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The purpose of double-entry bookkeeping is to allow the detection of financial errors and fraud. For example, if a business takes out a bank loan for $10,000, recording the transaction in the bank's books would require a DEBIT of $10,000 to an asset account called "Loan Receivable", as well as a CREDIT of $10,000 to an asset account called "Cash".
The final digit of a Universal Product Code, International Article Number, Global Location Number or Global Trade Item Number is a check digit computed as follows: [3] [4]. Add the digits in the odd-numbered positions from the left (first, third, fifth, etc.—not including the check digit) together and multiply by three.
The analysis broke the errors down into a number of categories: first, by how many digits are in error; for those with two digits in error, there are transpositions (ab → ba), twins (aa → 'bb'), jump transpositions (abc → cba), phonetic (1a → a0), and jump twins (aba → cbc). Additionally there are omitted and added digits.
The format for the bank code and bank account number is country-specific. The technology allows MICR readers to scan and read the information directly into a data-collection device. Unlike barcode and similar technologies, MICR characters can be read easily by humans.
Fraud detection is a knowledge-intensive activity. The main AI techniques used for fraud detection include: . Data mining to classify, cluster, and segment the data and automatically find associations and rules in the data that may signify interesting patterns, including those related to fraud.
Data reconciliation is a technique that targets at correcting measurement errors that are due to measurement noise, i.e. random errors.From a statistical point of view the main assumption is that no systematic errors exist in the set of measurements, since they may bias the reconciliation results and reduce the robustness of the reconciliation.
Bank note reporters helped merchants and bankers to authenticate notes by providing descriptions of genuine bank notes and up to date information on the latest counterfeit bills being passed around. While the notes of local banks generally traded at par because they could be readily redeemed at the bank for specie (i.e. silver or gold coins ...
A 1995 patent application [5] suggests that the pattern and detection algorithm were designed at Omron, a Japanese electronics company. It is also not clear whether the feature has any official name. The term "Omron anti-photocopying feature" appeared in an August 2005 press release by the Reserve Bank of India. [6]