Credit Suisse Group is a Switzerland-based multinational financial services holding company, headquartered in Zürich, that operates the Credit Suisse Bank and other financial services investments. The company is organized as a stock corporation with four divisions: Investment Banking, Private Banking, Asset Management, and a Shared Services Group that provides marketing and support to the other three divisions.Credit Suisse was founded by Alfred Escher in 1856 under the name Schweizerische Kreditanstalt in order to fund the development of Switzerland's rail system. It issued loans that helped create Switzerland's electrical grid and the European rail system. It also helped develop the country's currency system and funded entrepreneurship. In the 1900s Credit Suisse began shifting to retail banking in response to the elevation of the middle-class and the growing popularity of savings accounts. Credit Suisse partnered with First Boston in 1978. After a large failed loan put First Boston under financial stress, Credit Suisse bought a controlling share of the bank in 1988. In the 1990s, Credit Suisse acquired the Winterthur Group, Swiss Volksbank, Swiss American Securities Inc. and Bank Leu among others. In the year 2000, it added the U.S. investment firm Donaldson, Lufkin & Jenrette.The company restructured itself in 2002, 2004 and 2006. It was one of the least affected banks during the global financial crisis, but afterwards began shrinking its investment business, executing layoffs and cutting costs. During the period between 2008 and 2012, Germany, Brazil, and the United States began a series of investigations into the use of Credit Suisse accounts for tax evasion. In May 2014, the company pleaded guilty to decades of conspiring to help US citizens "hide their wealth" in order to avoid taxes, and agreed to pay $2.6 billion in fines.In 2014, Credit Suisse had 888.2 USD Bn of assets under management according to the Scorpio Partnership . Wikipedia.
Agency: Cordis | Branch: FP7 | Program: CP | Phase: ICT-2013.3.4 | Award Amount: 4.68M | Year: 2013
NanoStreams co-designs a micro-server architecture and software stack that address the unique challenges of hybrid transactional-analytical workloads, which are encountered by emerging applications of real-time big-data analytics. To this end, NanoStreams brings together embedded system design principles, application-specific compilers, and HPC software practices.The processor technology that underpins the NanoStreams micro-server is an amalgam of RISC cores and nano-cores, a new class of programmable custom accelerators. Novel automatic compiler generation and parameterization technology enables low-effort programming and integration of nano-cores into application-specific, many-core accelerators. The proposed heterogeneous Analytics-on-Chip processor forms the backbone of the NanoStreams micro-server, which further leverages a hybrid DRAM-PCRAM memory system and a non-cache-coherent scale-out architecture to achieve extreme energy-efficiency.The software stack of the NanoStreams micro-server is rooted in domain-specific languages for analytical queries, which the project implements with a streaming dataflow execution model. The language runtime system uses real-time scheduling, performance isolation techniques and region-based memory management to minimize latency on the transactional path and maximize throughput on the analytical path. NanoStreams virtualizes lightweight PCRAM-based persistent memory, for direct user access and locality optimization.The project will deliver a real-silicon prototype, based on the Xilinx Zynq platform and ARM-Linux. The quantitative objective of NanoStreams, in comparison with contemporary HPC servers, is to reduce analytical response time of commercial in-memory databases by at least 30%, while sustaining transactional throughput and improving system energy-efficiency and programmability. NanoStreams will demonstrate these advances with industry-standard workloads and four real-world case studies.
Agency: Cordis | Branch: FP7 | Program: CP | Phase: ICT-2013.6.2 | Award Amount: 4.31M | Year: 2013
The GreenDataNet project aims at designing and validating a new, system-level optimisation solution allowing urban data centres to radically improve their energy and environmental performance. The objective is to develop a set of beyond state-of-the-art technologies that will allow urban data centres to reach 80% of renewable power and decrease their average Power Usage Effectiveness (PUE) from 1.6-2.0 today to less than 1.3. GreenDataNet will enable energy monitoring and optimisation of IT, power, cooling and storage at three levels: servers and racks, individual data centres, and networks of data centres. To further reduce the need for grid power, GreenDataNet will also work on the integration of local photovoltaic energy in combination with an innovative, large-scale storage solution that will facilitate the connection of data centres to smart grids. Within this project, second-life electric vehicle Li-ion batteries will be investigated as a more advantageous solution for data centres to become actual smart grid nodes.The whole solution will be implemented as an open-source platform to allow third parties to provide additional optimisation modules and ensure the long-term sustainability of the project. Three demonstration sites will be utilised to test and validate the GreenDataNet concept: a data centre from Credit Suisse in Switzerland, a data centre from CEA in France that includes a large photovoltaic area and a smart grid test platform, and a pilot site in the Netherlands that is being used by a Dutch consortium working on Green IT technologies. In addition, research on heat reuse vs. free cooling will be conducted in a new data centre built by ICTroom in Belgium. Performance indicators that go beyond PUE will be experimented in the project and will support the work of the consortium in standardisation bodies like CEN/CENELEC/ETSI. Based on the project outcome, GreenDataNet will release guidelines to help make data centres more sustainable in the future.
Capriotti L.,Credit Suisse
Journal of Computational Finance | Year: 2011
We show how algorithmic differentiation can be used to efficiently implement the pathwise derivative method for the calculation of option sensitivities using Monte Carlo simulations. The main practical difficulty of the pathwise derivative method is that it requires the differentiation of the payout function. For the type of structured options for which Monte Carlo simulations are usually employed, these derivatives are typically cumbersome to calculate analytically, and too time consuming to evaluate with standard finite-difference approaches. In this paper we address this problem and show how algorithmic differentiation can be employed to calculate these derivatives very efficiently and with machine-precision accuracy. We illustrate the basic workings of this computational technique by means of simple examples, and we demonstrate with several numerical tests how the pathwise derivative method combined with algorithmic differentiation – especially in the adjoint mode – can provide speed-ups of several orders of magnitude with respect to standard methods. © 2011, Incisive Media Ltd. All rights reserved.
Credit Suisse | Date: 2014-11-24
A development and testing environment with reduced database storage requirements that uses synthetic data based on anonymized real data, which allows the use of sensitive data for testing while protecting such data as required by privacy laws, secrecy laws and company policies.
Credit Suisse | Date: 2016-01-06
The enterprise database system provides methods, data, and user interfaces for performing reassessments and creating financial and accounting disclosure reports. Data fields for entities are monitored for changes that are evident at the end of reporting periods and may trigger the need to reassess the categorization of the entity. The system receives a request to perform a reassessment based on changes to particular data fields during the reporting period. The system retrieves entities that require reassessment based on the trigger events applicable to the entities. A reassessment is performed for each of the entities having a trigger event and the reassessment is stored in a historical database. Based on the reassessment, the system generates prompts to re-categorize the reassessed entity. Following the reassessment and categorization, the system can generate a disclosure report that presents information about the newly categorized entity.
Credit Suisse | Date: 2014-10-17
A method for costing a web service operable for querying one or more data sources, the method including calculating the cost of operating the web service, measuring web service usage data using one or more service metrics, wherein the one or more service metrics include at least one derived service metric, choosing at least one service metric from the one or more service metrics, wherein one or more of the at least one chosen service metrics is a derived service metric, calculating the cost of the web service based on the at least one chosen service metric, and charging for usage of the web service based upon the calculated cost of the web service.
Credit Suisse | Date: 2013-12-31
A system for expense calculation, accrual, allocation, and reconciliation, including an expense calculation module configured to receive transaction data relating to a transaction and to apply at least one charge rule to the transaction data to calculate expense data detailing the expenses expected to be charged in association with the transaction; an accounting control module configured to receive the expense data as an input and to apply at least one accounting rule to the expense data to create enhanced data relating to the transaction; and an invoice reconciliation module configured to receive as inputs the expense data as well as invoice data related to the transaction, and to determine whether the invoice data matches the expense data for the transaction.
Credit Suisse | Date: 2012-09-26
Method of failure detection in an operating system, the method comprising sending a request from a client to a server through a router, storing a token at the router, carrying out the request at the server, and returning a reply from the server to the client through the router, wherein the method comprises the alternative steps of, in response to a failure in the server, generating at the router an error reply based on the token and returning the error reply from the router to the client.
Credit Suisse | Date: 2011-04-04
Providing computer-based systems and methods for analyzing historical performance of financial securities and identifying trades in those securities based on the securities current position as compared to this historical performance. These computer-based systems and computer-implemented methods include identifying stock pairs to include in a trading portfolio, based on a measure of the pairs relative performance, such as a modified Sharpe Ratio. The value of the stocks in each stock pair in the portfolio is assessed and deviations determined. This assessment can occur daily or at a longer or shorter time step. Stocks are bought or sold based on the current price of the stock as compared to historical performance. The present invention preferably employs a large number of stock pairs in the trading portfolio. This use of a large number of pairs results in a plurality of stocks being in more than one stock pair.
Credit Suisse | Date: 2011-10-27
A method is dedicated to synchronizing master (HM) and slave clocks (HE) of a packet-switched network comprising at least two equipments (E1, E2) that are connected to one another via an aggregated connection made up of at least two links (L1-L3) and that are located between said master (HM) and slave clocks (HE) in order to enable them to transmit synchronization packets to one another using a timestamp protocol. This method comprises the following steps: