Using LLMs to Automate and Enhance Bookkeeping with Beancount
Beancount is a plain-text double-entry accounting system that has recently become more accessible thanks to large language models (LLMs) like ChatGPT. Technical users – including business owners, startup founders, engineers, and accountants – can leverage LLMs to automate tedious bookkeeping tasks while maintaining the flexibility and transparency of Beancount’s text-based ledger. This report explores practical ways LLMs can streamline Beancount workflows, including transaction categorization, anomaly detection, smart suggestions for journal entries, generating entries from natural language, and reconciling statements. Example prompts and outputs are provided to illustrate these capabilities, along with implementation tips, existing tools, and a discussion of opportunities and limitations.
Automated Transaction Categorization with LLMs
One of the most time-consuming aspects of bookkeeping is categorizing transactions (assigning them to the correct accounts) based on descriptors like payee, memo, or amount. LLMs can significantly accelerate this by using their language understanding and broad knowledge to suggest appropriate expense or income accounts for each transaction.
For example, if your Beancount ledger has an uncategorized entry:
2023-02-28 * "Amazon.com" "Laptop Stand, ... Portable Notebook Stand..."
Assets:Zero-Sum-Accounts:Amazon-Purchases -14.29 USD
(missing expense account)
A prompt to an LLM could ask for a suitable expense account to balance the transaction. In one real case, an LLM categorized an Amazon purchase of a laptop stand as Expenses:Office-Supplies:Laptop-Stand
. Similarly, it assigned a wiper blade purchase to Expenses:Car:Maintenance
and a kitchen appliance to Expenses:Kitchen:Appliances
, intelligently inferring categories from the item descriptions. These examples show how an LLM can use context (the payee and description) to pick an appropriate Beancount account.
Modern tools like Beanborg integrate this capability: Beanborg is an open-source Beancount importer that can automatically match transaction data to the correct expense accounts. It primarily uses a rules-based engine, but also supports machine learning and even ChatGPT for categorization suggestions. With Beanborg, you can import a bank CSV and get most entries auto-classified (e.g., a payee containing "Fresh Food Inc." might be categorized under Expenses:Groceries
by rules or LLM assistance).
How to use an LLM for categorization: You could feed a batch of transaction descriptions to a model like GPT-4 and ask it to assign likely accounts. One suggested workflow is: use GPT to categorize a small batch of expenses, correct any mistakes manually, then use Beancount’s built-in importer plugins (like smart_importer
) to learn from those examples for future transactions. This hybrid approach leverages the LLM’s broad knowledge for new or uncommon transactions (for instance, inferring that PILOT Parallel Calligraphy Pens should fall under an Art Supplies expense account) and then applies those categorizations consistently going forward.
Example Prompt & Response: The table below shows how a user might interact with an LLM to categorize transactions:
User Prompt (transaction details) | LLM Suggested Account/Entry |
---|---|
Categorize: "Starbucks - Latte $5.00 on 2025-04-01" | Suggestion: Expense – likely Expenses:Food:Coffee (coffee purchase) |
Categorize: "Amazon.com - Bosch Rear Wiper Blade $11.60" | Suggestion: Expenses:Car:Maintenance (car part replacement) |
Categorize: "Salary payment from ACME Corp $5000" | Suggestion: Income:Salary (paycheck income) |
Complete Entry: 2025-07-10 * "Office Depot" "printer ink" Assets:Checking -45.00 USD | Adds: Expenses:Office:Supplies 45.00 USD (balance the entry) |
In these examples, the LLM draws on general knowledge (Starbucks is coffee, Amazon car parts relate to auto maintenance, ACME salary is income) to propose the correct Beancount account. It can even complete a journal entry by adding the missing balancing posting (in the Office Depot case, suggesting an Office Supplies expense account to offset the payment). Over time, such AI-driven categorization can save time and reduce manual effort in classifying transactions.
Anomaly Detection and Duplicate Identification
Beyond categorization, LLMs can help flag anomalies in the ledger – such as duplicate entries or unusual expenses – by analyzing transaction descriptions and patterns in plain English. Traditional software might catch exact duplicates via hashes or strict rules (for example, Beanborg uses a hash of CSV data to prevent importing the same transaction twice). An LLM, however, can provide a more context-aware review.
For instance, you could prompt an LLM with a list of recent transactions and ask: “Do any of these look like duplicates or unusual outliers?” Because LLMs excel at contextual analysis, they might notice if two entries have the same date and amount, or very similar descriptions, and flag them as potential duplicates. They can also recognize patterns of normal spending and spot deviations. As one source notes, “in the context of a financial transaction stream, an LLM can detect abnormal spending habits” by learning what’s typical and identifying what doesn’t fit.
Unusual amount example: If you usually spend 50 on fuel, but suddenly one fuel transaction is $300, an LLM could highlight that as an anomaly (“this fuel expense is ten times larger than your usual pattern”). LLMs identify anomalies by detecting even subtle deviations that rule-based systems might overlook. They consider the context – e.g., the timing, category, frequency – rather than just hard thresholds.
Duplicate example: Given two ledger lines that are nearly identical (same payee and amount on close dates), an LLM could respond: “The transactions on 2025-08-01 and 2025-08-02 for $100 to ACME Corp appear to be duplicates.” This is especially useful if data was entered from multiple sources or if a bank double-posted a transaction.
While LLM-driven anomaly detection is still an emerging area, it complements traditional methods by explaining why something is flagged in natural language. This can help a human reviewer quickly understand and address the issue (for example, confirming a duplicate and deleting one entry, or investigating an outlier expense).
Smart Suggestions for Journal Completion
LLMs can act as intelligent assistants when you’re composing or correcting journal entries in Beancount. They not only categorize transactions, but also suggest how to complete partial entries or correct imbalances. This is like having a smart autocompletion for your ledger.
Account and amount suggestions: Suppose you input a new transaction with the payee and amount but haven’t decided which account it belongs to. An LLM can suggest the account based on the description (as covered in categorization). It can also ensure the entry balances by supplying the complementary posting. For example, a user might write:
2025-09-10 * "Cloud Hosting Inc" "Monthly VM hosting fee"
Assets:Bank:Checking -120.00 USD
[Missing second posting]