El documento resume los resultados de un estrés test realizado por la firma Wyman a la banca española. Se analizan dos escenarios: base y adverso. En el escenario adverso, los mayores déficits de capital se darían en Bankia (-18.296 millones), La Caixa (-4.799 millones) y BBVA (-1.703 millones). En general, la mayoría de bancos requerirían capital adicional para cumplir con los requerimientos mínimos en el escenario adverso.
tl;dr:
What is and what is not a blockchain?
What is bitcoin?
How powerful is the bitcoin blockchain network?
How is bitcoin created?
Are there multiple means through which a person can create a cryptocurrency?
What is an ICO (Initial Coin Offering)?
What could blockchain technology mean for the world?
How to Apply Big Data Analytics and Machine Learning to Real Time Processing ...Codemotion
The world gets connected more and more every year due to Mobile, Cloud and Internet of Things. "Big Data" is currently a big hype. Large amounts of historical data are stored in Hadoop to find patterns, e.g. for predictive maintenance or cross-selling. But how to increase revenue or reduce risks in new transactions? "Fast Data" via stream processing is the solution to embed patterns into future actions in real-time. This session discusses how machine learning and analytic models with R, Spark MLlib, H2O, etc. can be integrated into real-time event processing. A live demo concludes the session
Caso de éxito: Proyecto transformacional y de excelencia operativaNeteris Spain
Neteris realizó un análisis exhaustivo de 9 departamentos, identificando más de 200 procesos altamente críticos de esta empresa industrial. Con más de 300 iniciativas de mejora, se construyó un modelo de cuadro de mandos integral, basado en KPIs de la industria. Estas soluciones a medida permitieron la mejora en el entorno colaborativo entre departamentos, proveedores y clientes, gracias a la visión transversal de los procesos.
Trends at JavaOne 2016: Microservices, Docker and Cloud-Native MiddlewareKai Wähner
In addition to focusing on many related concepts like container or service discovery, technologies like Docker and cloud platforms, my session also discussed ten lessons learned from building cloud-native middleware microservices together with our customers in the last months.
The demo brings this from theory to practice by showing how to deploy a single (i.e. built just once) TIBCO BusinessWorks Container Edition microservice to different cloud and container platforms: Docker, Kubernetes and Pivotal CloudFoundry. The video also shows how to leverage other cloud-native open source frameworks such as Consul and Spring Cloud Config for distributed configuration management and service discovery of middleware microservices.
Open Source IoT Project Flogo - Introduction, Overview and ArchitectureKai Wähner
Go-powered Open Source Project Flogo for Lightweight IoT and Edge Integration:
The Internet of Things (IoT) brings up 50 billion devices until 2020, which have to be connected somehow. Challenges include low bandwidth, high latency, non-reliable connectivity and the need for low network costs. Therefore, a gateway at the edge is needed remotely on site of the devices to filter, aggregate and send just relevant data into the cloud or data center.
This session introduces open source project Flogo, which allows developing ultra-lightweight IoT edge applications with a zero-coding web user interface. Coders can also rely just on Go code if they want. It is written in Go programming language and therefore 20-50x more lightweight than similar Java or JavaScript frameworks.
The session focuses on live demos and shows how to develop ultra-lightweight microservices and how to integrate IoT devices using standards such as MQTT, WebSockets, CoaP or REST. The last part of the session compares Project Flogo to other open source IoT projects like Eclipse Kura or Node-RED and cloud offerings such as AWS IoT.
Check out www.flogo.io and https://community.tibco.com/products/project-flogo for more information and community.
Log Analytics for Distributed MicroservicesKai Wähner
Log Analytics and Operational Intelligence for Distributed Microservices.
IT systems and applications generate more and more distributed machine data due to millions of mobile devices, Internet of Things, social network users, and other new emerging technologies. However, organizations experience challenges when monitoring and managing their IT systems and technology infrastructure. They struggle with distributed Microservices and Cloud architectures, custom application monitoring and debugging, network and server monitoring / troubleshooting, security analysis, compliance standards, and others.
This session discusses how to solve the challenges of monitoring and analyzing Terabytes and more of different distributed machine data to leverage the “digital business”. The main part of the session compares different open source frameworks and SaaS cloud solutions for Log Management and operational intelligence, such as Graylog , the “ELK stack”, Papertrail, Splunk or TIBCO LogLogic Unity). A live demo will demonstrate how to monitor and analyze distributed Microservices and sensor data from the “Internet of Things”.
The session also explains the distinction of the discussed solutions to other big data components such as Apache Hadoop, Data Warehouse or Machine Learning, and how they can complement each other in a big data architecture.
The session concludes with an outlook to the new, advanced concept of IT Operations Analytics (ITOA). Prsesn
How to Choose the Right Technology, Framework or Tool to Build MicroservicesKai Wähner
Microservices are the next step after SOA: Services implement a limited set of functions. Services are developed, deployed and scaled independently. This way you get shorter time to results and increased flexibility.
Microservices have to be independent regarding build, deployment, data management and business domains. A solid Microservices design requires single responsibility, loose coupling and a decentralized architecture. A Microservice can to be closed or open to partners and public via APIs.
This session discusses technologies such as REST, WebSockets, OSGi, Puppet, Docker, Cloud Foundry, and many more, which can be used to build and deploy Microservices. The main part shows different open service frameworks and proprietary tools to build Microservices on top of these technologies. Live demos illustrate the differences. The audience will learn how to choose the right alternative for building Microservices.
Machine Learning Applied to Real Time Scoring in Manufacturing and Energy Uti...Kai Wähner
Kai Wähner (@KaiWaehner) is a Technology Evangelist and Community Director at TIBCO Software - a leading provider of integration and analytics middleware. Kai is an experience guy in broad variety of topics like Big Data, Advanced Analytics & Machine Learning, he loves to write articles and blog about new technologies and make talks. The talk is about 3 different projects where Kai's team built analytic models with technologies R, Apache Spark or H2O.ai which were deployed to real time processing. The use cases include predictive maintenance in manufacturing but also fraud detection in banking and context-specific pricing in insurance. For one of the cases, Kai gonna show detailed steps will be, how it was built and deployed using supervised/unsupervised ML.
Talk was done together with my colleague Ankitaa Bhowmick.
Streaming Analytics - Comparison of Open Source Frameworks and ProductsKai Wähner
Stream Processing is a concept used to create a high-performance system for rapidly building applications that analyze and act on real-time streaming data. Benefits, amongst others, are faster processing and reaction to real-time complex event streams and the flexibility to quickly adapt to changing business and analytic needs. Big data, cloud, mobile and internet of things are the major drivers for stream processing and streaming analytics.
This session discusses the technical concepts of stream processing and how it is related to big data, mobile, cloud and internet of things. Different use cases such as predictive fault management or fraud detection are used to show and compare alternative frameworks and products for stream processing and streaming analytics.
The audience will understand when to use open source frameworks such as Apache Storm, Apache Spark or Esper, and powerful engines from software vendors such as IBM InfoSphere Streams or TIBCO StreamBase. Live demos will give the audience a good feeling about how to use these frameworks and tools.
The session will also discuss how stream processing is related to Hadoop and statistical analysis with software such as SAS, Apache Spark’s MLlib or R language.
TIBCO BWCE and Netflix' Hystrix Circuit Breaker for Cloud Native Middleware M...Kai Wähner
These slides show how to use TIBCO BusinessWorks Container Edition (BWCE) with Netflix' Hystrix Open Source Implementation of the Design Pattern 'Circuit Breaker' to develop, deploy and monitor cloud native middleware microservices.
Video recording with live demo: https://youtu.be/VL7-T6IIuZk
Find more information about cloud native middleware at https://community.tibco.com/wiki/microservices-containers-and-cloud-native-architectures
Blockchain + Streaming Analytics with Ethereum and TIBCO StreamBase Kai Wähner
This slide deck shows why middleware and streaming analytics is relevant for any blockchain project. It discusses how to leverage stream processing and how to integrate with blockchain events. The focus was on integration of TIBCO StreamBase and Ethereum Blockchain. But the same can be done easily for any Hyperledger Blockchain like IBM's Fabric, IROHA or Intel's Sawtooth Lake, or others like R3 Corda or Ripple. For smart contract deployment, I use Browser Solidity and MetaMask. But the sasme can be achieved with TIBCO StreamBase (or BusinessWorks, too). The live demo can be watched on Youtube.
The outlook includes some upcoming topics like
- Live Visualization for Real Time Monitoring and Proactive Actions
- Cross-Integration with Ethereum and Hyperledger Blockchains
-Data Discovery for Historical Analysis to Find Insights and Patterns
- Machine Learning to Build of Analytic Models
- Application Integration with other Applications (Legacy, Cloud Services, …)
- Native Hardware Integration with Internet of Things Devices
Some use cases / real world examples:
- Banking: Data Discovery for compliance issues, fraud or other anomalies
- Stock / Energy Trading: Subcribe to events (e.g. price went over a threshold) – event correlation and proactive live UI
- Manufacturing / Internet of Things: Supply chain management with various partner companies (maybe even various blockchains)
- Many other use cases...
Thanks to my colleague Steven Warwick for implementing the StreamBase connectors and demo!
Streaming Analytics Comparison of Open Source Frameworks, Products, Cloud Ser...Kai Wähner
Streaming Analytics Comparison of Open Source Frameworks, Products and Cloud Services. Includes Apache Storm, Flink, Spark, TIBCO, IBM, AWS Kinesis, Striim, Zoomdata, ...
This session discusses the technical concepts of stream processing / streaming analytics and how it is related to big data, mobile, cloud and internet of things. Different use cases such as predictive fault management or fraud detection are used to show and compare alternative frameworks and products for stream processing and streaming analytics.
The focus of the session lies on comparing
- different open source frameworks such as Apache Apex, Apache Flink or Apache Spark Streaming
- engines from software vendors such as IBM InfoSphere Streams, TIBCO StreamBase
- cloud offerings such as AWS Kinesis.
- real time streaming UIs such as Striim, Zoomdata or TIBCO Live Datamart.
Live demos will give the audience a good feeling about how to use these frameworks and tools.
The session will also discuss how stream processing is related to Apache Hadoop frameworks (such as MapReduce, Hive, Pig or Impala) and machine learning (such as R, Spark ML or H2O.ai).
The digital transformation is going forward due to Mobile, Cloud and Internet of Things. Disrupting business models leverage Big Data Analytics and Machine Learning.
"Big Data" is currently a big hype. Large amounts of historical data are stored in Hadoop or other platforms. Business Intelligence tools and statistical computing are used to draw new knowledge and to find patterns from this data, for example for promotions, cross-selling or fraud detection. The key challenge is how these findings can be integrated from historical data into new transactions in real time to make customers happy, increase revenue or prevent fraud. "Fast Data" via stream processing is the solution to embed patterns - which were obtained from analyzing historical data - into future transactions in real-time.
This session uses several real world success stories to explain the concepts behind stream processing and its relation to Hadoop and other big data platforms. It discusses how patterns and statistical models of R, Spark MLlib, H2O, and other technologies can be integrated into real-time processing by using several different real world case studies. The session also points out why a Microservices architecture helps solving the agile requirements for these kind of projects.
A brief overview of available open source frameworks and commercial products shows possible options for the implementation of stream processing, such as Apache Storm, Apache Flink, Spark Streaming, IBM InfoSphere Streams, or TIBCO StreamBase.
A live demo shows how to implement stream processing, how to integrate machine learning, and how human operations can be enabled in addition to the automatic processing via a Web UI and push events.
Keywords: Big Data, Fast Data, Machine Learning, Analytics, Analytic Model, Stream Processing, Event Processing, Streaming Analytics, Real Time, Hadoop, Spark, MLlib, Streaming, R, TERR, TIBCO, Spotfire, StreamBase, Live Datamart, H20, Predictive Analytics, Data Discovery, Insights, Patterns
tl;dr:
What is and what is not a blockchain?
What is bitcoin?
How powerful is the bitcoin blockchain network?
How is bitcoin created?
Are there multiple means through which a person can create a cryptocurrency?
What is an ICO (Initial Coin Offering)?
What could blockchain technology mean for the world?
How to Apply Big Data Analytics and Machine Learning to Real Time Processing ...Codemotion
The world gets connected more and more every year due to Mobile, Cloud and Internet of Things. "Big Data" is currently a big hype. Large amounts of historical data are stored in Hadoop to find patterns, e.g. for predictive maintenance or cross-selling. But how to increase revenue or reduce risks in new transactions? "Fast Data" via stream processing is the solution to embed patterns into future actions in real-time. This session discusses how machine learning and analytic models with R, Spark MLlib, H2O, etc. can be integrated into real-time event processing. A live demo concludes the session
Caso de éxito: Proyecto transformacional y de excelencia operativaNeteris Spain
Neteris realizó un análisis exhaustivo de 9 departamentos, identificando más de 200 procesos altamente críticos de esta empresa industrial. Con más de 300 iniciativas de mejora, se construyó un modelo de cuadro de mandos integral, basado en KPIs de la industria. Estas soluciones a medida permitieron la mejora en el entorno colaborativo entre departamentos, proveedores y clientes, gracias a la visión transversal de los procesos.
Trends at JavaOne 2016: Microservices, Docker and Cloud-Native MiddlewareKai Wähner
In addition to focusing on many related concepts like container or service discovery, technologies like Docker and cloud platforms, my session also discussed ten lessons learned from building cloud-native middleware microservices together with our customers in the last months.
The demo brings this from theory to practice by showing how to deploy a single (i.e. built just once) TIBCO BusinessWorks Container Edition microservice to different cloud and container platforms: Docker, Kubernetes and Pivotal CloudFoundry. The video also shows how to leverage other cloud-native open source frameworks such as Consul and Spring Cloud Config for distributed configuration management and service discovery of middleware microservices.
Open Source IoT Project Flogo - Introduction, Overview and ArchitectureKai Wähner
Go-powered Open Source Project Flogo for Lightweight IoT and Edge Integration:
The Internet of Things (IoT) brings up 50 billion devices until 2020, which have to be connected somehow. Challenges include low bandwidth, high latency, non-reliable connectivity and the need for low network costs. Therefore, a gateway at the edge is needed remotely on site of the devices to filter, aggregate and send just relevant data into the cloud or data center.
This session introduces open source project Flogo, which allows developing ultra-lightweight IoT edge applications with a zero-coding web user interface. Coders can also rely just on Go code if they want. It is written in Go programming language and therefore 20-50x more lightweight than similar Java or JavaScript frameworks.
The session focuses on live demos and shows how to develop ultra-lightweight microservices and how to integrate IoT devices using standards such as MQTT, WebSockets, CoaP or REST. The last part of the session compares Project Flogo to other open source IoT projects like Eclipse Kura or Node-RED and cloud offerings such as AWS IoT.
Check out www.flogo.io and https://community.tibco.com/products/project-flogo for more information and community.
Log Analytics for Distributed MicroservicesKai Wähner
Log Analytics and Operational Intelligence for Distributed Microservices.
IT systems and applications generate more and more distributed machine data due to millions of mobile devices, Internet of Things, social network users, and other new emerging technologies. However, organizations experience challenges when monitoring and managing their IT systems and technology infrastructure. They struggle with distributed Microservices and Cloud architectures, custom application monitoring and debugging, network and server monitoring / troubleshooting, security analysis, compliance standards, and others.
This session discusses how to solve the challenges of monitoring and analyzing Terabytes and more of different distributed machine data to leverage the “digital business”. The main part of the session compares different open source frameworks and SaaS cloud solutions for Log Management and operational intelligence, such as Graylog , the “ELK stack”, Papertrail, Splunk or TIBCO LogLogic Unity). A live demo will demonstrate how to monitor and analyze distributed Microservices and sensor data from the “Internet of Things”.
The session also explains the distinction of the discussed solutions to other big data components such as Apache Hadoop, Data Warehouse or Machine Learning, and how they can complement each other in a big data architecture.
The session concludes with an outlook to the new, advanced concept of IT Operations Analytics (ITOA). Prsesn
How to Choose the Right Technology, Framework or Tool to Build MicroservicesKai Wähner
Microservices are the next step after SOA: Services implement a limited set of functions. Services are developed, deployed and scaled independently. This way you get shorter time to results and increased flexibility.
Microservices have to be independent regarding build, deployment, data management and business domains. A solid Microservices design requires single responsibility, loose coupling and a decentralized architecture. A Microservice can to be closed or open to partners and public via APIs.
This session discusses technologies such as REST, WebSockets, OSGi, Puppet, Docker, Cloud Foundry, and many more, which can be used to build and deploy Microservices. The main part shows different open service frameworks and proprietary tools to build Microservices on top of these technologies. Live demos illustrate the differences. The audience will learn how to choose the right alternative for building Microservices.
Machine Learning Applied to Real Time Scoring in Manufacturing and Energy Uti...Kai Wähner
Kai Wähner (@KaiWaehner) is a Technology Evangelist and Community Director at TIBCO Software - a leading provider of integration and analytics middleware. Kai is an experience guy in broad variety of topics like Big Data, Advanced Analytics & Machine Learning, he loves to write articles and blog about new technologies and make talks. The talk is about 3 different projects where Kai's team built analytic models with technologies R, Apache Spark or H2O.ai which were deployed to real time processing. The use cases include predictive maintenance in manufacturing but also fraud detection in banking and context-specific pricing in insurance. For one of the cases, Kai gonna show detailed steps will be, how it was built and deployed using supervised/unsupervised ML.
Talk was done together with my colleague Ankitaa Bhowmick.
Streaming Analytics - Comparison of Open Source Frameworks and ProductsKai Wähner
Stream Processing is a concept used to create a high-performance system for rapidly building applications that analyze and act on real-time streaming data. Benefits, amongst others, are faster processing and reaction to real-time complex event streams and the flexibility to quickly adapt to changing business and analytic needs. Big data, cloud, mobile and internet of things are the major drivers for stream processing and streaming analytics.
This session discusses the technical concepts of stream processing and how it is related to big data, mobile, cloud and internet of things. Different use cases such as predictive fault management or fraud detection are used to show and compare alternative frameworks and products for stream processing and streaming analytics.
The audience will understand when to use open source frameworks such as Apache Storm, Apache Spark or Esper, and powerful engines from software vendors such as IBM InfoSphere Streams or TIBCO StreamBase. Live demos will give the audience a good feeling about how to use these frameworks and tools.
The session will also discuss how stream processing is related to Hadoop and statistical analysis with software such as SAS, Apache Spark’s MLlib or R language.
TIBCO BWCE and Netflix' Hystrix Circuit Breaker for Cloud Native Middleware M...Kai Wähner
These slides show how to use TIBCO BusinessWorks Container Edition (BWCE) with Netflix' Hystrix Open Source Implementation of the Design Pattern 'Circuit Breaker' to develop, deploy and monitor cloud native middleware microservices.
Video recording with live demo: https://youtu.be/VL7-T6IIuZk
Find more information about cloud native middleware at https://community.tibco.com/wiki/microservices-containers-and-cloud-native-architectures
Blockchain + Streaming Analytics with Ethereum and TIBCO StreamBase Kai Wähner
This slide deck shows why middleware and streaming analytics is relevant for any blockchain project. It discusses how to leverage stream processing and how to integrate with blockchain events. The focus was on integration of TIBCO StreamBase and Ethereum Blockchain. But the same can be done easily for any Hyperledger Blockchain like IBM's Fabric, IROHA or Intel's Sawtooth Lake, or others like R3 Corda or Ripple. For smart contract deployment, I use Browser Solidity and MetaMask. But the sasme can be achieved with TIBCO StreamBase (or BusinessWorks, too). The live demo can be watched on Youtube.
The outlook includes some upcoming topics like
- Live Visualization for Real Time Monitoring and Proactive Actions
- Cross-Integration with Ethereum and Hyperledger Blockchains
-Data Discovery for Historical Analysis to Find Insights and Patterns
- Machine Learning to Build of Analytic Models
- Application Integration with other Applications (Legacy, Cloud Services, …)
- Native Hardware Integration with Internet of Things Devices
Some use cases / real world examples:
- Banking: Data Discovery for compliance issues, fraud or other anomalies
- Stock / Energy Trading: Subcribe to events (e.g. price went over a threshold) – event correlation and proactive live UI
- Manufacturing / Internet of Things: Supply chain management with various partner companies (maybe even various blockchains)
- Many other use cases...
Thanks to my colleague Steven Warwick for implementing the StreamBase connectors and demo!
Streaming Analytics Comparison of Open Source Frameworks, Products, Cloud Ser...Kai Wähner
Streaming Analytics Comparison of Open Source Frameworks, Products and Cloud Services. Includes Apache Storm, Flink, Spark, TIBCO, IBM, AWS Kinesis, Striim, Zoomdata, ...
This session discusses the technical concepts of stream processing / streaming analytics and how it is related to big data, mobile, cloud and internet of things. Different use cases such as predictive fault management or fraud detection are used to show and compare alternative frameworks and products for stream processing and streaming analytics.
The focus of the session lies on comparing
- different open source frameworks such as Apache Apex, Apache Flink or Apache Spark Streaming
- engines from software vendors such as IBM InfoSphere Streams, TIBCO StreamBase
- cloud offerings such as AWS Kinesis.
- real time streaming UIs such as Striim, Zoomdata or TIBCO Live Datamart.
Live demos will give the audience a good feeling about how to use these frameworks and tools.
The session will also discuss how stream processing is related to Apache Hadoop frameworks (such as MapReduce, Hive, Pig or Impala) and machine learning (such as R, Spark ML or H2O.ai).
The digital transformation is going forward due to Mobile, Cloud and Internet of Things. Disrupting business models leverage Big Data Analytics and Machine Learning.
"Big Data" is currently a big hype. Large amounts of historical data are stored in Hadoop or other platforms. Business Intelligence tools and statistical computing are used to draw new knowledge and to find patterns from this data, for example for promotions, cross-selling or fraud detection. The key challenge is how these findings can be integrated from historical data into new transactions in real time to make customers happy, increase revenue or prevent fraud. "Fast Data" via stream processing is the solution to embed patterns - which were obtained from analyzing historical data - into future transactions in real-time.
This session uses several real world success stories to explain the concepts behind stream processing and its relation to Hadoop and other big data platforms. It discusses how patterns and statistical models of R, Spark MLlib, H2O, and other technologies can be integrated into real-time processing by using several different real world case studies. The session also points out why a Microservices architecture helps solving the agile requirements for these kind of projects.
A brief overview of available open source frameworks and commercial products shows possible options for the implementation of stream processing, such as Apache Storm, Apache Flink, Spark Streaming, IBM InfoSphere Streams, or TIBCO StreamBase.
A live demo shows how to implement stream processing, how to integrate machine learning, and how human operations can be enabled in addition to the automatic processing via a Web UI and push events.
Keywords: Big Data, Fast Data, Machine Learning, Analytics, Analytic Model, Stream Processing, Event Processing, Streaming Analytics, Real Time, Hadoop, Spark, MLlib, Streaming, R, TERR, TIBCO, Spotfire, StreamBase, Live Datamart, H20, Predictive Analytics, Data Discovery, Insights, Patterns
Un obra que analiza la estrategia seguida por el Real Madrid y el F.C.Barcelona para poder salir de una situación financiera compleja y ostentar su actual liderazgo
Antes de iniciar el contenido técnico de lo acontecido en materia tributaria estos últimos días de mayo; quisiera referirme a la importancia de una expresión tan sabia aplicable a tantas situaciones de la vida, y hoy, meritoria de considerar en el prefacio del presente análisis -
"no se extraña lo que nunca se ha tenido".
Con esta frase me quiero referir a las empresas que funcionan en las zonas de Iquique y Punta Arenas, acogidas a los beneficios de las zonas francas, y que, por ende, no pagan impuesto de primera categoría. En palabras técnicas estas empresas no mantienen saldos en sus registros SAC, y por ello, este nuevo Impuesto Sustitutivo, sin duda, es una tremenda y gran noticia.
Lo mismo se puede extender a las empresas que por haber aplicado beneficios de reinversión sumado a las ventajas transitorias de la menor tasa de primera categoría pagada; me refiero a las pymes en su mayoría. Han acumulado un monto de créditos menor en su registro SAC.
En estos casos, no es mucho lo que se tiene que perder.
Lo interesante, es que este ISRAI nace desde un pago efectivo de recursos, lo que exigirá a las empresas evaluar muy bien desde su posición financiera actual, y la planificación de esta, en un horizonte de corto plazo, considerar las alternativas que se disponen.
El 15 de mayo de 2024, el Congreso aprobó el proyecto de ley que “crea un Fondo de Emergencia Transitorio por incendios y establece otras medidas para la reconstrucción”, el cual se encuentra en las últimas etapas previo a su publicación y posterior entrada en vigencia.
Este proyecto tiene por objetivo establecer un marco institucional para organizar los esfuerzos públicos, con miras a solventar los gastos de reconstrucción y otras medidas de recuperación que se implementarán en la Región de Valparaíso a raíz de los incendios ocurridos en febrero de 2024.
Dentro del marco de “otras medidas de reconstrucción”, el proyecto crea un régimen opcional de impuesto sustitutivo de los impuestos finales (denominado también ISRAI), con distintas modalidades para sociedades bajo el régimen general de tributación (artículo 14 A de la ley sobre Impuesto a la Renta) y bajo el Régimen Pyme (artículo 14 D N° 3 de la ley sobre Impuesto a la Renta).
Para conocer detalles revisa nuestro artículo completo aquí BBSC® Impuesto Sustitutivo 2024.
Por Claudia Valdés Muñoz cvaldes@bbsc.cl +56981393599
PMI sector servicios España mes de mayo 2024LuisdelBarri
Estudio PMI Sector Servicios
El Índice de Actividad Comercial del Sector Servicios subió de 56.2 registrado en abril a 56.9 en mayo, indicando el crecimiento más fuerte desde abril de 2023.
El crédito y los seguros como parte de la educación financieraMarcoMolina87
El crédito y los seguros, son temas importantes para desarrollar en la ciudadanía capacidades que le permita identificar su capacidad de endeudamiento, los derechos y las obligaciones que adquiere al obtener un crédito y conocer cuáles son las formas de asegurar su inversión.
EL MERCADO LABORAL EN EL SEMESTRE EUROPEO. COMPARATIVA.ManfredNolte
Hoy repasaremos a uña de caballo otro reciente documento de la Comisión (SWD-2024) que lleva por título ‘Análisis de países sobre la convergencia social en línea con las características del Marco de Convergencia Social (SCF)’.
pablo LAMINAS A EXPONER PROYECTO FINAL 2023 sabado 28.10.23.pptxmarisela352444
Proyecto de PNF Contaduria de Diseño de herramientas en excel para mejorar el control de los registros contables de todas las operaciones relacionadas con las empresas
“La teoría de la producción sostiene que en un proceso productivo que se caracteriza por tener factores fijos (corto plazo), al aumentar el uso del factor variable, a partir de cierta tasa de producción
1. Ventures
Revisión Stress Test Bottom-up Wyman Banca Española
53.700 millones 81.300 millones 105.000 millones
Caso Adverso
El Caso Base y Adverso de Wyman Contemplan:
1. Ningún banco español va a repartir dividendo en cash entre 2012
y 2014.
2. La banca española reduce sus balances entre 2012-2014
a. Caso Base reducción en 173 mil M€
b. Caso Adverso reducción en 234 mil M€.
3. Robustez de capital “moldeable” según escenario.
- Se exige CET1 9% escenario base
- Cet1 del 6% en escenario adverso.
2 Octubre 2012 1
2. Ventures
Revisión Stress Test Bottom-up Wyman Banca Española
Detalle necesidades capital por entidad financiera
OW GB OW GB
Base Base Adverso Adverso
Santander 19.181 17.287 25.297 5.471
BBVA 10.945 9.554 11.183 -1.703
La Caixa 9.421 4.963 5.720 -4.799
Popular 677 -550 -3.223 -6.628
Sabadell 3.321 -904 915 -2.324
Bankinter 393 271 399 -919
Bankia -13.231 -16.449 -24.743 -29.802
Ibercaja Caja3 Liberbank 492 -1.787 -2.108 -5.472
CatalunyaCaixa -6.488 -6.493 -10.825 -11.330
NCG -3.966 -5.190 -7.176 -8.822
Unicaja CEISS 1.300 727 128 -1.774
Banco Valencia -1.847 -2.161 -3.462 -3.934
Mare Nostrum -368 -1.348 -2.208 -3.852
Kutxa 3.132 1.960 2.188 55
Necesidades Capital Total -25.901 -34.881 -53.744 -81.360
2 Octubre 2012 2
3. Ventures
Revisión Stress Test Bottom-up Wyman Banca Española
LA CAIXA
Bº Explotación La Caixa en 2011 fue 3.169M€. En escenario
adverso mejoran la media de 2011. Estimamos en escenario
La Caixa Base repite beneficio operativo 2011 hasta 2014, en adverso
mill. € % RWA reducción 15%
Capac. Generación Bº 3.489 2%
RWA 194.213 100% Objetivo del rescate es que la banca de crédito. Mantenemos
CET 1 18.696 10% el nivel del balance igual que 2011
2010 2011 2012e
Mantenemos nivel
Bº Explotación 3.681 3.169 3.050
solvencia en CET1
La Caixa Escenario Escenario La Caixa Escenario Escenario
Oliver Wyman Base Adverso Gurusblog Base Adverso
CET 1 2012 18.696 18.696 CET 1 2012 18.696 18.696
Pérdidas Estimadas -21.829 -32.733 Pérdidas Estimadas -21.829 -32.733
Provisiones Existentes 16.860 16.860
Provisiones Existentes 16.860 16.860
Bº Generados 2012-2014 12.161 10.919
Bº Generados 2012-2014 9.507 8.081
Impuestos -792 1.776
Impuestos -792 1.776
CET 1 2014 25.096 15.518
CET 1 2014 22.442 12.680
RWA 2014 174.163 163.296
% RWA 2014 14,4% 9,5% RWA 2014 = 2012 194.213 194.213
Desapalancamiento RWA 20.050 30.917 % RWA 2014 11,6% 6,5%
CET 1 Mínimo 9,0% 6,0% CET 1 Mínimo 9,0% 9,0%
Buffer Capital 9.421 5.720 Buffer Capital GB 4.963 -4.799
2 Octubre 2012 3
4. Ventures
Revisión Stress Test Bottom-up Wyman Banca Española
BBVA
BBVA
mill. € % RWA
Capac. Generación Bº 6.157 2% Según OW Beneficio 2012-2014 en caso adverso de La
RWA 336.944 100% Caixa cae un 10% y el de BBVA con menor peso en España
CET 1 32.299 10% un 14%
BBVA Escenario Escenario
BBVA Escenario Escenario
Oliver Wyman Base Adverso
Gurusblog Base Adverso
CET 1 2012 32.299 32.299
Pérdidas Estimadas -20.338 -31.297 CET 1 2012 32.299 32.299
Provisiones Existentes 10.019 10.019 Pérdidas Estimadas -20.338 -31.297
Esquema Protec. Activos 1.065 1.667 Provisiones Existentes 10.019 10.019
Bº Generados 2012-2014 16.742 14.414 Esquema Protec. Activos 1.065 1.667
Impuestos 92 2.961 Bº Generados 2012-2014 16.742 12.973
CET 1 2014 39.879 30.063 Impuestos 92 2.961
CET 1 2014 39.879 28.622
RWA 2014 321.486 314.664
% RWA 2014 12,4% 9,6% RWA 2014 = 2012 336.944 336.944
Desapalancamiento RWA 15.458 22.280 % RWA 2014 11,8% 8,5%
CET 1 Mínimo 9,0% 6,0% CET 1 Mínimo 9,0% 9,0%
Buffer Capital 10.945 11.183 Buffer Capital GB 9.554 -1.703
2 Octubre 2012 4
5. Ventures
Revisión Stress Test Bottom-up Wyman Banca Española
Santander
Santander
mill. € % RWA
Capac. Generación Bº 10.159 2%
RWA 560.310 100%
CET 1 54.517 10%
Santander Escenario Escenario
Santander Escenario Escenario
Oliver Wyman Base Adverso
Gurusblog Base Adverso
CET 1 2012 54.517 54.517
Pérdidas Estimadas -21.759 -34.069 CET 1 2012 54.517 54.517
Provisiones Existentes 12.030 12.030 Pérdidas Estimadas -21.759 -34.069
Esquema Protec. Activos 0 0 Provisiones Existentes 12.030 12.030
Bº Generados 2012-2014 25.063 23.806 Esquema Protec. Activos 1.065 1.667
Impuestos -2.136 864 Bº Generados 2012-2014 25.063 22.557
CET 1 2014 67.715 57.148 Impuestos -2.136 864
CET 1 2014 67.715 55.899
RWA 2014 539.265 530.844
% RWA 2014 12,6% 10,8% RWA 2014 = 2012 560.310 560.310
Desapalancamiento RWA 21.045 29.466 % RWA 2014 12,1% 10,0%
CET 1 Mínimo 9,0% 6,0% CET 1 Mínimo 9,0% 9,0%
Buffer Capital 19.181 25.297 Buffer Capital GB 17.287 5.471
2 Octubre 2012 5
6. Ventures
Revisión Stress Test Bottom-up Wyman Banca Española
Popular
Popular
mill. € % RWA
Capac. Generación Bº 1.802 2%
RWA 97.678 100%
CET 1 9.936 10%
Popular Escenario Escenario Popular Escenario Escenario
Oliver Wyman Base Adverso Gurusblog Base Adverso
CET 1 2012 9.936 9.936 CET 1 2012 9.936 9.936
Pérdidas Estimadas -15.078 -22.374 Pérdidas Estimadas -15.078 -22.374
Provisiones Existentes 7.767 7.767 Provisiones Existentes 7.767 7.767
Esquema Protec. Activos 0 0 Esquema Protec. Activos 0 0
Bº Generados 2012-2014 5.843 4.153
Bº Generados 2012-2014 5.406 4.595
Impuestos 210 2.239
Impuestos 210 2.239
CET 1 2014 8.678 1.721
CET 1 2014 8.241 2.163
RWA 2014 88.896 82.396
% RWA 2014 9,8% 2,1% RWA 2014 = 2012 97.678 97.678
Desapalancamiento RWA 8.782 15.282 % RWA 2014 8,4% 2,2%
CET 1 Mínimo 9,0% 6,0% CET 1 Mínimo 9,0% 9,0%
Buffer Capital 677 -3.223 Buffer Capital GB -550 -6.628
2 Octubre 2012 6
7. Ventures
Revisión Stress Test Bottom-up Wyman Banca Española
Sabadell
Sabadell
mill. € % RWA
Capac. Generación Bº 687 1%
RWA 79.418 100%
CET 1 8.747 11%
Sabadell Escenario Escenario
Sabadell Escenario Escenario
Oliver Wyman Base Adverso
Gurusblog Base Adverso
CET 1 2012 8.747 8.747
Pérdidas Estimadas -18.030 -25.347 CET 1 2012 8.747 8.747
Provisiones Existentes 13.124 13.124 Pérdidas Estimadas -18.030 -25.347
Esquema Protec. Activos 3.156 5.093 Provisiones Existentes 13.124 13.124
Bº Generados 2012-2014 3.756 3.093 Esquema Protec. Activos 3.156 5.093
Impuestos -1.098 268 Bº Generados 2012-2014 3.501 2.938
CET 1 2014 9.655 4.978 Impuestos -1.098 268
CET 1 2014 6.244 4.823
RWA 2014 70.382 67.270
% RWA 2014 13,7% 7,4% RWA 2014 = 2012 79.418 79.418
Desapalancamiento RWA 9.036 12.148 % RWA 2014 7,9% 6,1%
CET 1 Mínimo 9,0% 6,0% CET 1 Mínimo 9,0% 9,0%
Buffer Capital 3.321 915 Buffer Capital GB -904 -2.324
2 Octubre 2012 7
8. Ventures
Revisión Stress Test Bottom-up Wyman Banca Española
Bankia
Bankia
mill. € % RWA
Capac. Generación Bº 1.755 1% En escenario OW Bª Explotación Bankia parece
excesivamente castigado. Si va a ser prácticamente nulo o
RWA 164.613 100%
negativo mejor liquidar. Sensación se ha buscado cuadrar
CET 1 8.006 5% con las necesidades anunciadas. En 2011 beneficio
explotación Bankia fue de 421M€
Bankia Escenario Escenario
Bankia Escenario Escenario
Oliver Wyman Base Adverso
Gurusblog Base Adverso
CET 1 2012 8.006 8.006
Pérdidas Estimadas -29.593 -42.756 CET 1 2012 8.006 8.006
Provisiones Existentes 19.750 19.750 Pérdidas Estimadas -29.593 -42.756
Esquema Protec. Activos 0 0 Provisiones Existentes 19.750 19.750
Bº Generados 2012-2014 163 -2.236 Esquema Protec. Activos 0 0
Impuestos -1.060 -1.060 Bº Generados 2012-2014 1.263 1.074
CET 1 2014 -2.734 -18.296 Impuestos -1.060 -1.060
CET 1 2014 -1.634 -14.986
RWA 2014 116.638 107.624
% RWA 2014 -2,3% -17,0% RWA 2014 = 2012 164.613 164.613
Desapalancamiento RWA 47.975 56.989 % RWA 2014 -1,0% -9,1%
CET 1 Mínimo 9,0% 6,0% CET 1 Mínimo 9,0% 9,0%
Buffer Capital -13.231 -24.743 Buffer Capital GB -16.449 -29.802
2 Octubre 2012 8
9. Ventures
Revisión Stress Test Bottom-up Wyman Banca Española
Bankinter
Bankinter
mill. € % RWA
Capac. Generación Bº 462 2%
RWA 27.564 100%
CET 1 2.563 9%
Bankinter Escenario Escenario Bankinter Escenario Escenario
Oliver Wyman Base Adverso Gurusblog Base Adverso
CET 1 2012 2.563 2.563 CET 1 2012 2.563 2.563
Pérdidas Estimadas -2.039 -3.315 Pérdidas Estimadas -2.039 -3.315
Provisiones Existentes 859 859 Provisiones Existentes 859 859
Esquema Protec. Activos 0 0 Esquema Protec. Activos 0 0
Bº Generados 2012-2014 1.765 1.841 Bº Generados 2012-2014 1.545 1.313
Impuestos -176 142 Impuestos -176 142
CET 1 2014 2.972 2.090 CET 1 2014 2.752 1.562
RWA 2014 28.654 28.186
RWA 2014 = 2012 27.564 27.564
% RWA 2014 10,4% 7,4%
% RWA 2014 10,0% 5,7%
Desapalancamiento RWA -1.090 -622
CET 1 Mínimo 9,0% 9,0%
CET 1 Mínimo 9,0% 6,0% Buffer Capital GB 271 -919
Buffer Capital 393 399
2 Octubre 2012 9
10. Ventures
Revisión Stress Test Bottom-up Wyman Banca Española
CatalunyaCaixa
CatalunyaCaixa
mill. € % RWA
Capac. Generación Bº 279 1%
RWA 42.221 100%
CET 1 3.462 8%
CatalunyaCaixa Escenario Escenario CatalunyaCaixa Escenario Escenario
Oliver Wyman Base Adverso Gurusblog Base Adverso
CET 1 2012 3.462 3.462 CET 1 2012 3.462 3.462
Pérdidas Estimadas -12.518 -17.230 Pérdidas Estimadas -12.518 -17.230
Provisiones Existentes 5.808 5.808 Provisiones Existentes 5.808 5.808
Esquema Protec. Activos 0 0 Esquema Protec. Activos 0 0
Bº Generados 2012-2014 77 -760 Bº Generados 2012-2014 837 711
Impuestos -282 -282 Impuestos -282 -282
CET 1 2014 -3.453 -9.002 CET 1 2014 -2.693 -7.531
RWA 2014 33.721 30.376
RWA 2014 = 2012 42.221 42.221
% RWA 2014 -10,2% -29,6%
% RWA 2014 -6,4% -17,8%
Desapalancamiento RWA 8.500 11.845
CET 1 Mínimo 9,0% 9,0%
CET 1 Mínimo 9,0% 6,0% Buffer Capital GB -6.493 -11.330
Buffer Capital -6.488 -10.825
2 Octubre 2012 10