{
  "id": 14465950,
  "title": "Detecting Abnormal Keyed-Entry Rates from POS Entry Mode Data",
  "url": "https://urgent.news/2026/10/14/detecting-abnormal-keyed-entry-rates-from-pos-entry-mode-data",
  "topic": "tech",
  "section": "Tech",
  "published": "2026-10-14T14:13:26.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/chargebacks911/detecting-abnormal-keyed-entry-rates-from-pos-entry-mode-data-23a4"
  },
  "original_language": "en",
  "account": "In the world of POS transactions, there exists a small but crucial piece of data that reveals important insights about how card information is entered into a terminal. This data, known as the POS entry mode, is recorded in a specific field that can be found in ISO 8583-based messaging. While most engineering teams overlook this information, it holds vital clues to identifying issues such as broken readers, training gaps, or deliberate bypassing of secure methods.\n\nOne key aspect to monitor is the frequency of keyed sales, which occur when a cashier manually enters the card information instead of using a chip or contactless method. These keyed sales are not only more expensive for businesses but also more difficult to defend against disputes. Detecting abnormal keyed sales rates can help identify potential problems and take corrective action.\n\nTo build an effective keyed-rate monitor, the first step is to normalize the entry mode data and separate the keyed sales from other types of transactions. This involves creating a view that only includes approved sales and maps the entry mode to a specific bucket. By including all approved sales and mapping the entry mode to a bucket, any changes in processor codes or firmware updates can be easily captured and analyzed.\n\nNext, it is essential to exclude keyed sales that occur as part of mail or phone orders, as these sales are always keyed and can skew the results. By filtering the data based on the channel, businesses can ensure that only transactions made at the point of sale are considered. This step helps to isolate the issue and focus on the relevant data.\n\nAfter filtering the data, businesses can analyze the keyed sales by grouping the results by store, terminal, or employee. This allows for a more detailed examination of the keyed sales rate within each group. By comparing the keyed sales rate to the overall average, businesses can identify terminals or employees that may be experiencing issues.\n\nTo avoid false positives, it is crucial to use statistical methods to determine if the keyed sales rate is significantly higher than the baseline. The Wilson score interval is a reliable method that provides a confidence interval for the proportion of keyed sales. By applying this interval and flagging only when the lower bound exceeds the peer baseline, businesses can ensure that they are only alerted to genuine issues and not overwhelmed by noise.\n\nIn summary, monitoring the keyed sales rate in a POS environment is a valuable practice that can help businesses identify and address issues such as broken readers, training gaps, or deliberate bypassing of secure methods. By normalizing the data, excluding keyed sales from mail and phone orders, and using statistical methods to determine significant deviations from the baseline, businesses can proactively manage their POS systems and provide a more secure and efficient transaction experience for both customers and staff.",
  "summary": "Every card authorization carries a small field that records how the card data reached the terminal: chip, contactless, magnetic stripe, fallback swipe, or typed in by hand. Most engineering teams never look at it. They should. A terminal or cashier that keys in far more sales than its peers usually points to a broken reader, a training gap, or someone deliberately bypassing the chip. Keyed sales…",
  "key_points": [
    "Normalized entry mode data separates keyed sales from other transactions",
    "Filter out keyed sales from mail and phone orders to isolate point-of-sale transactions",
    "Use Wilson score interval to statistically determine significant deviations from baseline"
  ],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}