed results
- Produce benchmark and peer analysis at scale
- Personalize client reporting within compliance limits
- Trace lineage from source system to client statement
What does each module cover?
Where must the arithmetic stay deterministic?
45 minThe architectural separation between computation and narrative, and why it is not negotiable.
Objectives
- Separate computation from narrative architecturally
- Identify every place a number could be generated
- Design the separation into the system
Topics
Activity. Audit a reporting workflow for every point a number could be generated rather than computed.
How do you generate an attribution narrative?
50 minExplaining computed attribution results in language a client understands.
Objectives
- Generate narratives from computed attribution
- Ensure the narrative matches the numbers
- Explain at the right level for the audience
Topics
Activity. Generate attribution narratives and verify each statement against the computed results.
How do you handle benchmark and peer analysis?
45 minComparative analysis at scale with correct benchmark selection and honest comparison.
Objectives
- Select appropriate benchmarks defensibly
- Produce peer comparisons at scale
- Avoid misleading comparison
Topics
Activity. Produce benchmark commentary for a portfolio set and check for misleading framing.
How do you narrate risk and stress results?
45 minRisk reporting narratives that convey uncertainty accurately.
Objectives
- Narrate risk metrics without false precision
- Convey uncertainty honestly
- Explain stress test results accessibly
Topics
Activity. Narrate a stress test result and check it for false precision.
How much can you personalize a client report?
40 minPersonalization within the compliance limits that govern client communication.
Objectives
- Personalize within compliance limits
- Avoid crossing into individualized advice
- Maintain consistency across clients
Topics
Activity. Personalize a report and check it against the advice boundary.
What is the verification gate?
45 minThe check that makes it impossible for a generated number to reach a client.
Objectives
- Implement a numeric verification gate
- Verify every figure against its calculation
- Make the gate non-bypassable
Topics
Activity. Implement the gate and attempt to publish a generated figure.
How do you trace lineage to the statement?
45 minData lineage from source system through calculation to the number on a client statement.
Objectives
- Implement lineage from source to statement
- Reconstruct any figure's derivation
- Support audit and client inquiry
Topics
Activity. Trace one client statement figure back to its source system records.
Building the reporting agent
50 minThe lab module: a reporting agent where numbers come from calculation, not generation.
Objectives
- Build the reporting agent with strict separation
- Verify no figure is generated
- Measure production time savings
Topics
Activity. Build the agent and prove no number in its output was generated.
What is the capstone project?
Client reporting agent with verified numeric integrity
Build a portfolio reporting workflow with architectural separation of computation and narrative, verified attribution narratives, defensible benchmark commentary, compliance-limited personalization, a non-bypassable verification gate, and full lineage to the statement.
Deliverable: A working reporting agent with proof that no published figure was generated.
How are learners assessed?
- Verification gate tested — a generated figure must be impossible to publish
- Lineage test — trace a statement figure to source records
- Narratives verified statement by statement against computed results
What ships with the course?
Facilitator guide
Session-by-session running order, discussion prompts, and the questions that reliably derail a room.
Learner workbook
Exercises, checklists, and the templates each module's activity produces.
Hands-on lab environment
A sandboxed ibl.ai deployment so exercises run against real agents, not screenshots.
Assessment bank
Scenario questions and rubric criteria mapped to each stated learning outcome.
Source bibliography
Every primary regulation and standard cited on this page, linked and dated.
Which AI agents does this course use?
The hands-on modules run against agents already deployable on the ibl.ai platform for financial services.
Where does the course material come from?
Every module is grounded in primary sources — the regulation, standard, or research itself, not a summary of it. Each was resolved at authoring time.
- U.S. Securities and Exchange Commission
SEC
Performance presentation and marketing rule requirements.
- Artificial Intelligence
FINRA
Guidance on AI in client-facing analytical output.
- Supervision and Regulation Letters
Federal Reserve
Model risk expectations applying to analytics used in reporting.
- AI Risk Management Framework
NIST
Verification and monitoring structure for the reporting pipeline.
Delivery notes
Binding guidance for anyone preparing and delivering this course.
- Module 1's separation is the entire course. A generated number in a client statement is a regulatory and reputational failure, and the architecture must make it impossible rather than unlikely.
- Module 6's gate should be tested adversarially. Participants should genuinely try to get a generated figure through, and the ones that succeed are the design flaws.
- Module 4's false precision problem is subtle and important. Models narrate uncertainty poorly by default and will state a risk figure with unwarranted confidence.
- Use synthetic portfolios with known correct attribution. Verification requires ground truth and real portfolios cannot provide it in a workshop.
- Coordinate with FIN-5 — personalization limits appear in both and should be consistent.
Why run AI training on a platform you own?
You own the course, not a licence to it
Course content, learner data, and the platform run inside your perimeter — you own all the code and the data.
Model-agnostic delivery
Run the course's AI components on any LLM — Claude, GPT, Llama, Gemini, Command — and switch anytime.
No per-seat training licences
Usage-based or self-hosted, so cost tracks actual use rather than headcount.
Deploy anywhere
Cloud, private VPC, on-premise, or fully air-gapped — including for cohorts that cannot use public AI tools.
Frequently asked questions
What does the Portfolio Analytics with AI Agents course cover?
Portfolio analytics is computation plus narrative, and the two must be kept strictly apart. This course covers where AI genuinely helps — attribution narratives, benchmark commentary, risk reporting — while enforcing that every number comes from a deterministic calculation with lineage traceable from source system to client statement. It runs 5 hours across 8 modules across 8 modules, at intermediate level, and closes with a capstone: Client reporting agent with verified numeric integrity.
Who should take Portfolio Analytics with AI Agents?
It is written for Performance and attribution analysts, Portfolio reporting teams, Investment operations, Client reporting and communications staff. Prerequisites: Performance measurement or reporting experience; Familiarity with your reporting systems.
Can we run this course on our own infrastructure?
Yes. ibl.ai is model-agnostic and deploy-anywhere — cloud, private VPC, on-premise, or fully air-gapped — and you own all the code and the data. Cohort data, submissions, and any material learners upload stay inside your perimeter, which matters for financial services teams that cannot send work to a public AI tool.
How do we get access to Portfolio Analytics with AI Agents?
Request access and we will set it up for your cohort — hosted by ibl.ai, or running against your own deployment. Tell us the group size and timing you need, and whether it should run inside your own perimeter.
How much does AI training for financial services cost on ibl.ai?
There is no per-seat pricing — you pay for usage or self-host and pay only for the infrastructure, so a 5,000-person rollout does not cost 5,000 licences. 1.6M+ users across 400+ organizations run the platform this way, including NVIDIA, MIT, and Syracuse University.