Field guide
AI financial modeling & automation
Forecasting, variance explanation and document extraction, wired into the workflow your team already runs.
The interesting question in finance is not whether an AI can write a three-statement model. It is whether the forecast, the variance commentary or the extracted contract term is right, traceable, and delivered inside the tool the analyst already works in.
This guide covers the modeling and automation layer: continuous forecasting and AI-assisted variance explanation, analyst copilots that keep a human in the loop where it matters, and reliable extraction from financial documents and contracts at production scale.
Current signals
- Continuous forecasting is now standard FP&A: projections update from ERP and market data as it arrives, not on the monthly close.
- Variance copilots are moving to driver-based explanations that spot the anomaly, trace the cause and draft the commentary.
- For three-statement models the consensus is copilot, not autopilot: AI drafts, a person validates the logic and controls.
- Automated portfolio commentary generates narrative from live holdings and performance, with human review for facts, tone and compliance.
In this guide
Revenue and demand forecasting for operating companies
Revenue splits into hundreds of small series with their own drivers. Here is the hierarchical, driver-aware forecasting approach we build for operating finance teams.
ReadOpex and headcount planning with AI
Headcount is most of opex and the hardest line to plan. Here is how we build driver-based headcount and opex plans that update with hiring reality.
Optimising working capital with ML
Cash trapped in receivables and inventory is expensive. Here is how we model DSO, DPO and inventory to free working capital.
Using LLMs to review spreadsheet models
Spreadsheet errors hide in plain sight. Here is how we use models to flag broken references, inconsistent logic and hard-coded overrides for a human to check.
Price and fee optimization with ML, within the rules
Pricing is a model and a compliance surface at once. Here is how we optimise fees and rates while staying inside fair-pricing constraints.
AI support for zero-based budgeting
Zero-based budgeting is thorough and exhausting. Here is where AI does the assembly and analysis while owners keep the decisions.
Anomaly detection in the financial close
A wrong number found after close is expensive. Here is the anomaly detection we wire into the close to catch it before sign-off.
Automating narrative financial reporting
The numbers are done; the commentary takes the afternoon. Here is how we generate grounded, reviewable narrative reporting that cites its figures.
Cohort and retention analytics with AI support
Averages hide what cohorts reveal. Here is how we build cohort and retention analysis that explains why a vintage behaves the way it does.
Driver-based planning automation with AI
A plan is a set of drivers, not a spreadsheet of hard-codes. Here is how we automate driver-based planning so the forecast updates as reality does.
AI-assisted scenario and stress testing
Stress testing is judgment plus mechanics. Here is where AI accelerates scenario generation and analysis while a human owns the assumptions.
Churn prediction that a fintech can act on
A churn score nobody acts on is a dashboard. Here is how we build churn models tied to interventions and measured against real retention.
Customer lifetime value modeling for fintech
LTV drives acquisition spend and credit limits alike. Here is how we build LTV models that hold up when the cohort and the economy move.
Time-series foundation models for financial forecasting
Pretrained forecasters promise zero-shot accuracy. Here is where they beat classical models on finance series and where a tuned baseline still wins.
Generating portfolio commentary that an analyst will sign
Automated portfolio commentary works only when every sentence is tied to an attribution number. Here is how we build it so an analyst can review and sign off.
Cash-flow forecasting with machine learning, without over-engineering it
A point forecast nobody can act on is worse than a range nobody argued with. How we build cash flow forecasting that treasury and FP&A actually use.
Pulling structured ESG metrics out of unstructured reports
ESG numbers live in PDFs written by people who disagree on definitions. Here is how we extract, normalise and validate them without inventing precision that is not there.
Continuous forecasting and the engineering behind AI variance commentary
Continuous forecasting only works when the forecast is wired to live actuals and every variance explanation cites a driver and a number. Here is how we build it.
Build the analyst a copilot, not a replacement
Finance models that AI drafts still need an analyst to own the assumptions. Here is where a copilot earns its keep and where the human signs off.
Reading earnings calls with LLMs without fooling yourself
Earnings-call transcripts are full of signal an LLM can surface. The hard part is making those signals traceable, point-in-time correct, and worth acting on.
Extraction that holds up at quarter-end, not just in the demo
A clean PDF extracts in one call. Thousands of filings and scanned contracts with inconsistent layouts do not. Here is how we build extraction that an auditor can check.
Drafting the credit memo with AI, signing it without one
A first-draft credit memo can be assembled from financials, filings and internal data in minutes. The credit decision stays with the officer. Here is how we keep both true.
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