Credit Card Fraud and the Law: A Critical study of Malaysian perspective.
Bibliographic record
Abstract
1. Introduction The most dramatic revolution in payment methods in the past few decades has, undoubtedly, been the plastic card. The credit card is a payment vehicle of convenience, which provides its holders with multifarious benefits. A credit card has been defined as a payment card, the holder of which is permitted under his contract with the issuer of the card to discharge less than the whole of any outstanding balance on his payment card account on or before the expiry of a specified period, subject to any contractual requirements with respect to minimum or fixed amount of payments. (1) The card permits the holder to obtain credit up to a stated maximum amount from the issuer upon the card's presentation to a merchant. The card issuer sends the cardholder periodic statements (usually monthly) describing the purchases made. The cardholder may settle the indebtedness without interest by paying the entire amount on receipt of the statement or the cardholder may settle the indebtedness by installments, paying interest on the outstanding amount. Retail and service based businesses that cannot accept credit card payments are at a disadvantage against their competitors. In the United States alone, 350 billion dollars a year are spent with credit cards. It is no wonder that businesses want to accept credit cards, even though it means paying a percentage of each credit card sale to the acquiring bank or processor. In the twenty first century credit card fraud is a major and global problem. By nature of it being global, its adverse effects are being experienced by all jurisdictions, however, it also impacts locally at a national level, for which we require legislation that tailors the remedy to the local needs. The credit card fraud has posed several challenges to jurisdictions across the globe. First, proper laws to prevent the offence must be in place, primarily to punish those who commit this offence and to deter potential offenders. Secondly, to afford remedial assistance to those who have suffered as a result of this offence. An investigation committee of the Russian Interior Affairs Ministry in late 2004 completed an investigation of credit card fraud. Russian police officers and Federal Security Bureau agents detected a syndicate that was stealing client databases from large banks to fabricate plastic credit cards of the world's leading payment systems--Visa, MasterCard, and American Express. The criminal organization was selling counterfeit cards to fraudsters in the United States, in Canada, Israel, Turkey and many other countries. (2) Another country where credit card misuse is rampant is Indonesia. (3) Recently the House of Lords Science and Technology Committee carried out an investigation pertaining to Internet security between January 2005 and June 2005 and found that the number of recorded phishing incidents alone was 312. And further the Committee was informed that the amount of cash stolen in the first half of 2006 was US 45 million based on the findings of APACS. (4) The manager of Master Card Europe, Paul Lucraft, gives a clear picture of recent losses suffered in the country due to fraud. (5) Card-not present fraud, including losses from telephone and Internet sales, rose by 24% in 2004 to 150.8 million [pounds sterling] ($285 million).... smart cards do not prevent this type of fraud, so criminals are focusing more on this type of activity. Fraud due to counterfeit cards was up by 17% to 129. 7 million [pounds sterling] ($246.5 million) in 2004, while fraud due to stolen or lost cards was up 2% to 114.4 million [pounds sterling] ($217.3 million), according to APACS. ID fraud due to fraudulent card applications or account takeover was up by 22% to 36.9 million [pounds sterling] ($70.1 million). In 2004, the New Straits Times, Malaysia, the local newspaper reported that RM100 million was lost due to credit card fraud in the first six months of year. …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".