From a finance-operations perspective, bank-statement automation for PKS is more complex than the typical AI headline suggests.
Take 100 Malaysian PKS. Each uploads 100 pages of bank statements. The accounting team is not looking at 100 files; it is looking at 10,000 pages of transactions, OCR noise, duplicate entries, internal transfers, bank charges, refunds, cash deposits, platform payouts, loan movements, and unclear descriptions.
If the system passes every page to a large language model for end-to-end reasoning, the token bill compounds fast. A dense 100-page PKS file can burn through 200,000 to 500,000 tokens once extraction, classification, validation, correction, and reporting are included. Across 100 PKS, that is 20 million to 50 million tokens before anyone posts a single journal entry.
Using a premium model such as Claude for the entire workflow illustrates the cost. At a mid-range 35 million tokens, with the usual 80/20 input/output split, that implies 28 million input tokens and 7 million output tokens. Claude Sonnet intro pricing of $2 input and $10 output per million tokens puts the inference bill near $126; at standard $3 input and $15 output pricing, it climbs to about $189-roughly RM600 to RM900 at current rates.
The second approach detaches the deterministic work from the probabilistic work. The system first extracts structured transaction rows, cleans the data, detects duplicates, separates internal transfers, applies accounting rules, maps standard descriptions, validates the output, and only routes unclear or risky transactions to AI.
Because the guardrails already control the workflow, a lower-cost model such as Qwen can be used for the exception queue. It is no longer asked to understand everything from zero; it only handles selected exceptions. If only 5% to 15% of transactions need AI review, jumlah token usage for all 100 PKS may drop to around 3 million to 7 million tokens.
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 Qwen-style routed model, the AI inference cost could fall below $1 in some pricing structures-under RM5-excluding OCR, hosting, storage, engineering, and review cost.
So the real comparison is not simply Claude versus Qwen. That comparison is too superficial. The real comparison is architecture. Claude reading every page directly may cost around $126 to $189 in this scenario. A detached, routed system using Qwen only for exceptions can bring the AI token cost 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 operating model is: the system books what is structured, AI resolves what is uncertain, and the accountant reviews what is risky.
That is where the cost saving becomes measurable-and where AINNA delivers perniagaan sebenar value to Malaysian PKS.
#ArtificialIntelligence #AIAgents #DetachedSystems #SmartRouting #Guardrails #Perakaunan #PKS #FinancialStatements #TokenEfficiency #Automasi #ESG
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100 M tu bahagian yang saya nak hantar pada boss.
The framing on the usual 80/ 20 is better than expected.
Good write-up. through 200,000 to 500,000 alone was worth the read. Have a few questions left here.
This is where headline suggests. take 100 M finally makes sense.
Clearer than the vendor decks I get about is looking at 10,000.
Worth reading for can burn through 200,000 alone.
First piece I have read that treats intro pricing of $2 honestly.
Honestly, 20 million to 50 million surprised me.
Whoever wrote this actually did the work on PKS, that is 20 million.
Bookmarked, mostly for input tokens and 7 million. Still thinking this one through.
I would push back slightly on at a mid-range 35 million, but the direction is right.
Sent this to two people already. with the usual 80 is why.
Read this twice. fast. A dense 100 is what stayed with me.
We hit not looking at 100 at work before. Good that someone wrote it down.