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About Blockchain & Machine Learning Conferences

About Blockchain & Machine Learning Conferences

Machine Learning & AI in Quantitative Finance Conference, London: 15th, 16th & 17th November 2017

SPECIAL OFFERS: 10% Early Bird Discount Until Friday 20th October 2017. When 2 colleagues attend the 3rd goes free! Main Conference + Workshop (£150 Discount)

Workshop Day: Wednesday 15th November: Machine Learning in Finance : A Practical View by Miquel Noguer Alonso: UBS & Columbia University

Topics:

Predictive Power vs. Expressiveness of Machine Learning Models
Challenges with opaque ML models
Machine Learning in Quantitative Finance
Changing role of quants – from derivatives modellers to data scientists
Machine learning in finance - Practice
Black-box Machine Learning: Improving Transparency
Learning Curve Dynamics with Artificial Neural Networks
Machine Learning, High-Frequency Trading and Kdb+/q for Quants and Data Scientists
Machine learning - Deep learning
What are the modelling applications which benefit from deep neural networks?
Financial Singularity -- Paths, Dangers, Strategies
Unsupervised Anomaly Detection in Finance
Financial Time-Series Regime Detection
What new insights can Machine Learning offer into the analysis of financial time series?
Machine Learning at Central Banks
Machine Learning and Regulation: From Regulating Machines to Regulation by Machines




Blockchain Developments in Financial Markets Conference London: 23rd & 24th November 2017:

This event have been built in collaboration with some of the world’s leading banks, financial institutions and technology companies to ensure that the most pressing and pertinent issues are covered. Topics ranging across trading, fixed income, derivatives, syndicated loans and trade finance, identity and payments. Discussion for leading figures on both the business and technology sides of the global banking industry.

Topics:

Distributed Ledger Technology in Financial Markets, Smart Contracts Standards, Decentralised Clearing Network, Decentralization in Financial Markets, Implementation Strategies, KYC Blockchain Implementation, Regulatory and Compliance Challenges, Distributed Ledger Regulations & Compliance.

Who will attend?

CEOs, Chairmen, CTOs, CIOs, Managing Directors, Global Heads, Heads of Desks, Heads of Innovation and teams, Traders, Quants, Structurers, FinTech companies and more besides.

Benefits of attending:

  • Boost your contact list
  • The event will bring together the tech and financial worlds to create a framework for one to solve the needs of the other.
  • It will be a perfect place to network and do business with everyone you need, under one roof.
  • Keep up to date with this rapidly developing arm of the industry.
  • Join the debate
WBS Training are pleased to announce our latest dynamic conferences in the ever changing world of financial technology. The fees are set low so that in a world of tight budgets all can attend.

Limited places so book early!

Conference Speakers

Machine Learning & AI in Quantitative Finance Conference 15-17 November, Kingsway Hall Hotel, London

SPECIAL OFFERS: 10% Early Bird Discount Until Friday 20th October 2017. When 2 colleagues attend the 3rd goes free!

Pre-Conference Workshop Day: Wednesday 15th November

Machine Learning in Finance : A Practical View by Miquel Noguer Alonso, Executive Director, UBS & Adjunct Assistant Professor, Columbia University

Outline:

    • Using machine learning in the new financial markets big data landscape
    • Big Data in Finance Landscape
    • Infrastructure and technologyData sources
    • Modern data analysis – Structured and Unstructured Data & New Models
    • Classical and advanced models
    • Machine Learning models in practice
    • Machine learning robust modeling
    • The future of machine learning in finance

09.30 Registration and Morning Welcome Coffee


10.00 – 11.00: Big Data in Finance Landscape

 

  • Big data in finance landscape: Financial modeling, data governance, integration, NoSQL, batch and real-time computing and storage
  • Infrastructure and technology
  • New data sources
  • Modern data analysis: Structured / Unstructured data and new models

11.00 –  11.15: Morning break


11.15 – 12.30: Machine Learning Models

 

  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning
  • Deep learning
  • Advanced machine learning models

12.30 – 13.30: Lunch


13.30 – 15.00 Machine learning in finance – Practice

 

  • Momentum and Mean Reversion
  • Sentiment Analysis
  • Asymmetric Trading Strategies
  • Non Linear Multi-Factor Models
  • High Frequency Trading
  • Advanced Machine Learning

15.00 –  15.30: Afternoon break


15.30 – 16.30: Machine learning in finance – Opportunities and challenges

 

  • Algo-Grading 101
  • Interpretation
  • Data mining biases: overfitting, survivorship and data-snooping
  • Robust trading strategies
  • The future of machine learning in finance

Course Tutor:


Miquel Noguer Alonso, Executive Director, UBS & Adjunct Assistant Professor, COLUMBIA UNIVERSITY
Miquel Noguer i Alonso is a financial markets practitioner with more than 20 years of experience in asset management, he is currently working for UBS AG (Switzerland). He worked as a CFO and CIO for a European bank from 2000 to 2006. He started his career at KPMG.

He is Adjunct Assistant Professor at Columbia University teaching Asset Allocation, Big Data in Finance, Fintech and Hedge Fund Professor at ESADE. He received an MBA and a Degree in business administration and economics in ESADE in 1993. In 2010 he earned a PhD in quantitative finance with a Summa Cum Laude distinction (UNED – Madrid Spain). He also holds the Certified European Financial Analyst diploma ( 2000 ).

His research interests range from asset allocation, big data to algorithmic trading and fintech. His academic collaborations include a visiting scholarship in Columbia University in 2013 in the Finance and Economics Department, in Fribourg University in 2010 in the mathematics department, and presentations in Indiana University, ESADE, London Business School, CAIA Association, AFI and several industry seminars.

Day 1: Machine Learning & AI in Quantitative Finance Conference

BROCHURE

Location: Kingsway Hall Hotel, London

Thursday 16th November

08:30: Registration and Morning Welcome Coffee

Stream Chairman:Andres Hernandez (Machine Learning)

Andres Hernandez: Manager, Financial Services Risk Consulting,PwC

 

 


09.00 – 10.00:  Keynote SpeechO. Ediz Ozkaya: (Machine Learning)

Presenter: O. Ediz Ozkaya: Executive Director, Machine Learning Strategist, Securities Division, Goldman Sachs

Overcoming the Trade-off: Predictive Power vs. Expressiveness of Machine Learning Models

  • Challenges with opaque ML models
  • Controlling model behaviour in relation to tail risk
  • Expressive regularising models, native prediction confidence
  • Improving model expressiveness
  • Readiness for increasing model complexity

10.00 – 10.45: “Black-box Machine Learning: Improving Transparency”.Abdel Lantere

“Many of the state of the art machine learning applications are based on black-box models which are difficult to interpret and explain. With more ML-based models being integrated into live decision-making systems, new challenges will be faced by various functions within banks as well as by the regulators. This talk disucsses the challenges faced and presents techniques to help provide more transparency and better understanding of the results of a given ML black-box model.”

Presenter: Abdel Lantere, Data Scientist, Quantitative Consultant, HSBC


10.45 – 11.15: Morning Break and Networking Opportunities


11.15 – 12.00: Machine Learning in Quantitative FinanceDr. Miquel Noguer Alonso

  • Short history of machine learning
  • Data usage across a typical trading business
  • What a data analytics platform should look like for trading
  • Changing role of quants – from derivatives modellers to data scientists
  • Machine learning in finance – Practice

Presenter: Miquel Noguer Alonso: Executive Director, UBS & Adjunct Assistant Professor, Columbia University


12.00 – 12.45: Financial Time-Series Regime Detection

  •   Methodological framework
  •   Tools from machine learning and complex systems
  •   Applications in trading and risk management

Presenter: Topic to be confirmed


12.45- 13.45: Lunch


13.45 – 14.30: Reliable Machine Learninglawrence.png

Presenter: Lawrence Edwards: Executive Director, Morgan Stanley

 

 

 


14.30 – 15.15: Fast Pricing of Derivatives with Machine Learning Tools

Ignacio Ruiz

  • Financial optimisation problems via “Smart” interpolation schemes
  • Machine Learning made possible with MoCaX fast pricing solution
  • MoCaX Smart grids via parametric interpolations
  • Examples
  • Dimensionality reduction with ML techniques

Presenter: Ignacio Ruiz: Founder & CEO, MoCaX Intelligence


15.15 – 15.45: Afternoon Break and Networking Opportunities


15.45 – 16.30: Learning Curve Dynamics with Artificial Neural Networks Alexei Kondratyev

  • Relative performance of ANN, PCA and parametric models in predicting possible future curve shapes
  • Finding natural curve shapes and their transformations
  • Extraction and preservation of information about curve moves from the training dataset
  • Short-term and long-term curve dynamics; dissipation of information derived from the initial term structure

Presenter: Alexei Kondratyev: Managing Director Financial Markets, Standard Chartered Bank


16.30 – 17.15: Model Calibration with Neural Networks Andres Hernandez (Machine Learning)

  • Advantages and limitations of using neural networks to calibrate models
  • Requirements for applying neural networks to calibrate models
  • Sample set generation: multi-normal distribution, variational autoencoders
  • 1-factor Hull-White calibration: local optimizer vs neural network
  • 2-factor Hull-White calibration: global optimizer vs neural network
  • Towards the calibration of a local stochastic volatility model with neural networks
  • A recurrent neural network as a better optimizer

Presenter: Andres Hernandez: Manager, Financial Services Risk Consulting, PwC


17.15 – 18.00: Machine Learning & Ai in Quantitative Finance Panel

Moderator:

  • Paul Bilokon: Founder, CEO,Thalesians, Senior Quantitative Consultant, BNP Paribas & Visiting Lecturer, Imperial College

Panelists:

  • O. Ediz Ozkaya: Executive Director, Machine Learning Strategist, Securities Division, Goldman Sachs
  • Alexei Kondratyev: Managing Director Financial Markets, Standard Chartered Bank
  • Claudi Ruiz Camps: Machine Learning, Deep Learning Specialist,ABN AMRO Clearing Bank N.V.
  • Daniel Drummer: Vice President, Corporate & Investment Bank FinTech, J.P. Morgan
  • Abdel Lantere: Data Scientist, Quantitative Consultant, HSBC
  • Ignacio Ruiz: Founder & CEO, MoCaX Intelligence

Topics:

  • What is the current state of utilisation of machine learning in finance?
  • What are the distinct features of machine learning problems in finance compared to other industries?
  • What are the best practices to overcome these difficulties?
  • What’s the evolution of a team using machine learning in terms of day to day operations?
  • What is a typical front office ‘Quant’ skillset going to look like in three to five years time?
  • How do we deal with model risk in machine learning case?
  • How is machine learning expected to be regulated?
  • What applications can you list among its successes?
  • How much value is it adding over and above the “classical” techniques such as linear regression, convex optimisation, etc.?
  • Do you see high-performance computing (HPC) as a major enabler of machine learning?
  • What advances in HPC have caused the most progress?
  • What do you see as the most important machine learning techniques for the future?
  • What are the main pitfalls of using Machine Learning currently in trading strategies?
  • What new insights can Machine Learning offer into the analysis of financial time series?
  • Discuss the potential of Deep Learning in algorithmic trading?
  • Do you think machine learning and HPC will transform finance 5-10 years from now?
  • If so, how do you envisage this transformation?
  • Can you anticipate any pitfalls that we should watch out for.

Day 2: Machine Learning & AI in Quantitative Finance Conference

BROCHURE

Location: Kingsway Hall Hotel, London

Friday 17th November

08:30: Morning Welcome Coffee

Stream Chairman:Yves Hilpisch

Yves J. Hilpisch: Founder, The Python Quants

 

 

 


09.00 – 09.45: Machine Learning, High-Frequency Trading and Kdb+/q for Quants and Data Scientists Paul Bilokon, PhD

Abstract: Kdb+/q is a de-facto standard technology among the high-frequency trading firms and market makers. The underlying programming language, q, is based on Ken Iverson’s idea of efficient notation, which enables one to deal with financial data extremely efficiently and with minimum effort. We present excerpts from our forthcoming book and show how big financial data and machine learning can be handled efficiently in q and give several examples – from neural nets, to classifiers, and feature extraction for alpha generation.

Presenter: Paul Bilokon: Founder, CEO,Thalesians, Senior Quantitative Consultant, BNP Paribas & Visiting Lecturer, Imperial College


09.45 – 10.30: Machine learning – Deep learning

  • What are the modelling applications which benefit from deep neural networks?
  • What are the implications of using deep probabilistic programming for trading and risk management?
  • What are the trade-offs between using other techniques such as Kalman filtering?
  • What are some of the challenges with using open-source tools, such as TensorFlow, for trading applications?
  • Have we seen examples of successful application of Q-learning (reinforcement learning) to trading?
  • Other topics of interest by prop trading firms is “speed versus analytics” – the cost to be the fastest in the market is pushing some firms to be “smarter” and explore alternative strategies involving price prediction

Presenter:To be confirmed


10.30 – 11.00: Morning Break and Networking Opportunities


11.00 – 11.45: Topic to be confirmedRoland Fejfar

Presenter: Roland Fejfar: Vice President, FinTech IBD, Morgan Stanley

 

 

 


11.45 – 12.30: Financial Singularity — Paths, Dangers, Strategies Yves Hilpisch

Investopedia defines: “Financial singularity is the point at which all investment decisions are made by intelligent machines rather than human agents.” This talk explores the potential for a Strong Financial AI, an artificially intelligent machine that has access to all relevant financial data to make investment decisions based on learning and self-updating algorithms. In this regard, it touches on different recent advances both on the software as well as on the hardware side. Is it possible to build a machine that beats the financial market with certainty?

Presenter: Yves J. Hilpisch: Founder, The Python Quants 


12.30- 13.30: Lunch


13.30 – 13.50: Co-creating Machine Learning solutions within a global Corporate & Investment BankDaniel Drummer, CFA, LL.M.

  • The Challenge – Why banks and data science startups can often benefit from cooperating with each other.
  • The Opportunity – How JP Morgan is co-creating ML  solutions with startups via the In-Residence program
  • Case study – Experiences and lessons learned from day-to-day cooperation with machine learning startups

Daniel Drummer: Vice President, Corporate & Investment Bank FinTech, J.P. Morgan


13.50 – 14.45: Unsupervised Anomaly Detection in FinanceMarleen MeierClaudi Ruiz Camps

‘ABN AMRO Clearing Bank works with considerably large amounts of data every day and we design and implement Deep Learning models to approach  some of our business cases. One example is, how to find real time anomalies (strange behaviors) in our data by using Unsupervised Anomaly Detection with TensorFlow and Spark. The output is being visualized with Tableau in order to express the anomalies and to make data-driven business decisions.’

Presenter: Claudi Ruiz Camps: Machine Learning, Deep Learning Specialist, & Marleen Meier: Quantitative Risk Analyst, Data Visualization, ABN AMRO Clearing Bank N.V.


14.45 – 14.50: Quick Afternoon Break


14.50 – 15.35: Machine Learning at Central Banks (to be confirmed) 

Abstract:

We introduce machine learning in the context of central banking and policy analyses. Our aim is to give an overview broad enough to allow the reader to place machine learning within the wider range of statistical modelling and computational analyses, and provide an idea of its scope and limitations. We review the underlying technical sources and the nascent literature applying machine learning to economic and policy problems. We present popular modelling approaches, such as artificial neural networks, tree-based models, support vector machines, recommender systems and different clustering techniques. Important concepts like the bias-variance trade-off, optimal model complexity, regularisation and cross-validation are discussed to enrich the econometrics toolbox in their own right. We present three case studies relevant to central bank policy, financial regulation and economic modelling more widely. First, we model the detection of alerts on the balance sheets of financial institutions in the context of banking supervision. Second, we perform a projection exercise for UK CPI inflation on a medium-term horizon of two years. Here, we introduce a simple training-testing framework for time series analyses. Third, we investigate the funding patterns of technology start-ups with the aim to detect potentially disruptive innovators in financial technology. Machine learning models generally outperform traditional modelling approaches in prediction tasks, while open research questions remain with regard to their causal inference properties.

Presenter: Bank of England (to be confirmed)


15.35 – 16.15: Machine Learning and Regulation: From Regulating Machines to Regulation by MachinesKwasi Affum

Presenter: Kwasi Affum: Vice President, Regulatory Impact Assessment, Barclays Investment Bank

Blockchain Developments in Financial Markets Conference

Full Conference Fee: £545 + VAT, Attend Conference Day 1 or 2 Only: £295 + VAT

Day 1 – Regulatory & Compliance Developments, Smart Contracts, Digital Identity & ICOs

Thursday 23rd November

BROCHURE

08:30: Registration and Morning Welcome Coffee


09:00 – 10:00 Keynote Speech: Taking Finance to Blockchain Massimo Morini (Blockchain)

Derivative Products on the Public Blockchain

  • A new market design to minimize risk by technology
  • What stays onchain, what goes offchain
  • Providing the services the market needs. What ICOs are offering.

Redesigning Clearing and CCPs on Private Blockchains

  • Transparency and Auditability by Smart Contracts
  • How to design central clearing without centralization
  • The need for digital versions of fiat currencies

Presenter: Massimo Morini: ‎Head of Interest Rate and Credit Models, Gruppo Intesa Sanpaolo


10.00 – 10.45: Regulatory Challenges Facing Fintech Companies and Banks Image result for Adedayo Banwo: Senior Counsel, Deutsche Bank

Presenter: Adedayo Banwo: Senior Counsel, Deutsche Bank

 

 


10.45 – 11.15: Morning Break and Networking Opportunities


11.15 – 12.00: Data & Process Standards for Smart Contracts Tim Smith (Blockchain)

Presenter: Tim Smith: Consultant, Credit Suisse (To be confirmed)

 

 

12.00 – 12.45: “Designing Standards for Smart Contracts” Panel

Moderator:Lee Braine (Blockchain)

  • Lee Braine: Investment Bank CTO Office, Barclays

 

Panelists:

  • Massimo Morini: Head of Interest Rate and Credit Models, Gruppo Intesa Massimo Morini (Blockchain)Sanpaolo

 

 

  • Tim Smith: Consultant, Credit Suisse (To be confirmed)
  • Yiseul Cho: Data Science, Blockchain, HSBCYiseul Cho (Blockchain)

 

 

 

  • Adedayo Banwo: Senior Counsel, Deutsche BankAdedayo Banwo (Blockchain)

 

Topics:

  • Smart Contracts: how should law react to self-executing agreements?
  • What it takes for a Smart Contract to be also a legal Contract?
  • Tokens and Smart Contract. How should ICO be designed to be considered Legal?
  • Smart Contracts and Dapps on the Public Blockchains: anyhting good in the recent hype?
  • Smart Contracts on the Private Blockchains: do we have three Champions that will rule the world?
  • What is the current state of play with architecture standards for smart contracts?
  • Why are business process standards necessary for smart contracts?
  • Why types of data standards are required for smart contracts?
  • Do you foresee trade associations (such as ISDA, FIA, BAFT, etc) supporting smart contracts standards?

12.45 – 13.45: Lunch 


13.45 – 14.30: Topic to be confirmedMartin Bartlam (Blockchain)

Presenter: Martin Bartlam: Head of International Finance, DLA Piper


 


14.30 – 15.15: The Crypto Finance Scene Yiseul Cho (Blockchain)

  • Blockchain data project
  • Global trends
  • Building a quantitative tool to assess the risks around ICOs 

Presenter: Yiseul Cho: Partner, Blockchain Partners Korea


15.15 – 15.45: Afternoon Break and Networking Opportunities


15.45 – 16.30: Blockchain … Blockbuster or Hype? Patrick Strauss (Blockchain)

Presenter: Patrick Strauss: Asso VP – Head of Digital Supply Chain Practice, Internet of Things and Blockchain, Antuit

 

 

 


16.30 – 17.15: How to Begin the Transition to Blockchain Image result for Michele Curtoni

  • Taking first steps on the road to Blockchain
  • How should companies go about making the transition?
  • Key considerations and potential challenges

Presenter: Michele Curtoni: Emerging Technology Strategy, London Stock Exchange Group  

Day 2 – DLT (From PoC to Large Scale Implementation), Clearing and Settlements & Pricing for Cryptocurrencies

Friday 24th November

BROCHURE

08:30: Registration and Morning Welcome Coffee


09.00 – 09.45: Keynote Speech: Distributed Ledger Technology: Clearing and SettlementImage result for Keith Bear ibm

  • Blockchain in Financial Markets: Bridging the Divide
  • Use Case Analysis
  • Governance and Consortium: the challenges
  • Turning PoC’s into Industrialised distributed networks
  • Lessons learnt from early Financial Markets deployments 

Presenter: Keith Bear: VP, Global Financial Markets, Global Markets, IBM


09:45 – 10:30: Using Blockchain to Set New Standards in Post Trade SettlementsHugh Halford-Thompson (Blockchain)

  • How blockchain streamlines post-trade
  • Deploying blockchain at scale
  • Use cases in energy and finance
  • Blockchain’s advantages in setting industry-wide standards
  • What might adoption of blockchain look like

Presenter: Hugh Halford-Thompson: CIO, BTL Group LTD


10.30 – 11.00: Morning Break and Networking Opportunities


11:00 – 11.45: Panel – Challenges the Industry is Experiencing Taking DLT from PoC to Large Scale Production Implementations.

Image result for Keith Bear ibm

Moderator:

  • Keith Bear: VP, Global Financial Markets, Global Markets, IBM

Panelists:

  • Anthony Woolley: MD, UK CIO, Societe Generale
  • Richard Crook: Head of Innovation Engineering, RBSRichard Crook (Blockchain)

 

 

  • Adriano Bertini: Vice President – Electronic Trading Technology, Bank of America Merrill LynchAdriano Bertini (Blockchain)

 

 

  • Hugh Halford-Thompson: CIO, BTL Group LTDHugh Halford-Thompson (Blockchain)

 

 

Topics:

  • Technological issues
  • Centralized vs. Decentralized Blockchain
  • Commercial structures of consortia and associated operations
  • Legal and regulatory considerations

11.45 – 12.30: Pricing and Risk in a CryptoTech World Chris Kenyon (Blockchain)

  • CryptoTech is about information and coordination, not pricing but pricing and risk are influenced by coordination.
  • XVA and coordination

Presenter: Chris Kenyon: Head of XVA Quantitative Research, Financial Markets, Lloyds Banking Group


12.30 – 13.30: Lunch 


13.30 – 14.15: How Blockchain is Changing Traditional Venture Capital and Capital Markets Alexander Shelkovnikov (Blockchain)

  • Technology financing landscape has started to shift significantly over the past couple of years
  • There are some key advantages of blockchain technology which are driving this shift
  • Why decentralised technology and transparency is going to win long term
  • What does it mean for venture capital and the world of capital markets

Presenter: Alexander Shelkovnikov: Founder, Semantic Capital


14.15 – 15.00: How FinTech is Shaping Financial Services Image result for Steve Davies: Head of FinTech EMEA, PwC

Presenter: Steve Davies: Head of FinTech EMEA, PwC (To be confirmed)

 

 

 


15.00 – 15.15: Afternoon Break and Networking Opportunities


15.15 – 16.00: Presenter to be confirmed

The Future of OTC Derivatives: Using Blockchain Post-Trade Services

Sponsors

There are limited sponsorship opportunities for this event so please contact neil@wbstraining.com
Featured Image

MoCaX Intelligence is a new-to-the-market algorithm that accelerates existing Risk Engines without the need for complex systems development or expensive hardware upgrades. MoCaX removes the pricing step bottle-neck that often uses over 90% of computational effort in existing engines and increases capabilities by several orders of magnitude with no loss of accuracy.

MoCaX builds on the new Algorithmic Pricer Acceleration (APA) and Algorithmic Greeks Acceleration (AGA) methods. APA synthesises your existing pricers and creates an accelerated version of them. Even your very slowest and complex pricer, passed through MoCaX, will return the same results (down to 10-15 precision) ultra-fast (up to a few nanoseconds). For example, this enables highly accurate Monte Carlo within Monte Carlo in an instant.

Please ask for a free version of MoCaX so you can test it for yourself: i.ruiz@iruiztechnologies.com

Featured Image

CompatibL is a software application, analytics and consultancy company specializing in Regulation and market and counterparty risk such as FRTB and all XVAs, for which for which it was the winner of Risk magazine’s 2017 award for Specialist Market Risk Vendor of the Year. Compatibl not only offers turnkey solutions for XVA and regulatory needs, but CompatibL’s consultancy teams additionally offer FRTB and pricing / XVA Model validation and development.
CompatibL is at the forefront of many important industry innovations and trends around the trading and risk space, including Adjoint Algorithmic Differentiation (AAD), a proven technique for delivering massive performance gains for the calculation of sensitivities and capital measures.
With a team of over 200 experienced developers and financial engineers, CompatibL has implemented more than 70 major projects across a client base of over 50 banks, central banks, Supranationals and asset managers in the US, EMEA and Asia, including 4 out of 5 largest derivatives dealers.

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TriOptima provides risk management services for OTC derivatives, reducing costs and eliminating operational and credit risk through a range of services.

triResolve for proactive reconciliation of OTC derivative portfolios, repository validation and dispute resolution

triReduce for multilateral portfolio compression services across OTC product types

triBalance for rebalancing counterparty risk exposure between multiple CCPs and bilateral relationships

triCalculate for the complete spectrum of counterparty credit risk analytics leveraging state-of-the-art massively parallel computing devices

TriOptima maintains offices in London, New York, Singapore, Stockholm, and Tokyo.

Media Partner (Blockchain)

Media Partner (Blockchain)

Distributed:

Distributed produces a print magazine, online news portal, international event series and weekly newsletter covering enterprise-level blockchain technology. Our products provide professionals with the intelligence, support and expertise they need to navigate the distributed ledger industry.

We explore the business cases, technical considerations, regulatory approaches and projects that will shape the future of industry. Our global team combines critical information with authoritative insight, bringing you closer to the people, projects and perspectives behind this digital revolution.

https://distributed.com/

Featured Image

Over the years, financial professionals around the world have looked to Wiley and the Wiley Finance series with its wide array of bestselling books for the knowledge, insights, and techniques that are essential to success in financial markets. As the pace of change in financial markets and instruments quickens, Wiley continues to respond.



Media Partner (Blockchain)

Media Partner (Blockchain)

Brave New Coin (BNC) is a Data & Research company who has built a superior Market-Data Engine for the Blockchain & Digital Equities industry. We collect, process & index live data from over 100 trading platforms in real-time to produce a number useful data tools for Developers, Traders & Enterprise. 



Media Partner (Blockchain)

Media Partner (Blockchain)

CIOReview is a technology magazine that talks about the enterprise solutions that can redefine the business goals of enterprises tomorrow. It is the leading source that shares innovative enterprise solutions developed by established solutions providers, upcoming hot enterprises and is a neutral source for technology decision makers. Published from Fremont, California, CIOReview is an excellent platform for the enterprise to showcase their innovative solutions.

Media Partner (Blockchain)

Media Partner (Blockchain)

Connecting the Financial Services and Commodities Technology Community, Harrington Starr work with both the world's leading brands and the best talent in the sector to help our customers become more productive and successful. Through talent and opportunity identification, network introductions, thought leading content and a series of events we work hard to ensure added value in every point of contact with our clients.