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About

Speakers

Machine Learning & AI in Quantitative Finance Conferences: New York City & London 2018

Pre-Conference Workshop Days:

NEW YORK BROCHURE

New York City: Wednesday February 28th 2018
London: Wednesday 14th March 2018

Machine Learning in Finance: A Practical View by Miquel Noguer Alonso: 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

08.30 Registration and Morning Welcome Coffee

Workshop Timings: 9.00 – 15.30


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

Machine Learning Models

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

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

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, 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.


BOOK NEW YORK              BOOK LONDON


09.00 – 09.45:  Keynote Speech

O. Ediz Ozkaya: (Machine Learning)

Presenter: O. Ediz Ozkaya: Executive Director, Machine Learning Labs, Securities, 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

09.45 – 10.30Machine Learning: History and Implications for Quantitative Finance WILLIAM MCGHEE

  • Historical overview of Machine Learning – from MENACE to Alpha Go Zero
  • Implications for Quantitative Analytics
    • Changing role of Quants
    • Building a Machine Learning framework
    • Model Governance

Presenter: William McGhee: Global Head of Quantitative Analytics, NatWest Markets


10.30 – 11.00: Morning Break and Networking Opportunities


11.00 – 11.45: Topic to be confirmed

Presenter: Daniel Giamouridis: Global Head of Scientific Implementation, Bank of America Merrill Lynch 

 

 


11.45 – 12.30: “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 


12.30- 13.30: Lunch


13.30 – 14.15: Machine Learning Models Dr. Miquel Noguer Alonso

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

Presenter: Miquel Noguer Alonso: Adjunct Assistant Professor, Columbia University


14.15 – 15.00: Fast MVA Optimisation using Chebyshev Interpolants 

  • MoCaX Smart grids based on Chebyshev spectral decomposition
  • Machine Learning accelerated with MoCaX fast pricing
  • Application: MVA optimisation in real time. With the massive acceleration to compute Greeks with MoCaX, it is possible to evaluate a Monte Carlo simulation of SIMM in fractions of seconds. This in turn makes it possible to revalue an MVA objective function as frequently as required by the optimisation algorithms.

Presenter: Mariano Zeron: Head of R&D, MoCaX Intelligence & Andrés Hernández: Manager, Financial Services Risk Consulting, PwC


15.00 – 15.30: Afternoon Break and Networking Opportunities


15.30 – 16.15: Reliable Machine Learning 

  • Robustness
  • Awareness
  • Adaptation
  • Value learning
  • Monitoring

Presenter: Lawrence Edwards: Executive Director, Morgan Stanley (to be confirmed)


16.15 – 17.15: 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 Labs, Securities, Goldman Sachs
  • Daniel Giamouridis: Global Head of Scientific Implementation, Bank of America Merrill Lynch 
  • Miquel Noguer Alonso: Adjunct Assistant Professor, Columbia University
  • 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: Friday 16th March 2018

08:30: Registration and Morning Welcome Coffee


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 – 10.45Unsupervised Anomaly Detection in Finance 

Claudi 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.


10.45 – 11.15: Morning Break and Networking Opportunities


11.15 – 12.00: Practical Aspects of Applying Deep Learning for Market Making 

Presenter: Oded Luria: Data Scientist, Citi (To be confirmed)  


12.00 – 12.45: “Applications & Challenges of using Deep Learning & Bayesian Inference Methods for High Frequency Market Making”

 

 

Presenters: Farhan Feroz: eFX Quantitative Trader & Pawel Chilinski: Quantitative Trader, UBS


12.45- 13.45: Lunch


13.45 – 14.15: 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

Presenter: Daniel Drummer: Vice President, Corporate & Investment Bank FinTech, J.P. Morgan (to be confirmed)


14.15 – 15.00: Using Big Data to Trade FX (& Python for finance)  Saeed Amen

  • Discussion of what Big Data is with financial examples
  • Brief overview of machine learning
  • Case study on using machine readable Bloomberg News to trade FX
  • Python for financial analysis with interactive demo

 Presenter: Saeed Amen: Quant strategist & trader, Cuemacro


15.00 – 15.15: Afternoon Break


15.15 – 16.00: “Can AI help FRTB?”

“Time Series Data & FRTB – time to get it right”

Presenter: John Barclay: Managing Director, RiskTensor

 


 

New York City: March 1st & 2nd, Machine Learning & AI in Quant Finance Conference

NEW YORK BROCHURE

  • 25% Super Early Bird Discount Until Friday, January 26th 2018
  • When 2 colleagues attend the 3rd goes free
  • Main Conference + Workshop ($250 Discount)


D
ay 1: Thursday March 1st 2018
08:30: Registration and Morning Welcome Coffee


09.00 – 09.45:  Keynote Speech

O. Ediz Ozkaya: (Machine Learning)

Presenter: O. Ediz Ozkaya: Executive Director, Machine Learning Labs, Securities, 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

9.45 – 10.30Topic to be confirmed 

Presenter: Daniel Giamouridis: Global Head of Scientific Implementation, Bank of America Merrill Lynch 

 

 


10.30 – 11.00: Morning Break and Networking Opportunities


11.00 – 11.45: “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


11.45 – 12.30: Machine Learning – Recent Trends and Applicability to Risk and Related Areas (To be confirmed) 

  • Supervised, unsupervised, Reinforcement
  • Deep learning, feature Learning, incremental learning
  • Predictive power and robustness

Presenter: Suhail Shergill: Head of R&D and Innovation Lead, Scotiabank (To be confirmed) 


12.30- 13.30: Lunch


13.30 – 14.15: Applying Machine Learning to Evaluate Systemic Risk and Contribution of Individual SIFIs

Presenter: To be confirmed


14.15 – 15.00: Fast MVA Optimisation using Chebyshev Interpolants 

  • MoCaX Smart grids based on Chebyshev spectral decomposition
  • Machine Learning accelerated with MoCaX fast pricing
  • Application: MVA optimisation in real time. With the massive acceleration to compute Greeks with MoCaX, it is possible to evaluate a Monte Carlo simulation of SIMM in fractions of seconds. This in turn makes it possible to revalue an MVA objective function as frequently as required by the optimisation algorithms.

Presenter: Mariano Zeron: Head of R&D, MoCaX Intelligence 


15.00 – 15.30: Afternoon Break and Networking Opportunities


15.30 – 16.00: Practical Aspects of Applying Deep Learning for Market Making

Presenter: To be confirmed


16.00 – 16.30: Machine Learning Models Dr. Miquel Noguer Alonso

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

Presenter: Miquel Noguer Alonso: Adjunct Assistant Professor, Columbia University


16.30 – 17.15: 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 Labs, Securities, Goldman Sachs
  • Miquel Noguer Alonso: Executive Director, UBS & Adjunct Assistant Professor, Columbia University
  • Rajesh T. Krishnamachari: Vice President, Quantitative and Derivatives Strategy, J.P. Morgan (to be confirmed)
  • 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

Model Management Workshop: The Magic Behind Making Decisions Happen; ML, AI, & Prescriptive Analytics within Business Design & Control Constraints

SPECIAL OFFERS: 20% Super Early Bird Discount Until Friday, February 2nd 2018. When 2 colleagues attend the 3rd goes free!

New York City: February 26th & 27th 2018

NEW YORK BROCHURE

Description

Every company now practices and competes with high-performance analytics  where they analyze, optimize, profitize, individually customize, and instantly digitize products shorting development and implementation of business strategy and information support

Content

Models and aggregated data are the areas that are white hot and growing exponentially the last few years. Today prescriptive analytics – machine learning and artificial intelligence – are critical components along with valuable informational assets that are leading the way to what will and what can be made to happen to accelerate business activity velocity. As important   are the speed governors of regulatory scrutiny bodies and barriers of personal data privacy and cyber security. The industry’s greatest concern and benefit is the constraints of proper design, implementation, use, performance, and controls over algorithms and analytics restricting their operational boundaries 

A enterprise’s success depends on its ability to model and analyze data efficiently and effectively in ways that uncover both risks and opportunities. Being able to  analyze critical information and prescribe  outcomes is of extreme importance and must be supporting by solid model and data management framework operating over modern  infrastructure.

Attendees can interact with speakers and their peers in a classroom setting that encourages both participation and engagement. Seating for this conference is limited to maintain an intimate educational environment that will cultivate the knowledge and experience of all participants.


Your Expert Trainer and Provider

Donald Wesnofske, CPA, Founder & CEO, RiskOfficer, Inc.

Donlad.Wesnofske@RiskOfficer.com
www.linkedin.com/in/donaldwesnofske/

Don is the Founder and Chief Executive Officer of Risk Officer Incorporated, a boutique management consulting, strategy, and advisory firm offering professional services to the Universal Bank, Capital Markets, and Treasury sectors. His practice covers the subject areas of capital adequacy, planning and forecasting, finance, risk, model management, operations, and technology solutions, and includes performing reviews, assessments, and internal audits. With 35 years of experience in the financial services industry, Don has a unique hands-on career acquiring capabilities that span governance, financial-risk management, regulatory compliance, operations, technology, information, and data. Over his career, he homed in on Basel, Dodd-Frank, Prudential Regulators (FRB, OCC, FDIC, FCA, FHFA), CFTC, and SEC compliance specializing in capital planning and stress testing using his ability to assess quantitative and qualitative models, and analysis solutions. Don holds a MS Accounting and a BS Finance/Accounting from the CW Post Center of the Long Island University, and is an active licensed Certified Public Accountant. Don is a steering committee member of the Professional Risk Managers International Association’s (PRMIA) New York City Steering Committee. and former Chair of the Financial Executive Network Group (FENG) Risk Special Interest Group.


Focused Topics

  • Optimize model design and controls to ensure applicability and accuracy of analytics for decision needs that use high performance infrastructure
  • Utilize model governance to reduce knowledge, skills, and abilities bottleneck improving socialization of high value approaches and methods
  • Implement model specific research and development programs that improve decision analytics properly aligned to business activities
  • Merge multiple and redundant model design and research activities with streamlined processes and enhanced cost effective methods
  • Reduce dependency on islands of model research and development that are expensive and highly customized to single user needs

Areas of Interest

  • Install enhance quality, privacy, and cyber security controls over models produced information and decisions
  • Use predictive and prescriptive analytics, machine language and artificial intelligence, and data to uncover and manage risks and opportunities
  • Apply effectively guidelines and standards set by international, USA, and European regulatory authorities
  • Explore Model research, development, and use associated with Capital Adequacy and Sustainability  projections and forecasting
  • Integrate modelling requirements with strategic and tactical plans

Know Your Risks ℠

  • Understand the role and goals of the model  oversight committee (MOC) its policies, strategy, quality levels, and the heath model risk control
  • Recognize compliance gaps to international guidelines and US prudential regulator rules  (SR 2011-07) including the Dodd Frank Act
  • Continuously innovate modeling processes and controls to assure appropriateness and reliability of decisions based upon demand and needs
  • Understand approach and monitor information output from modeling environments under high throughputs, shorter development times, and faster autonomous decisions making circumstances
  • Eliminate islands of model research and development that are expensive, highly customized to single needs and utilize outdated approaches

Capitalize Your Benefits ℠

  • Assess increased demand and utilization of predictive and prescriptive models decisioning customer, position, exposure, and portfolio activities
  • Leverage model governance, scientists, and developers to reduce knowledge and skills bottleneck while improving socialization of high value
  • Rationality and objectively assess your modeling needs and dependencies
  • Consolidate multiple modelling environments and data repositories using streamlined processes and evolving cost effective methods
  • Supercharge enterprise model quality and control programs that improve use, predictability, decision accuracy, and add to performance

Couse Modules & Components

  • Model governance and design
  • Model resources, tools, platforms, and infrastructure
  • Model frameworks, approaches, and methods
  • Business analytics
  • Descriptive analytics, approaches, and tools
  • Diagnostic analytics, approaches, and tools
  • Predictive and prescriptive analytics
  • Artificial intelligence, approaches, tools
  • Machine learning, approaches, tools
  • Data lineage, aggregation, and control environment
  • Model risk, inventory, validation, review
  • Model and data audit

Who should attend

Risk managers , finance managers, model managers, model designers, data managers, IT  managers, operations, model users, forecasters, planners

BOOK NOW

Monday February 26th, Workshop Agenda Day 1:

1.   Model governance and design

  • Model governance, program, and structure
  • Board of Directors, committees, executives, management
  • Regulatory environment, modeling charter, and internal policies
  • Modeling framework, guidelines, workflow, and internal procedures
  • Performance, risk, and effective challenge feedback loops
  • Program/project management, communication, gap changes, and delivery
  • Model Research, design,  and development
  • Resources: scientists, quantitative programs, developers, users
  • Model development tools, testing environments, validation, and testing
  • Model data collection, aggregation, and architecture
  • Alternative IT infrastructure and production platforms
  • Historical perspectives and capability maturity tracking
  • Descriptive Analytics: insight into the past
  • Diagnostic analytics – insights into why
  • Predictive Analytics: Understanding the future
  • Prescriptive Analytics: Advise on possible outcomes
  • Industry leading practices, consortium, whitepapers, and innovation 

2.   Model resources, tools, platforms, and infrastructure

  • Program/project management, communication, gap changes, and delivery
  • Model Research, design,  and development
  • Resources: scientists, quantitative programs, developers, users
  • Model development tools, testing environments, validation, and testing
  • Model data collection, aggregation, and architecture
  • Alternative IT infrastructure and production platforms

3.   Model frameworks, approaches, and methods

  • Historical perspectives and capability maturity tracking
  • Descriptive Analytics: insight into the past
  • Diagnostic analytics – insights into why
  • Predictive Analytics: Understanding the future
  • Prescriptive Analytics: Advise on possible outcomes
  • Industry leading practices, consortium, whitepapers, and innovation 

4.   Business analytics

  • Model program/ project governance and model design tollgates
  • Teams and resources
  • Business application, operating models, and playbooks
  • Approaches, methods, and techniques
  • Model and data alignment
  • Business applications and model design tollgates
  • Model inventory, model approaches and methods, and research
  • Taxonomies and metadata
  • Model development and implementation playbooks
  • Data, metadata, data acquisition, data quality analysis, and controls
  • Business applications and model design tollgates
  • Model inventory, model approaches and methods, and research
  • Taxonomies and metadata
  • Model development and implementation playbooks
  • Data, metadata, data acquisition, data quality analysis, and controls

5.   Descriptive analytics, approaches, and tools

  • Business applications and model design tollgates
  • Model inventory, model approaches and methods, and research
  • Taxonomies and metadata
  • Model development and implementation playbooks
  • Data, metadata, data acquisition, data quality analysis, and controls

6.   Diagnostic analytics, approaches, and tools

  • Business applications and model design tollgates
  • Model inventory, model approaches and methods, and research
  • Taxonomies and metadata
  • Model development and implementation playbooks
  • Data, metadata, data acquisition, data quality analysis, and controls

Case study & interactive exercise

Day 1 Morning

An interactive discussion and analysis of alternative model governance organizations and the interaction of BOD and management committees on the oversight  of model design, models  implementation, use, and controls.

Day 1 Afternoon

An interactive discussion on model sandboxes with explanations of business activities and associated application of business analytics with focus on descriptive and diagnostic analytics modeling processes.


Workshop Schedule: 09.00 – 17.30

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Tuesday February 27th, Workshop Agenda Day 2:

1.   Predictive and prescriptive analytics and algorithms

  • Model program/ project governance and model design toollgates
  • Teams and resources
  • Business application, operating models, and playbooks
  • Approaches, methods, and techniques
  • Model and data alignment

2.   Artificial Intelligence, approaches, tools

  • Business applications and model design tollgates
  • Model inventory, model approaches and methods, and research
  • Taxonomies and metadata
  • Model development and implementation playbooks
  • Data, metadata, data acquisition, data quality analysis, and controls

3.   Machine learning, approaches, tools

  • Business applications and model design tollgates
  • Model inventory, model approaches and methods, and research
  • Taxonomies and metadata
  • Model development and implementation playbooks
  • Data, metadata, data acquisition, data quality analysis, and controls

4.   Data lineage, aggregation, and control environment

  • Operating models and data lineage
  • Principals: Accuracy, integrity, completeness, timeliness, adaptability
  • Data controls & data control environment
  • Control statistics, dashboards and business intelligence
  • Mining, research, discovery, tracing, and forensics

5.   Model risk, inventory, validation, review

  • Model governance, roles, responsibilities
  • Modeling framework, polices, and design committees
  • Model effective challenge and controls
  • Model inventory, reviews, and validation
  • Model outsourcing and third party vendors
  • Regulatory reviews, internal audit and corrective actions

6.   Model and data audits

  • Model design and supporting documentation
  • Model performance reviews and testing
  • Model internal audit programs and assessment ratings
  • Regulatory reviews and findings
  • Corrective actions progress monitoring

Case study & interactive exercise

Day 2 Morning

An interactive discussion on model sandboxes with explanations of business activities and associated application of machine learning and artificial intelligence models and algorithms  with focus on predictive and prescriptive analytics modeling processes

Day 2 Afternoon

A discussion with explanation of the three lines of defense and the control environment associated with model development, implementation and use with focus on validation, controls, and the supporting documentation required by regulators and internal audit


Workshop Schedule: 09.00 – 17.30

BOOK NOW

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.

Featured Image

RavenPack is the leading big data analytics provider for financial services. Financial professionals rely on RavenPack for its speed and accuracy in analyzing large amounts of unstructured content. The company’s products allow clients to enhance returns, reduce risk and increase efficiency by systematically incorporating the effects of public information in their models or workflows. RavenPack’s clients include the most successful hedge funds, banks, and asset managers in the world. www.ravenpack.com

Featured Image

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.