Unit 4
Warming up activities
What is it Big data? What kind of information about the customers can help to the banks? How?
Vocabulary:
big data – большие данные databases – база данных
financial institution – финансовое учреждение fraud - обман; мошенничество, жульничество
money-laundering – отмывание денег (легализация денег) fake accounts – поддельные счета
cross-border remittances – трансграничные денежные переводы debit cards - платёжная карта, дебетовая карта
cash – деньги, наличные деньги, финансы, денежные средства criminal gang – криминальная банда
Read the following text carefully and answer the following questions:
1.What is it Watson?
2.For what purpose did Citigroup “hired” Watson?
3.For what purposes «big data» can be used?
4.How do computers help to identify fraud?
Big data Crunching the numbers
Banks know a lot about their customers. That information may be valuable in more ways than one
May 19th 2012 | THE ECONOMIST
A BIG BANK hires a star analyst from another firm, promising to pay a substantial bonus if the new hire increases revenue or cuts costs. In banking this happens all the time, but this deal differs from the rest in one small detail: the new hire, Watson, is an IBM computer.
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Watson became something of a celebrity after beating the champion human contestants on “Jeopardy”, an American quiz show. Its skill is to be able to process millions of documents quickly by reading and “understanding” ordinary written language. Computers have no trouble with searching data neatly sorted in databases. Watson's claim to fame is that it can do the same with “unstructured data” such as those found in e- mails, news reports, books and websites. IBM hopes that Watson may, in time, do some of the work that human analysts do now, such as reading the financial pages of newspapers, looking at thousands of company results and forecasts and producing a list of companies that might be takeover targets soon.
Citigroup has hired Watson to help it decide what new products and services (such as loans or credit cards) to offer its customers. The bank doesn't say so, but Watson's first job may well be to try to cut down on fraud and look for signs of customers becoming less creditworthy. If so, Watson will be following other computers designed to deal with “big data”. Across a slew of new firms in Silicon Valley and in big banks across the world, a range of new ideas is being tried to crunch data. Some have the potential to change banking from the bottom up.
In most financial institutions the immediate use of big data is in containing fraud and complying with rules on money laundering and sanctions. Even seemingly simple tasks, such as checking the names of clients against those on a sanctions blacklist, become immensely complicated in the real world, where banks may have thousands of customers with the same names as those on the blacklist. Each becomes a false positive that may embarrass the bank and ruin a client relationship. So banks have had to turn to computers that can amass data from a variety of different sources, including the customer's nationality and address, the names of family members, and whether they have travelled to or received money from countries on sanctions lists.
When moving on to more complex tasks, such as identifying the tiny percentage of fraudulent transactions among the millions of legitimate ones, the demands become ever greater. The problem is getting bigger because as banking has moved onto computers and mobile phones, and payments have shifted from cash to cards or electronic transfers, the opportunities for fraud have proliferated.
The danger of fraud is particularly acute in areas such as card payments and some of the more innovative kinds of money transfers that
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are offering cheaper or more convenient services than those already available. PayPal, which dominates online payments, barely survived its first year in business after it came under sustained attack from fraudsters, and several of its early rivals were cleaned out and had to close down.
PayPal came up with Igor, a computer system named after a Russian thief and hacker who had opened fake accounts and taunted the firm's security team in e-mails. Igor would look for patterns, such as a concentration of payments close to the top limit and their destinations, and then compare those payments with all the others in the system. What started at PayPal soon spread to the rest of banking and beyond it.
A better kind of crystal ball
The firm that has perhaps gone furthest in finding useful connections in disparate databases is Palantir Technologies, which takes its name from the magical all-seeing crystal balls of J.R.R. Tolkien's mythology. It was founded by a group of PayPal alumni and backed by Peter Thiel, one of PayPal's co-founders. Its speciality is building systems that pull together information from different places and try to find connections. Some of its earliest adopters have been spy agencies. In America the CIA and the FBI use it to connect individually innocuous activities such as taking flying lessons and receiving money from abroad to spot potential terrorists. Its other main market is in banking, where big firms such as JPMorgan and Citi use it for a range of activities from structuring equity derivatives to reducing loan losses.
A stablemate of sorts to Palantir is Xoom, a firm that specialises in cross-border remittances. It is backed by some of Palantir's investors and has swapped a senior employee with it, but more importantly it shares Palantir's belief that given enough data even the toughest risks can be managed. Xoom accepts payments from bank accounts or debit cards in America, then hands over cash in countries such as the Philippines or India. It does not have much time to find out if it has been swindled on a payment before it has to produce the cash. So it has devised a sophisticated computer system that analyses a range of data, the nature of most of which it will not disclose.
Some of these checks may seem obvious, but some are not easy to do when processing millions of transactions and moving billions of dollars. Moreover, few of these pieces of information on their own are powerful
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enough signals for Xoom to decline or agree to make a payment. Yet when the computer looks at all of the payments in its system, it is remarkably good at weaving together the bits of information to spot fraud.
It also learns as it goes. When it recently noticed a string of payments funded by Discover credit cards and originating in New Jersey, its algorithms raised a red flag even though each payment looked legitimate. “It saw a pattern when there shouldn't have been a pattern,” says John Kunze, Xoom's chief executive. The pattern it found turned out to have been an effort by a criminal gang to defraud the firm.
Fill the gaps using the words:
Creditworthy, activities, money, credit, transactions
1.Discover … cards and originating in New Jersey, its algorithms raised a red flag even though each payment looked legitimate.
2.Some of these checks may seem obvious, but some are not easy to do when processing millions of … and moving billions of dollars.
3.The bank doesn't say so, but Watson's first job may well be to try to cut down on fraud and look for signs of customers becoming less …
4.Its other main market is in banking, where big firms such as JPMorgan and Citi use it for a range of … from structuring equity derivatives to reducing loan losses.
5.In most financial institutions the immediate use of big data is in containing fraud and complying with rules on … laundering and sanctions.
Write down all words connected with economy
Make up your plan to this article
Reproduce the text using your list of the words and your plan
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Unit 5
Warming up activities
«Typical tax heaven» - do you know about it anything? What do you know about onshore and offshore?
Vocabulary:
to peddle – торговать
curbing criminality – пресечение преступности hassle-free – без проблем
to fight tooth and nail – бороться не на жизнь, а на смерть shells (pl) – деньги, активы
tax havens – налоговый рай anonymity – анонимность
reciprocation – взаимный обмен, ответное действие withdrawal – изъятие
to flock – стекаться
Read the following text very carefully and answer the following questions:
1.What is typical tax heaven?
2.What is the difference between onshore and offshore?
3.Does Delaware need to levy taxes on sales? Why?
4.Why do investigators joke that Delaware stands for “Dollars and Euros Laundered and Washed at Reasonable Expense”?
Not a palm tree in sight
Some onshore jurisdictions can be laxer than the offshore sort
Feb 16th 2013 | THE ECONOMIST
SAM KOIM, THE chairman of Papua New Guinea’s anticorruption watchdog, raised eyebrows at a meeting of financial crime fighters in Sydney last October when he described how officials from his country were systematically “using Australia as a Cayman Islands” by laundering a significant portion of corruptly obtained funds through Australian banks and property deals. Papuans were thought to be the largest proper-
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