Banking technology pdf

Explore Magazines. Editors' Picks All magazines. Explore Podcasts All podcasts. Difficulty Beginner Intermediate Advanced. Explore Documents. Uploaded by Ragini Verma. Did you find this document useful? Is this content inappropriate? Report this Document.

Flag for inappropriate content. Related titles. Carousel Previous Carousel Next. Jump to Page. Search inside document. Reduced operational and transactional cost for the bank ATM is an electronic banking outlet that allows customers to complete basic transactions without the aid of a branch representative or teller. Documents Similar To 2 Banking Technology. Eon Zeus. Tejaswi Assu. Monojit Kar Munna. Macris Mallari. Blake Thompson. Mahesh Chavan.

Live Law. Hossein tt. Darshan Dhaduk. Anonymous Dcijbdx7. Amar Koli. Chandrani Chatterjee. Mohamed Abdel-Aziz. Timothy Black. Afnan Ansari. D Ann Hoppie.

Donald Brandenburg. Salim Sha. Garda Anggara. Diana Juan. Rekha Rani. Nasser Alsowyan. More From Ragini Verma. Ragini Verma. Popular in Banking. Matthew Butler. SWIFT provides secure, standard- plays an extremely important role. It essentially ized messaging services and interface software involves collecting data from several disparate to 7, financial institutions spread over sources to build a central data warehouse to countries.

Data mining in payments, securities, treasury, and trade. Data warehousing also allows institutions. SWIFT, through its comprehensive banks to perform time series analysis and online messaging standards, offers the financial services analytical processing OLAP to answer various industry a common platform of advanced tech- business questions that would put the banks ahead nology and access to shared solutions through of their competitors.

SWIFT works in partnership with pliance-driven reasons are also there behind its members to provide low-cost, competitive fi- banks establishing a data warehouse. Basel II nancial processing and communications services accord is one such compliance. Basel II is one of of the highest security and reliability.

It contrib- the largest financial shake-ups in recent times; it utes significantly to the commercial success of will eventually lead to new rules and regulations its members through greater automation of the for the banking industry worldwide. Banks were end-to-end financial transaction process, based supposed to have their processes and systems on its leading expertise in message processing in place by the start of , which was when and financial standards setting.

The crux of information and communication technologies in Basel II is to ensure that financial institutions the banking industry Graham, Even though Internet card data.

This data is rewritten to a dummy banking provides ease and convenience, it is most card, which is then usually taken on elaborate vulnerable to hackers and cyber criminals. Online shopping sprees. As the fraudster can sign the fraud is still big business around the world. Even back of the card himself or herself, the merchant though surveillance cameras, guards, alarms, will usually be unaware that they have fallen security screens, dye packs, and law enforcement victim to the fraud.

The key focus in banking industry through other means. Using minimizing credit card and electronic fraud is to Internet banking and high tech credit card fraud, enable the actual user of the account to be cor- it is now possible to steal large amounts of money rectly identified. The notion of allowing a card to anonymously from financial institutions from the prove your identity is fast becoming antiquated comfort of your own home, and it is happening and unreliable.

With this in mind, using biometrics all over the world Graham, This information voice recognition, and computer-recognized is then used to open accounts usually credit card , handwriting analysis Graham, Account Bankers Association are proposing that the smart theft, which is commonly mistaken for identity payment cards are finally poised to change the theft, occurs when existing credit or debit cards future of electronic payments.

The smart card or financial records are used to steal from exist- combines a secure portable payment platform ing accounts. Although account theft is a more with a selection of payment, financial, and non- common occurrence than identity theft, financial financial applications. The reach of the smart card losses caused by identity theft are on average potentially goes beyond the debit and credit card greater and usually require a longer period of model.

Instead of a smart card, ISO uses the term time to resolve Graham, The benefits provided by smart legitimate reason to login to their accounts. The where the fraudster can record the login details. Despite investing enormously into the ICT para- Data mining tools can answer business ques- phernalia for providing better services to custom- tions very quickly and accurately now due to ers, banks cannot take their customers for granted.

The advent of more demanding. In other words, if customers are the Internet has undoubtedly contributed to the dissatisfied with the services of a particular bank, shift of marketing focus, as online information they immediately shift loyalties to its competitors.

Collecting Hence, like in other businesses such as retail and customer demographic and psychographic data insurance, banks have made a paradigmatic shift and its analysis makes target marketing possible. Consequently, the Knowledge discovery in databases KDD or data age-old product-focused strategy has given way mining activities can be categorized into three to a customer-focused strategy.

Hence, building major groups: discovery, predictive modeling, and profitable and long-lasting relationships with forensic analysis. Data mining performs analysis customers has become paramount to banks. This that would be too complicated for traditional is precisely where CRM plays a critical role. The statisticians Rygielski et al.

Most of the main objective of CRM is to make long-lasting banks are investing large amounts of money to and profitable relationships with customers. The emphasis redefines the traditional models of interaction is now on how to effectively utilize the customer between a business and its customers both na- databases to manage the customer relationship. CRM promises achieving The potential difficulty of converting data into corporate objectives, which involves continuous profits lies in obtaining relevant information from use of refined information about current and po- the data and customize the marketing mix poli- tential customers.

The effective management of information tasks: and knowledge is important to CRM for product tailoring and service innovation. It provides a 1. Card marketing: By identifying opti- single and consolidated view of the customer, mal customer segments, card issuers and calculating the value of the customer, establishing acquirers can improve profitability with a strategy for multi-channel-based communication more effective acquisitions and retention with the customer, and designing personalized programs, targeted product development, transactions.

CRM together with data mining and customized pricing. Cardholder pricing and profitability: Card attract more customers and thereby increase their issuers can take advantage of data mining to profit.

Customer knowledge is recognized as an price their products so as to maximize profit asset. IT is the enabling technology for discovery and minimize loss of customers. They can and management of customer knowledge CRM in also perform risk-based pricing. UK ref. Applications of data mining in banking Bankruptcy Customer prediction, lifecycle Credit management scoring Fraud Cardholder detection, pricing and Anti-money laundering profitability Data M ining Target marketing Customer segmentation Ma rket basket Churn analysis modeling Cross-sell, Up -sell lifetime value and to service each segment potential churners and giving banks early appropriately.

Once potential churners 4. Forensic analysis: It is unusual to employ are identified, banks take remedial actions data mining for forensic analysis in many to prevent such customers from leaving. Businesses must have retaining the existing customers.

Anti-money laundering: Money launder- achieve a balance between privacy rights ing, considered a major financial crime, is and economic benefits. Current CRM solu- the process of illegally transferring money tions are not ensuring customer information from one country to another in an innocuous privacy fully Rygielski et al.

With development banks can cross-sell or up-sell their products of global economy and Internet banking, it to customers. Customer churn modeling: Customer will become more prevalent, more difficult churn modeling is an important problem for to investigate, and more detrimental to the banks and financial institutions to grapple economy and financial systems.

The investi- with. Churn happens when existing custom- gation of money laundering crimes involves ers become disgruntled with some aspects of reading and analyzing thousands of textual the service of a given bank and shift loyalties documents to generate crime group models to one of its competitors. Risk is defined as the potential for realization of This is achieved by either overvaluing imports or unwarranted consequences of an event.

In view of undervaluing exports. Even in the area Zdanowicz, Data mining is extremely of risk management, various areas of computer useful in tackling the problem, and techniques science are employed like never before and the like Web mining, text mining, collaborative filter- growth has been tremendous.

Various statistical ing, social network analysis, and link discovery and computer science algorithms are used for based on correlation analysis are nowadays used quantifying the risk whose information can then be to trace the links between transfer of high-value used by the management team in hedging the risk amounts Zhang et al. Figure 2 succinctly through various countermeasures as applicable. Credit risk is the risk of a ingly important as a way for companies to remain counter party not meeting its obligations.

Vari- competitive. The banking sector is also demand- whose information can be very valuable when ing reengineering due to changes in economic the management makes the decision of whether setting, consumer needs, and market competition, or not to grant a loan to a counter party.

The majority of cur- ric methods, and artificial intelligent techniques, rent banking IT systems adopt an account-oriented have been developed in order to successfully approach, thus limiting flexibility either to create handle credit scoring tasks.

Discriminant analysis strong relationships with their existing customers and logistic regression are the most commonly or to attract new ones with increased marketing utilized statistical credit scoring techniques, efforts. Hence, there is a practical need for re- though often being criticized due to their strong engineering of both banking business processes model assumptions and poor credit scoring ca- and their associated information systems.

It was pabilities. On the other hand, the artificial neural found that object-oriented methods are useful for networks are attractive alternatives in handling business process reengineering as they can form a credit scoring tasks due to their associated memory basis for representing banking business processes characteristic, generalization capability.

Even and information systems Mentzas, Li et al. Later hybrid models involving mul- used to learn causal relationships and hence used tivariate adaptive regression splines MARS and to gain an understanding of problem domains and neural networks Lee et al. In banks in which problems. Many Market risk can be broadly classified into sophisticated methodologies have been proposed interest rate risk, foreign exchange rate risk, in order to quantify operational risk in recent and liquidity risk.

Interest rate risk and foreign times. The methodologies range from simple math- exchange rate risk are modeled and predicted ematical methods to sophisticated soft computing by using time series methods, neural networks, methods. Scandizzo discussed the use of decision trees, and so forth. Linear models include regression models, An interesting and useful way of storing informa- discriminant analysis, and so forth.

The non- tion for banks with business presence in several linear models, based on artificial intelligence, countries is with a storage area network SAN. Neural networks are an alternative to non- information infrastructure, which enables any-to- parametric regressions. Bayesian belief networks any n-to-n cardinality interconnection of serv- have attracted much attention recently as a pos- ers and storage systems.

Using SAN, banks can sible solution to the problems of decision support store their wealth of information in an organized, under uncertainty. Bayesian networks provide a secure, and easily accessible manner. SAN offers lot of benefits for data analysis. Firstly, the model the following advantages: encodes dependencies among all variables and it also handles missing data.

The signature Having stored a lot of sensitive and confi- is sensitive to the contents of the file and dential financial data about their customers in the signature is sent along with the file for information systems, the banks have to worry verification e.

To avoid such catastrophic devel- HTML pages. The area of cryptography opments, the banks must deploy a very powerful encompasses several algorithms to ensure and reliable mechanism for securing the data. This can prevent Business continuity planning and disaster re- unwanted people entering into the bank. Business disruptions occur for both correct user is accessing the information foreseeable and unforeseen reasons, such as ter- authentication by a valid username and rorist attacks, floods, earthquakes, and landslides.

To survive, tion. The role played by smart duced, and regulatory compliance is maintained cards, storage area networks, data warehousing, in the event of crisis. Customer loyalty, business customer relationship management, cryptography, reputation, and public trust must be protected by statistics, and artificial intelligence in modern an effective and actionable BCP when a disaster banking is very well brought out. The chapter strikes. The planning process should include: laundering, and so forth.

As regards future directions, and recovery plans; awareness planning; and the proliferating research in all fields of ICT and periodic reviews and revisions. Physically, banks computer science can make steady inroads into can have a mirror site, which will act as a hot banking technology because any new research redundant unit to the original site where disaster idea in these disciplines can potentially have a may strike and business can be conducted as usual great impact on banking technology.

However, software solutions to BCP are also possible. Modeling, measuring and awareness activities Business Continuity Plan- hedging operational risk.

Chichester: John Wiley ning for Banking and Finance, Engler, H. UK: Reuters, Pearson Education. Graham, B.