AI in Financial Modelling

01What you'll walk away with

Six things you'll own
by the final week

Not topics you sat through: artefacts and habits you can show a hiring manager or apply to Monday's sprint.

01

A prompting system for financial work

Structure that produces analysis you can trace back to a source, not a confident paragraph you then have to fact-check line by line.

02

A model-review checklist

What to verify before a number leaves your desk: assumptions, circularity, edge cases, and the silent errors a model will not flag.

03

A working auditable workflow

Built and defended, with the assumptions written down and every input traceable. The artefact is the point.

04

A fraud and anomaly instinct

Where AI genuinely helps surface signals, and where it invents patterns that were never in the data.

05

A compliance-safe AI policy

What client and customer data can never enter a prompt, and how to work usefully inside CBN and internal governance limits.

06

A 30-day adoption plan

The two or three processes at your own institution worth automating first, and the ones to leave alone.

02Curriculum · 8 classes over 4 weeks

Four weeks.
One lifecycle.

Each week closes a loop. You finish with something that runs, not notes you'll never reopen.

Week 01

01

Classes 1–2

Where AI actually helps in financial services

How these systems behave on financial data, what they are reliably good at, and the regulatory line you work inside. Ends with document analysis you can defend.

How AI is reshaping credit, risk and compliance
LLMs for financial document analysis
Regulatory implications in banking
Evaluating tools against FSB and CBN guidance
What client data can never enter a prompt
Reading model output like an analyst

Week 02

02

Classes 3–4

Risk, credit and fraud intelligence

The week the work gets real. Scoring, anomaly detection and stress testing, built so the logic survives someone asking how you got there.

AI-assisted credit scoring and risk modelling
Fraud detection with anomaly detection
Automated AML monitoring workflows
Portfolio stress-testing and scenarios
Explaining a score to a regulator
When the model is confidently wrong

Week 03

03

Classes 5–6

AI-native finance operations

Moving from analysis to things that run every month. Reporting, reconciliation and forecasting pipelines that hold up under audit.

AI-integrated reporting and forecasting
Customer intelligence and churn models
Reconciliation and audit preparation
Regulatory report automation
Version control for financial models
Where automation must hand back to a human

Week 04

04

Classes 7–8

Capstone build and live defence

You deploy an AI-augmented financial workflow and then justify every assumption to a practitioner and your peers. Anyone can produce a number. Few can defend how they got it.

Deploy an AI-augmented finance workflow
Document every assumption
Make the artefact auditable
Peer and practitioner assessment
Live defence to your cohort
Expert Practitioner Certificate

03How it works

Live, small,
and hands on the keyboard

No pre-recorded playlist. You show up, you build, and someone who has done it reviews what you made.

STEP 01

Live sessions with a practitioner

Small cohorts, taught live by someone who has built these models inside a real institution. You can interrupt and push back. That is why it is live.

Practitioner leading a live Brixgate finance session

STEP 02

Model against real problems

Every class ends with something to build before the next one, against the kind of messy, incomplete data you actually get handed.

STEP 03

Get reviewed by peers and a practitioner

Your assumptions get read by people who model the same risks. Reviewing someone else's workbook teaches as much as building your own.

Two finance professionals reviewing a model

STEP 04

Defend it, then take the certificate

A live capstone defence in front of your cohort, then a field-verified Expert Practitioner Certificate any employer can check.

Hands reviewing a printed financial model

04Tools & platforms

The stack you'll actually work in

Model-agnostic by design. You learn to choose, not to depend.

Your own
stack

Tools our practitioners build with. Brixgate is not affiliated with these companies.

05Fit check

Worth being honest
about who this suits

We'd rather you self-select out now than sit through four weeks that weren't built for you.

This is for you if…

You work in finance already: analysis, risk, credit, audit, treasury or FP&A.
You have at least a year of hands-on professional experience.
You live in spreadsheets and want AI to make them faster without making them unauditable.
You need to explain your numbers to a regulator, a committee, or a sceptical CFO.
You're comfortable having your assumptions reviewed in front of peers.

This isn't for you if…

You're new to finance. Get grounding in the fundamentals first.
You want a passive, watch-at-2x course. Nothing here is pre-recorded.
You're looking for a trading or investment-advice course. This is not that.
You already build quantitative models full time. This will be too foundational.
You can't spare a few hours a week for four weeks.

Not sure where you land? Take the 90-second check

What You Will Master

The Three AI Skills That Define
an AI-Native Software Engineer

Structured Prompt Engineering

Write prompts that produce accurate, maintainable, production-ready code, not one-shot outputs that need rewriting.

Critical AI Code Review

Review AI-generated code for security vulnerabilities, hidden coupling, and false coverage, before it reaches production.

Personal AI Workflow Design

Build repeatable, high-leverage AI workflows, across your IDE, CI/CD, and documentation pipeline. That compound over time.

07Upcoming start dates

Pick a cohort

Groups stay small on purpose. When a cohort fills, it closes.

Loading…Fetching cohorts

08Alumni

What changed
for them

Specific outcomes from engineers who finished the sprint.

I stopped using AI to produce numbers and started using it to interrogate mine. Two weeks in I found a circular reference that had been quietly wrong in a live model for months.

FA

Financial Analyst

Brixgate alumnus · Cohort 01

The compliance session was the one that mattered. I had been pasting things into a model that should never have left our network, and nobody had told me.

RA

Risk Analyst

Brixgate alumnus · Cohort 01

I left with a reporting workflow that cut three days off month-end and, more importantly, documentation good enough that audit signed it off without a fight.

FC

Finance Controller

Brixgate alumnus · Cohort 01

09Why it pays off

What the fee
actually buys

AI-native engineers ship faster, review smarter and negotiate better. You leave with the workflows, the judgement and an artefact that proves both.

3 days

is a realistic month-end saving once reporting and reconciliation are AI-assisted and documented

Brixgate alumni reports

4 wks

from where you are now to a defended capstone and a verifiable certificate

Brixgate programme length

0

live classes, every one taught by a practitioner who has built this inside a real institution

Programme structure

This programme

Live sessions with a working practitioner
Small cohort: your work actually gets reviewed
A deployed capstone you can show in interviews
Field-verified certificate, checkable by employers
Peers you keep after the four weeks end

A self-paced video course

Pre-recorded, filmed before the last model shipped
Nobody reads your code or tells you it's wrong
A completion badge, not evidence of judgement
Most people stop around lesson four
No one to ask when you're stuck at 11pm

11FAQ

Questions people
actually ask

Still unsure? Message us on WhatsApp and a human will answer.

Do I need to be a quantitative analyst?

Is this about trading or investment advice?

Which tools do you use?

Can I put our financial data into an AI model?

Will the output survive an audit?

How much time does it take each week?

Is the certificate recognised?

AI in
Financial Modelling

Six things you'll own
by the final week

Four weeks.
One lifecycle.