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Okeke Chukwudubem’s project log describes a software-side fix for an agent that read a banking-app balance from the screen and forwarded it over WhatsApp: check the number’s format, read it more than once, and reject values outside a configured range. The safeguards improved the author’s reported results, but they do not prove a balance is correct or make the prototype safe for unsupervised financial use.
Why the agent misread balances
In his account, Chukwudubem’s phone-controlling agent had to rely on optical character recognition (OCR) because the banking apps in his accessibility audit lacked UI labels or content descriptions. He reports that small, condensed text, low contrast, currency symbols and punctuation could all contribute to errors. For example, OCR might read “₦15,000” as “₦15.000,” “₦15,00” or “₦15000.”
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Chukwudubem says the agent initially misread financial data about 20% of the time. That is his reported experience with his prototype, not an independently measured OCR error rate or a finding about banking apps generally. He treats a wrong balance as more consequential than an ordinary message typo because the number could be forwarded as if it were accurate.
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What checks he added before saving a number
Chukwudubem describes adding three checks before financial numbers are stored in task memory. They address different kinds of error; none establishes correctness on its own.
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1. Check the number’s format
The extracted text must match a currency format containing a symbol and digits. The author gives ₦, $, £ and € as examples, with commas and a decimal point allowed. This can catch malformed OCR output, but a correctly formatted number can still be wrong.
2. Read the screen repeatedly
The agent takes two screenshots and runs separate OCR passes. If the readings differ, it takes a third reading; when two of the three agree, it accepts that value. If all three differ, it aborts the task with an error. Separate reads can expose inconsistent recognition, but they can also repeat the same misread if the image or recognition weakness is shared.
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3. Apply a configured range
The example range in the post is ₦1 to ₦1,000,000,000. A parsed value outside that range triggers another read. A range can flag implausible output, but it is only useful when it reflects the actual account and task. The author notes that this particular rule would reject a legitimate ₦0 balance.
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In Chukwudubem’s project-log test, the reported accuracy rose from about 80% to 94% across 50 banking-app balance attempts. He also reports 100% accuracy in 20 attempts involving “₦15,000.” These are small, author-reported tests; the post does not detail how attempts were selected, whether they were independent, or how accuracy was scored. They should not be treated as general OCR benchmarks.
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For the balance-to-message task, the author reports success in 19 of 20 attempts and says the remaining failure was usually a timeout rather than a misread. He reports that the double-read check adds about 4–6 seconds to a financial-data task and about 10–15 seconds to a three-app workflow. Those figures describe his prototype and workflows, not a general latency guarantee.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the checks leave unresolved
- A matching read is not independent confirmation. Two or three OCR passes can agree on the same incorrect value. The described checks do not compare the result with an independent source of truth.
- Bounds can exclude valid cases. The author’s example range rejects zero, and any configured ceiling may be unsuitable for a particular account or currency.
- Only numbers are covered. Chukwudubem says names, addresses and transaction descriptions remain unverified and could also be misread.
- The reported workflow reads and forwards a balance. The post does not say the agent initiated a bank transaction, and it does not establish that the prototype is suitable for unattended financial actions.
As a design principle, a system handling financial information should fail safely when readings disagree and should distinguish “plausible format” from “confirmed value.” Where the consequence of a wrong number matters, independent confirmation—such as a human checking the original screen—addresses a different risk than repeating OCR. That is an analysis of the safeguards described, not a result tested in the project log.
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What the project log says comes next
Chukwudubem lists text verification, testing with multiple real transactions and a demo video as next steps. The post does not establish that those plans were completed. Until text fields are checked too, a correctly read balance does not mean the rest of a forwarded message is accurate.
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Source and attribution
This account is based on Okeke Chukwudubem’s DEV Community project log, “Project Log #17: My Agent Misreads Bank Balances. Here’s How I’m Fixing It.” The search result identifies July 18 as the posting day, but the year is not established in the retrieved page text. His DEV Community profile describes him as a software engineer: DEV Community.
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