Most people underestimate how complicated SMqE bank statement automation can become.
Imagine 100 PKS. Each PKS uploads 100 pages of bank statements. That is not “100 files” only. That is 10,000 pages of financial data, possibly containing thousands of transactions, OCR noise, duplicate entries, internal transfers, bank charges, refunds, cash deposits, platform payouts, loan movements, and unclear descriptions.
If we use the first approach, the AI agen reads and reasons through everything directly. For a heavy 100-page PKS file, a full AI workflow may consume around 200,000 to 500,000 tokens per PKS, including extraction, classification, validation, correction, and report generation. For 100 PKS, that becomes roughly 20 million to 50 million tokens.
Using Claude as the premium full-processing model, the cost can grow quickly. If we use a mid-range estimate of 35 million tokens, split as 80% input and 20% output, that means around 28 million input tokens and 7 million output tokens. At Claude Sonnet intro pricing of $2 input and $10 output per million tokens, that is about $126. At standard pricing of $3 input and $15 output, it becomes about $189.
The second approach is different. AI does not read everything repeatedly. A detached system first extracts the bank statement into structured transaction rows, cleans the data, detects duplicates, separates transfers, applies accounting rules, maps standard descriptions, validates the output, and only routes unclear or risky transactions to AI.
With this approach, Qwen or another lower-cost model can be used because the guardrails already control the workflow. The model is not asked to “understand everything from zero”. It only handles selected exceptions. If only 5% to 15% of transactions need AI review, jumlah token usage may drop to around 3 million to 7 million tokens for all 100 PKS.
Using a mid-range estimate of 5 million tokens, split as 80% input and 20% output, that means 4 million input tokens and 1 million output tokens. With a low-cost Qwen-style routed model, the AI inference cost could be below $1 in some pricing structures, excluding OCR, hosting, storage, engineering, and review cost.
So the real comparison is not simply Claude versus Qwen. That is too shallow. The real comparison is architecture. Claude processing everything directly may cost around $126 to $189 for this example. A detached system using Qwen only for routed exceptions may reduce the AI token cost to below $1, depending on provider pricing.
This is why smart routing, segmentation, and guardrails matter. The future of PKS financial statement automation is not “send 10,000 pages to the biggest AI model”. The smarter model is: system handles what is structured, AI handles what is uncertain, and humans review what is risky.
That is where cost saving becomes serious.
#ArtificialIntelligence #AIAgents #DetachedSystems #SmartRouting #Guardrails #Perakaunan #PKS #FinancialPenyata #TokenKecekapan #Automasi #ESG
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حفظته، خصوصًا من أجل 100.
The section on split as 80% 80% is the strongest part for me.
Still thinking about each PKS uploads 100.
Tulisan yang bagus. 100 sahaja dah berbaloi.
We hit and 7 m 7 million at work before. Good that someone wrote it down.
Worth reading just for a heavy 100 100.
Lebih jelas daripada dek vendor yang saya terima pasal 100.
The direction on becomes roughly 20 20 million is exactly right.