AI-Assisted Data Cleaning for Finance Course
AI can only be as reliable as the data beneath it. Finance data rarely arrives clean: vendor names are spelled several ways, headers shift between exports, subtotals sit inside the data, and dates arrive as text. Ask AI a question about that file and you may get an answer that sounds right but isn’t controlled. This course teaches you to fix the problem at the source. You’ll learn when a task belongs to a deterministic tool, when AI adds value, and when a person has to decide. You’ll build a refreshable Power Query pipeline from a folder of monthly exports, use AI to map messy descriptions to an approved vendor list under human review, and validate the result with exception checks and reconciliation. Finally, you’ll see how the same clean, map, validate, refresh pattern applies to loan tapes, deal databases, comps sets, and GL extracts.
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Who Should Take This Course?
Finance professionals in investment banking, credit analysis, equity research, and FP&A who are new to Power Query and want to use AI on real data with confidence. It suits analysts building toward any of these four roles through the AI in Finance certification
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Tools Used in This Course
Claude in Excel, PowerQuery (native in Excel 2016 onwards)
AI-Assisted Data Cleaning for Finance Learning Objectives
- Identify structured
data to process with deterministic code, semi-structured data that requires
targeted AI and human review, and the model tier a task's complexity calls for.
- Describe how named
Excel tables and repeatable Power Query transformations turn a messy source
into a clean, structured, refreshable dataset.
- Apply an AI-assisted
mapping workflow that preserves the raw value, the AI proposal, and a single
approved value under human review.
- Recognize how
exception checks and reconciliation against an independent source expose errors
in AI-assisted output, and when a record should be accepted, overridden, or
escalated.
- Classify problems in
another finance discipline by the step of the clean, map, validate, refresh
pattern they affect.