The most detailed open-source guide to automating every operation in Indian broking, mutual fund distribution, and insurance distribution using AI agents.
Your ops team spends 6 hours a day on trade reconciliation. An AI agent does it in 11 minutes.
Your KYC team rejects 30% of applications for fixable errors. An AI agent fixes them before rejection.
Your commission team takes 5 days to reconcile AMC payouts. An AI agent does it overnight.
This repo shows you exactly how — with 52 use cases, architecture diagrams, working code, ready-to-use prompts, and a prioritised rollout plan.
A mid-size Indian stockbroker with 5-10 lakh clients employs 200-500 people in operations. They process 50,000+ trades/day, generate 10,000+ contract notes, handle 500+ KYC applications, and file 40+ regulatory returns/month.
80-90% of what these people do every day is read a document, type it into a system, compare two spreadsheets, or send a templated message.
That is not a job. That is a script waiting to be written.
Agentic AI — LLMs that can read documents, call APIs, make decisions, and execute multi-step workflows — makes it possible to automate not just the easy 80%, but to intelligently triage the hard 20% too.
This repository is the blueprint.
| If you are a... | Start here | Then forward this repo to... |
|---|---|---|
| CEO / COO of a broking firm | The 52 Use Cases (scan the tables) | Your CTO, Head of Ops, and Head of Product |
| Head of Operations | Broking Ops Deep Dive — you will recognise every pain point | Your team leads, and your vendor evaluation committee |
| CTO / Engineering Lead | Agent Design Patterns and Trade Recon Playbook | Your dev team and your cloud/AI vendor contacts |
| MF Distributor / IFA | MF Distribution Deep Dive — this is your daily life | Other IFAs in your WhatsApp group |
| Insurance Corporate Agent | Insurance Distribution Deep Dive | Your ops manager and insurer relationship contacts |
| Developer / AI Engineer | Implementation Guide and the Playbooks — copy-paste ready | Your PM (so they understand what is possible) |
| Startup Founder building for BFSI | Everything — this is your market research | Your co-founder and your investors |
agentic-wealthops/
|
|-- README.md ..................... You are here. Start here.
|-- GLOSSARY.md .................. Every BFSI and AI term explained in plain English
|-- CONTRIBUTING.md .............. How to add to this project
|
|-- docs/
| |-- 01-foundations/ .......... Why this matters, why now, which AI platform to use
| |-- 02-broking-ops/ ......... Every broking operation, mapped and scored
| |-- 03-mf-distribution/ ..... Every MF distribution operation, mapped and scored
| |-- 04-insurance-distribution/ Every insurance distribution operation, mapped and scored
| |-- 05-agentic-architecture/ . How to design AI agents for financial ops
| |-- 06-implementation/ ....... Prioritisation, build-vs-buy, prompts, testing, deployment
| |-- 08-regulatory/ ........... SEBI, IRDAI, DPDPA — what the regulators say about AI
|
|-- playbooks/ ................... Step-by-step implementation guides with code
|-- examples/ .................... Code samples (Python, prompts, schemas)
|-- templates/ ................... Reusable templates for agent design
How to read the scorecards: Each use case is rated on automation potential (what % of the work can AI do today), implementation complexity, and estimated ROI payback period.
| # | Use Case | People Today | AI Potential | People After | Complexity | Payback | Priority |
|---|---|---|---|---|---|---|---|
| 1 | KYC document verification | 10-30 | 95% | 2-3 | Medium | 3 months | Phase 2 |
| 2 | Account opening form processing | 5-15 | 95% | 1-2 | Medium | 3 months | Phase 2 |
| 3 | Client data modification | 3-8 | 90% | 1 | Low | 2 months | Phase 1 |
| 4 | Trade reconciliation — 3-way match | 5-20 | 95% | 1-2 | Medium | 2 months | Phase 2 |
| 5 | Margin shortfall monitoring | 3-8 | 90% | 1 | Medium | 3 months | Phase 2 |
| 6 | Contract note generation | 2-5 | 95% | 0-1 | Low | 1 month | Phase 1 |
| 7 | Fund settlement | 3-8 | 80% | 1-2 | Medium | 4 months | Phase 2 |
| 8 | Client ledger reconciliation | 3-10 | 90% | 1 | Medium | 3 months | Phase 2 |
...and 14 more broking use cases in the full docs.
Broking total: 55-160 ops staff at a mid-size broker. After AI: 15-25 people (75-85% reduction).
| # | Use Case | People Today | AI Potential | People After | Complexity | Payback | Priority |
|---|---|---|---|---|---|---|---|
| 23 | Client onboarding: eKYC | 5-15 | 95% | 1-2 | Low | 2 months | Phase 1 |
| 24 | SIP registration | 3-8 | 90% | 1 | Low | 2 months | Phase 1 |
| 26 | Failed SIP tracking | 2-5 | 90% | 0-1 | Low | 1 month | Phase 1 |
| 27 | Commission reconciliation | 3-10 | 90% | 1 | Medium | 3 months | Phase 2 |
...and 12 more MF use cases in the full docs.
MF distribution total: 30-90 ops staff. After AI: 8-15 people (70-80% reduction).
| # | Use Case | People Today | AI Potential | People After | Complexity | Payback | Priority |
|---|---|---|---|---|---|---|---|
| 39 | Proposal form filling across 9 insurer portals | 5-15 | 90% | 1-2 | Medium | 3 months | Phase 2 |
| 44 | Renewal management | 3-8 | 95% | 1 | Low | 2 months | Phase 1 |
| 46 | Commission reconciliation | 2-5 | 90% | 0-1 | Medium | 3 months | Phase 2 |
...and 11 more insurance use cases in the full docs.
Insurance distribution total: 25-75 ops staff. After AI: 8-15 people (65-75% reduction).
| Stays Human | Why |
|---|---|
| Novel regulatory interpretation | SEBI issues a new circular with ambiguous language — someone needs to interpret intent |
| High-value client relationship management | Your top 50 HNI clients want to talk to a human. Period. |
| Complex claims negotiation | Disputed insurance claims require empathy, negotiation, and judgment |
| Ethical judgment calls | Should we onboard this PEP? AI flags, human decides. |
| System failure improvisation | The exchange API is down at 3:47 PM on settlement day. |
| Audit defence | SEBI inspector asks "why did you do this?" — a human needs to explain |
The AI agent does the work. The human does the thinking.
If you read nothing else, here are the 7 automations you can ship in 30 days with the highest ROI:
| # | What | Before | After |
|---|---|---|---|
| 1 | Email-to-structured-data for exchange circulars | 2 people reading emails all day | Zero. Auto-parsed and routed. |
| 2 | Contract note QA | Manual spot-checking | 100% automated validation |
| 3 | Client communication (margin calls, SIP reminders) | 3 people sending WhatsApp/emails | AI-generated, personalised, auto-dispatched |
| 4 | Failed SIP recovery | Manual calling | Auto-detect, diagnose, outreach |
| 5 | CAMS/KFintech reverse feed processing | 2 people, 2 hours/day | Zero-touch automated pipeline |
| 6 | Renewal tracking for insurance | Manual diary + calls | Predictive 90-day engagement engine |
| 7 | Scheme master updates from AMC circulars | Manual reading + data entry | Auto-parsed and applied |
This is a living document. Every chapter can be deeper. Every playbook can have more code.
We especially want:
- Ops practitioners who can validate (or challenge) the automation potential ratings
- Developers who have built agents for any of these use cases
- Compliance experts who can add regulatory nuance
- Founders who can share production learnings
See CONTRIBUTING.md for how to submit.
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0).
Star the repo. It helps the algorithm, and it helps the next ops person find this before they burn out.
Built at the intersection of financial operations experience and AI engineering obsession.
Last updated: March 2026