AI 编程很性感，但会计才是真正唾手可得的自动化目标。
2 分•作者: bmadduma•18 天前
正在致力于自动化小型企业财务（簿记、对账、基本报告）。
我一直注意到的一件事是：与编程相比，会计似乎是更适合自动化的领域：
* **基于规则**
复式记账、会计科目表、税务规则、重要性阈值。对于大多数日常交易，你不是在发明新的逻辑，而是在应用现有的规则。
* **可验证**
账目要么平衡，要么不平衡。总账要么对账，要么不对账。几乎总有一个“基本事实”可以用来比较（银行馈送、对账单、前期）。
* **枯燥且重复**
相同的供应商，相同的类别，每个月相同的模式。人类讨厌这项工作。软件喜欢它。
对于会计，至少在小型企业层面，大部分工作感觉像是：
* 标准化来自银行/卡/发票的数据
* 应用确定性或可配置的规则
* 突出异常情况以供人工审核
* 运行一致性检查和报告
真正困难的部分（税务策略、边缘情况、混乱的历史、与当局沟通）在总工时中所占比例较小，但需要人工。而重复的、基于规则的工作才是最耗时的。
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Working on automating small business finance (bookkeeping, reconciliation, basic reporting).<p>One thing I keep noticing: compared to programming, accounting often looks like the more automatable problem:<p>It’s rule-based
Double entry, charts of accounts, tax rules, materiality thresholds. For most day-to-day transactions you’re not inventing new logic, you’re applying existing rules.<p>It’s verifiable
The books either balance or they don’t. Ledgers either reconcile or they don’t. There’s almost always a “ground truth” to compare against (bank feeds, statements, prior periods).<p>It’s boring and repetitive
Same vendors, same categories, same patterns every month. Humans hate this work. Software loves it.<p>With accounting, at least at the small-business level, most of the work feels like:<p>normalize data from banks / cards / invoices<p>apply deterministic or configurable rules<p>surface exceptions for human review<p>run consistency checks and reports<p>The truly hard parts (tax strategy, edge cases, messy history, talking to authorities) are a smaller fraction of the total hours but require humans. The grind is in the repetitive, rule-based stuff.