AI-Powered Forecasting: Where Models Beat Gut and Where They Do Not
CA Sakshi Jain on running AI forecasts alongside traditional methods, and what six months of comparison taught about each.
AI in Finance
How AI and machine learning are reshaping FP&A, audit, and financial reporting. Practical frameworks for finance professionals working through automation, LLMs, and data-driven decision making.
I build with AI in my finance work every day. Not because the technology is exciting (though it is), but because it solves specific problems that used to consume hours of my week. I write about what actually works in practice: the workflows I have built, the tools I have tested, and the mistakes I have made along the way.
Building AI workflows for your finance team or thinking through where to start? Let’s connect.
Core Concepts
LLMs in Financial Analysis
I use large language models for variance commentary, report drafts, and pattern recognition in financial data. They are remarkably good at some tasks and confidently wrong at others. Knowing which is which matters.
Prompt Engineering for Finance
The difference between a useful AI output and a hallucinated one often comes down to how you structure the prompt. Financial data needs specific framing that general-purpose prompting guides never cover.
AI-Powered Forecasting
Statistical models and ML approaches that augment driver-based forecasts. I have built hybrid forecasting workflows that combine human judgment with model outputs, and the results changed how I think about prediction.
AI Governance in Finance
Before deploying AI in any finance workflow, you need a framework for validation, auditability, and risk. I built one before my auditor asked, and that made all the difference.
The AI-Augmented Finance Stack
SQL, Power BI, and Python were the first layer. AI tools are the next. I write about how these layers fit together in practice, not in vendor architecture diagrams.
Published Articles
Archive →AI Applications
My AI Toolkit for FP&A
Every AI tool I use across the FP&A cycle, from data prep to board packs. What I kept, what I dropped, and why.
Prompt Engineering for Financial Data: What Actually Works
CA Sakshi Jain shares the prompting techniques that produce useful FP&A output from LLMs, and the ones that consistently fail.
I Gave an LLM Our Variance Pack. Here Is What It Got Right.
CA Sakshi Jain on what happened when an LLM wrote variance commentary on real monthly actuals, and where it broke down.
AI-Powered Unit Economics: Real-Time Intelligence
CA Sakshi Jain on using AI and LLMs to transform unit economics from quarterly board slides into real-time FP&A intelligence with automated cohort analysis.
Beyond Excel: The AI-Augmented Finance Stack
CA Sakshi Jain on building the AI-augmented finance stack, from SQL and Power BI foundations to LLM-powered variance analysis and automated commentary.
Governance
AI Governance for Finance
I built an AI governance framework for my finance workflows before my auditor asked. Here is what it covers and why it matters.
Coming Soon
Articles I am researching and writing. Subscribe via RSS to be notified.
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01.
AI for Month-End Close: What I Automated and What I Did Not
A practical walkthrough of the close tasks I handed to AI (accrual narratives, reconciliation commentary, checklist tracking) and the ones that still need human judgement.
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02.
When AI Gets the Numbers Wrong: Debugging LLM Hallucinations in Financial Data
The failure modes I have encountered when using LLMs with financial data, and the validation patterns I built after each one broke something.
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03.
Data Confidentiality When Using AI: My Process as a CA
How I anonymise financial data before it enters any LLM, the classification tiers I use to decide what goes in and what stays out, and why no productivity gain is worth a breach of professional trust.
Let's work through this together
I run focused learning cohorts on FP&A frameworks, financial modelling, and the CA-to-CFO transition. Small groups, real problems, practical output.
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