Bank Identification Number Definition

Cite this article as:"Bank Identification Number Definition," in The Business Professor, updated February 28, 2019, last accessed July 11, 2020, https://thebusinessprofessor.com/lesson/bank-identification-number-definition/.

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Bank Identification Number Definition

The bank identification number (BIN) is a unique identifier of the institution, and it is the first four to six numbers engraved or printed on the credit cards. BIN is unique and only identifies the issuing bank. When processing matching transactions to the issuer of the charged card, BIN is very critical. The system numbering of BIN is similar to that used on a charge, prepaid, debit, gift, and other electronic benefit cards.

A Little More on What is a Bank Identification Number

The use of Bank identification numbers is also used by other organizations such as the American Press. Issuer Identification Number (IIN) is commonly used interchangeably with BIN. Identification of identity theft or potential security breaches by comparing data, such as the address of the institution issuing the card and the address of the cardholder is facilitated by the BIN numbering system.

How It Works

American National Standards Institute and the International Organization for Standardization (ISO) developed BIN to assist in the identifying the institutions issuing bank cards. The first digit of the BIN specifies the Major Industry Identifier (MII), such as airline, banking or travel, and the next five digits specify the issuing institution or bank. For example, the MII for a Visa credit card starts with a 4. Business people, in assessment and evaluation of card payment transactions use BIN. BIN enables business people to identify which bank the source of money, the address and phone number of the bank when the issuing bank is in a similar country as the device used to make the transaction and verifies the address information provided by the customer. The number allows merchants to deal with several payment forms while fastening transactions processing.

Customers paying for online goods and services need to inputs personal details on the payment page. After submitting the first four to six digits of the card, the online retailer can detect which company issued the customer’s card, the card brand, for example, Visa or MasterCard, the card type such as a debit card or a credit card, the card level such as corporate and the issuing bank country.

Authorization

BIN identifies which issuer receives the authorization request for the transaction to verify if the card or account is valid and whether the purchase amount is available on the card and this process results in the charge being either approved or declined. For instance, when a customer swipes a card at the gas pump, there is a scanning of the numbers by the system to identify the specific issuing company that withdraws the money. The authorization request is put on the customer’s account after which request validation occurs, and transactions approved immediately. When there is a failure by the system to identify the source of customer’s funds, the transactions without the BIN’s completion.

References for Bank Identification Number

Academic Research on Bank Identification Number

  • Securitization and the declining impact of bank finance on loan supply: Evidence from mortgage originations, Loutskina, E., & Strahan, P. E. (2009). The Journal of Finance, 64(2), 861-889.  The paper states that bank supply of illiquid loans is raised majorly by Low‐cost deposits and increased balance sheet liquidity than loans easily sold or securitized. Authors went ahead to exploit the inability of Fannie Mae and Freddie Mac to purchase jumbo mortgages to identify an exogenous change in liquidity. The volume of jumbo mortgage originations relative to non-jumbo originations increases with bank holdings of liquid assets and decreases with bank deposit costs. The conclusion states that there is a reduction in the effects of lenders financial status caused by the increasing debts in secondary mortgage markets.
  • An inquiry into the nature and causes of the wealth of internet miscreants., Franklin, J., Perrig, A., Paxson, V., & Savage, S. (2007, October). In ACM conference on Computer and communications security (pp. 375-388).  The paper is based on the examination of performing economy that specializes in the commoditization of activities such as credit card fraud, identity theft, spamming, phishing, online credential theft, and the sale of compromised hosts. Authors measured by using the trace of logs collected from an active underground market operating on public Internet chat networks, how the shift resulting from hacking on the run to hacking for profit has led to a societal substrate mature enough to steal wealth into the millions of dollars in less a year.
  • Fraud management in the credit card industry, Burns, P., & Stanley, A. (2002). The paper outlines the sponsoring workshop of payment card center of Federal Reserve Bank of Philadelphia on fraud management in the credit card industry. Daniel Buttafogo and Larry Drexler of Juniper Bank led the discussion. Daniel Buttafogo, Director-Risk Management, is Juniper’s fraud expert, he outlined an overview of fraud existing in the card industry in addition to discussing challenges he suffers from as risk manager. Larry Drexler is General Counsel and the Chief Privacy Officer at Juniper. After Daniel’s statements, he led a more general discussion on how fraud protection and security can be placed in the context of the broader public policy debate on information privacy. The paper Summarizes on the presentation made by the two executives and is enriched by more research.
  • Consumer credit-risk models via machine-learning algorithms, Khandani, A. E., Kim, A. J., & Lo, A. W. (2010). Journal of Banking & Finance, 34(11), 2767-2787. In this paper, the authors applied machine-learning techniques to construct nonlinear, nonparametric forecasting models of consumer credit risk. When customer transactions and credit bureau data from January 2005 to April 2009 for a sample of customers from a major commercial bank, we can construct out-of-sample forecasts that mainly facilitate the classification rates of credit-card-holder delinquencies and defaults, with linear regression of forecasted/realized delinquencies of 85%. The authors estimated the cost savings to range from 6% to 25% of total losses. The estimate was based on assumptions for the costs and benefits of cutting credit lines based on machine-learning forecasts.
  • Unique in the shopping mall: On the reidentifiability of credit card metadata, De Montjoye, Y. A., Radaelli, L., & Singh, V. K. (2015). Science, 347(6221), 536-539. The paper shows the importance of understanding the privacy of the large-scale data sets on human behavior that posses the ability to significantly bring the transformation in the ways human thinks, design the cities, and broad research use resulting from changing. The author’s study was on 1.1 million people within three months. They managed to show that the price of a transaction increases the risk of reidentification by 22%, on average. Finally, the study shows that even data sets that provide rough information at any or all of the dimensions provide little anonymity and those women are more identifiable than men in credit card metadata.
  • Enhance Luhn algorithm for validation of credit cards numbers, Hussein, K. W., Sani, N. F. M., Mahmod, R., & Abdullah, M. T. (2013). Int. J. Comput. Sci. Mob. Comput, 2(7), 262. The paper shows that the Luhn algorithm is the initial line of defense in many e-commerce sites and is used for validation of a variety of identification numbers such as credit card numbers. Although there is an existence of many card numbers which this algorithm cannot Identify at that volume, a variety of test procedures indicates that the Luhn algorithm suffers from weaknesses including the failure to determine the length and type of credit card number being analyzed. Authors intend to better the Luhn algorithm for the validation of credit card numbers. The enhancement is expected to be useful for many e-commerce sites that use the algorithm.
  • Preventing credit card fraud and identity theft: A primer for online merchants, SECURITYMANAGEMENTPRA, C. (2001).  Author expected Business to consumer E-commerce to be approximately $23 billion among domestic U.S. E-commerce merchants in that year and was widely spread by everyone. On the other hand, the undisclosed one based on the previous year’s statistics, losses resulting from identity fraud were expected more than $2.3 billion. Both l dot.com and bricks-and-clicks merchants were to suffer from the said losses. When merchants become aware of identifying and putting in place cost-effective controls to prevent fraud, both top and bottom lines results might be raised.
  • Consumers’ use of debit cards: patterns, preferences, and price response, Borzekowski, R., Elizabeth, K. K., & Shaista, A. (2008). Journal of Money, Credit and Banking, 40(1), 149-172. There has been dramatic growth of debit cards at selling points in previous years in the USA and is currently out doing the number of transactions for credit cards. Although there are numerous unanswered questions based on debit card use, consumer preferences when using debit, and how consumers might respond to specific pricing of card transactions. Using representative, national consumer survey, this paper describes the current use of debit cards by U.S. consumers, including how demographics affect use. Besides, consumers’ preference for debit cards is not only used in the analysis of how consumers substitute between the debit card and other payment instruments but also examine the relationship between household financial status and payment choice.
  • Off-line generation of limited-use credit card numbers, Rubin, A. D., & Wright, R. N. (2001, February). In International Conference on Financial Cryptography (pp. 196-209). Springer, Berlin, Heidelberg.  There has been a recent launching of limited use card numbers by some credit card companies. For Example, American Express’s single Visa gift cards and card numbers. There is a limitation on the exposure of the old long-term credit number more so in internet transactions. There is involvement of online solutions in using the credit cards where their holders have to must communicate with the credit card issuer to derive a limited-use token. In this paper, the authors state in details a cryptographic off-line generation of limited-use credit card numbers method. The paper shows the numerous balances between security and maintaining the current infrastructure.
  • DNA: an online algorithm for credit card fraud detection for games merchants, Schaidnagel, M., Petrov, I., & Laux, F. (2014, January). In The Second International Conference on Data Analytics (pp. 1-6).  The paper states that online credit card fraud is much and represents a significant problem for online traders. The author proceeds by stating a total loss as a result of credit card fraud were &7.60 billion with an indication of its increase. The paper introduces DNA an approach to online fraud detection that is based on the formulas using characteristics originating from transaction sequence. The experimental analysis and evaluation showed that DNA fraud detection performance was 16.25 % better fraud detection accuracy, 99.59 % precision and low response time. More experiments to demonstrate the good scalability of the suggested algorithm were conducted
  • E-gaming and money laundering risks: a European overview, Levi, M. (2009, December). In ERA Forum (Vol. 10, No. 4, pp. 533-546). Springer-Verlag. The paper outlines the examination of fraud and money laundering risks that evolve from a controlled online gaming sector and describe the safeguard taken by the same sector against such risks. There was the establishment that the online gaming sector uses many techniques to mitigate money laundering and fraud risks and when the techniques are compared by Customer identification and monitoring in off-line gaming and financial service sector, It is modest in the significant misuse of online gaming for laundering reasons both for cash-generating crimes and non-cash.

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