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Topics:

Topics:

London:

Predictive Power vs. Expressiveness of Machine Learning Models
Machine Learning: History and Implications for Quantitative Finance
Reliable Machine Learning
Black-box Machine Learning: Improving Transparency
Machine Learning Models
Fast MVA Optimisation using Chebyshev Interpolants
Machine Learning - Recent Trends and Applicability to Risk and Related Areas
Machine Learning, High-Frequency Trading and Kdb+/q for Quants and Data Scientists
Unsupervised Anomaly Detection in Finance
Practical Aspects of Applying Deep Learning for Market Making
Applications & Challenges of using Deep Learning & Bayesian Inference Methods for High Frequency Market Making
Co-creating Machine Learning solutions within a global Corporate & Investment Bank
Using Big Data to Trade FX (& Python for finance)
Can AI help FRTB?”
Time Series Data & FRTB - time to get it right

New York:

Predictive Power vs. Expressiveness of Machine Learning Models
Machine Learning - Recent Trends and Applicability to Risk and Related Areas
Black-box Machine Learning: Improving Transparency
Machine Learning - Recent Trends and Applicability to Risk and Related Areas
Applying Machine Learning to Evaluate Systemic Risk and Contribution of Individual SIFIs
Fast MVA Optimisation using Chebyshev Interpolants
The 7 Reasons Most Machine Learning Funds Fail
Machine Learning, High-Frequency Trading and Kdb+/q for Quants and Data Scientists
Machine Learning for Trading
Big Data and AI Strategies: Machine Learning and Alternative Data Approach to Investing
Text Mining and Market Sentiment
Machine Learning & Event Detection for Trading Energy
Extracting embedded alpha in Stocks and Commodity underlyings using statistical arbitrage/ML techniques from News/Social data

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 – 17.00


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

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

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

“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.” 


09.45 – 10.30: Using Machine Learning Methods for Volatility Trading Artur Sepp (London)

• Statistical models for realized volatility estimation and forecast
• Model selection using machine learning
• Supervised machine learning and learning to rank
• Applications for volatility trading and asset allocation 

Presenter: Artur Sepp, Director, Senior Quantitative Strategist, Julius Baer


10.30 – 11.00: Morning Break and Networking Opportunities


11.00 – 11.45: 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


11.45 – 12.30: Presenter & Topic to be confirmed


12.30- 13.30: Lunch


13.30 – 14.15: “Machine Learning for Financial Systems: Where it can be Competitive” Tomaso Aste

  • Machine leraning ad artificial intelligent have been developed for domains that are very different to finance
  • Financial data are noisy and training sets are very scarse
  • Socio-economic systems continuously evolve and never repeat identical patterns
  • New tools must be developed to operate with machine learning in these systems

Presenter: Tomaso Aste: Professor of Complexity Science, UCL Computer Science


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 – 16.30: Unsupervised 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.  


16.30 – 17.15: Learning the Optimal Risk

Marco BianchettiMarco Scaringi

  • Portfolio optimization from a risk management point of view
  • Eligible risk optimization strategies
  • Optimization metaheuristics and machine learning
  • Test cases
  • Mathematical precision vs effective risk hedging

Presenters: Marco Bianchetti: Financial and Market Risk Management, Head of Fair Value Policy & Marco Scaringi: Financial and Market Risk Management, Quant Risk Analyst, Intesa Sanpaolo


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:

  • Miquel Noguer Alonso: Adjunct Assistant Professor, Columbia University
  • Artur Sepp, Director, Senior Quantitative Strategist, Julius Baer 
  • Abdel Lantere: Data Scientist, Quantitative Consultant, HSBC
  • Ignacio Ruiz: Founder & CEO, MoCaX Intelligence
  • Claudi Ruiz Camps: Machine Learning, Deep Learning Specialist, ABN AMRO Clearing Bank N.V.  

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

Chair: Yves Hilpisch: Image result for yves hilpisch

Founder and Managing Partner

The Python Quants  

 

 


09.00 – 10.00: Keynote

Presenter: Vacslav Glukhov, PhD: Executive Director, Linear Quantitative Research, Global Equities, J.P. Morgan (to be confirmed)

Topics in Self-Learning Agents and Traditional Quantitative Models in Finance 

  • What can we draw from our experience of training and running an industry first self-learning agent for electronic order execution?
  • Will traditional hand-crafted heuristic- and quant-based execution algorithms go extinct within 10 years?
  • Does the success of ML and AI agents in finance indicate the eventual demise of traditional quantitative models?
  • Practical aspects of using feeder models and heuristics in AI agents for trading applications.
  • Do we have practical solutions for the equivalence puzzle in Neural Nets? 

10.00 – 10.45:“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

 


10.45 – 11.15: Morning Break and Networking Opportunities


11.15 – 12.00: Reliable Machine Learning 

  • Robustness
  • Awareness
  • Adaptation
  • Value learning
  • Monitoring

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


12.00 – 12.45: From Artificial Intelligence to Machine Learning, from Logic to ProbabilityPaul Bilokon, PhD

Applications of Artificial Intelligence (AI) and Machine Learning (ML) are rapidly gaining steam in quantitative finace. These terms are often used interchangeably. However, the pioneering work on AI by participants of the Dartmouth Summer Research Project — Marvin Minsky, Nathaniel Rochester, and Claude Shannon — was more symbolic than numerical, and often used the language of logic. Recent advances in ML — especially Deep Learning — are more numerical than symbolic, and often use the language of probability. In this talk we shall show how to connect these two worldviews.

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


12.45- 13.45: Lunch


13.45 – 14.30: AI-First Finance and Algorithmic TradingImage result for yves hilpisch

  • This talk considers the consequences of recent advances in the field of Artificial Intelligence (AI) for finance in general and algorithmic trading in particular.
  • The talk is mainly based on practical examples, using Python as well as Machine & Deep Learning techniques to come up with algorithmic trading strategies.
  • The examples in turn are mainly based on (tick) data from FXCM Forex Capital Markets Ltd. and their new RESTful API for data retrieval and algorithmic trading. 

Presenter: Yves Hilpisch: Founder and Managing Partner, The Python Quants


14.30 – 15.15: 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


15.15 – 15.20: Quick Afternoon Break


15.20 – 16.00: Big Data and AI Strategies: Machine Learning and Alternative Data Approach to Investing

Abstract: The past few years have witnessed widespread adoption of quantitative investment techniques including risk premia investing, algorithmic trading, utilization of differentiated types of data and adoption of new methods of analysis drawn from machine learning and artificial intelligence. We will provide an overview of big/alternative data – including sentiment signals from RavenPack – and illustrate their use for different investors. We will explain the use of machine learning techniques – covering both classical and deep learning methods – in design of systematic strategies across asset classes.

Presenter: To be confirmed


16.00 – 16.45: “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


Location:
Downtown Conference Center
157 William Street
New York, NY 10038
USA
Tel: +1 212 618 6990
Website


  • 10% Early Bird Discount Until February 9th 2018
  • When 2 colleagues attend the 3rd goes free
  • Main Conference + Workshop ($250 Discount)

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

Chair:Seong Seog Lee

Seong Seog Lee

Director of Quant Strategy

Quantopian 


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 – 10.30From Artificial Intelligence to Machine Learning, from Logic to ProbabilityPaul Bilokon, PhD

Applications of Artificial Intelligence (AI) and Machine Learning (ML) are rapidly gaining steam in quantitative finace. These terms are often used interchangeably. However, the pioneering work on AI by participants of the Dartmouth Summer Research Project — Marvin Minsky, Nathaniel Rochester, and Claude Shannon — was more symbolic than numerical, and often used the language of logic. Recent advances in ML — especially Deep Learning — are more numerical than symbolic, and often use the language of probability. In this talk we shall show how to connect these two worldviews.

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


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  Suhail Shergill

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

Presenter: Suhail Shergill: Director | Data Science and Model Innovation, Scotiabank | Global Risk Management


12.30- 13.30: Lunch


13.30 – 14.15: Bridging the Gap Between AI and Regulatory Requirements

Presenter: Ksenia Shnyra: Senior Advisor, Deloitte


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 – 16.15: Responsible Machine Learning George A. Lentzas

This talk will discuss the variance-bias decomposition, estimation of test error and the intricacies of cross validation. I will explain what cross validation really estimates and why it is not to be used blindly.

Presenter: George A. Lentzas: Manager & Chief Data Scientist, Springfield Capital| Adjunct Associate Professor, Columbia & New York University  


16.15 – 17.00: 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


17.00 – 17.45: 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: Adjunct Assistant Professor, Columbia University
  • Rajesh T. Krishnamachari: Vice President, Quantitative and Derivatives Strategy, J.P. Morgan 
  • Abdel Lantere: Data Scientist, Quantitative Consultant, HSBC
  • Suhail Shergill: Director | Data Science and Model Innovation, Scotiabank | Global Risk Management

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 - The Magic Behind Making Decisions Happen; ML, AI, Big Data & Prescriptive Analytics within Business Design & Control Constraints

SPECIAL OFFER: When 2 colleagues attend the 3rd goes free!

New York City: February 26th & 27th 2018

NEW YORK BROCHURE

Location:
Downtown Conference Center
157 William Street
New York, NY 10038
USA
Tel: +1 212 618 6990
www.downtownmeetings.com


  • When 2 colleagues attend the 3rd goes free!

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 Trainers and Provider

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

Donald.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.


Lazaro Martinez-Lopez: Independent ConsultantLazaro Martinez-Lopez

Lazaro is a recognized and accomplished Statistician, Data Scientist, Engineer, Quantitative Programmer, Application Developer, and Actuary with wide ranging and comprehensive experience. He has extensive industry experience in Banking, Risk Management, Healthcare, Marketing, Telecommunications, Agriculture, and Insurance. His strengths include significant capability to adapt to new technical standards within diverse technical environments.

Lazaro has designed and implement financial and statistical models and applied analytic models and algorithms to use cases in the areas of, Econometric modelling and scenarios, Co-integration, Stochastic, Logistic, Timer series, Non-linear timer series, Marketing Delinquency, Origination, Payment, PD, LGD, EAD, Loss forecasting, Credit card acquisition, Lookalike, Survival, Cybersecurity, Payment Fraud, and Churn Process Improvement. He has more than twenty plus years’ experience design and building data solutions covering big-data and data structured warehouses analytic solutions including working with SAS, SPSS, MATLAB/MathWorks and other analytical modeling and reporting tools.

Lazaro Martinez has 26 years of experience training, consulting, and managing advanced analytics. He graduated from University of Nebraska with a dual Bachelor’s degree in Actuarial Science and Mathematics, and a Minor in Economics. In addition, from University of Nebraska he has a dual Master degree in Mathematics Statistics and Biometrics. Furthermore, Lazaro also has a Bachelor degree in Electrical Engineer from Institute of Technology Jose Antonio Echeverria in Havana Cuba.

Lazaro is currently designing, implementing, and training leading technologies including Machine Learning and Artificial Intelligence for the Banking Industry


Mubeen Bhatti: Expert Advisor, Boston Consulting Group Mubeen Bhatti

Mubeen is a recognized leader in quantitative, advanced, and big-data analytics including machine learning and artificial analytical skill. He has designed, developed, and implement statistical models and machine learning algorithms using supervised learning: least squares regression, logistic regression, perceptron, naive Bayes, and support vector machines (SVMs), feature selection for facial recognition, and ensemble methods used for boosting to provide the background necessary to design unsupervised learning including Clustering. K-means, EM, mixture of gaussians, and principal component analysis. In addition, Mubeen has designed and managed various strategic and tactical technology risk modelling initiatives and developed risk reporting using business intelligence tools like QlikView and Tableau.

Mubeen has unique talents in analytical frameworks focused on valuation, risk and business optimization. He has dual masters from University of Pennsylvania with specialization in mathematics, and machine learning and artificial intelligence. Mubeen has more than 12 years of experience cover numerous projects from control of automated drones for DARPA to firm-wide bank capital sustainability, planning, and analysis using scenario building and stress testing methods required by Federal Reserve Bank.

Recently, he worked as a subject matter expert advisor for Boston Consulting Group performing Model risk governance and review, redesign, and validation of models. Mubeen has significant skills and capabilities in the areas of Model Governance and Model Development including approaches, methods, and techniques focused on based on regulatory guidance from the Fundamental Review of the Trading Book (BCBS 346/352 FRTB), Credit Valuation Adjustment (BCBS 325 CVA/XVA), Risk Model Validation (FRB/OCC SR Letter 2011-7), and Bank Capital Planning (FRB/OCC SR Letter 2015-18 & 2015-19). 


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

Topics and subtopics

1.     Model governance and design

2.     Model resources, tools, platforms, and infrastructure

3.     Model frameworks, approaches, and methods

4.     Business analytics

5.     Descriptive analytics, approaches, and tools

6.     Diagnostic analytics, approaches, and tools

7.     Predictive and prescriptive analytics

8.     Artificial intelligence, approaches, tools

9.     Machine learning, approaches, tools

10.   Data lineage, aggregation, and control environment

11.   Model risk, inventory, validation, review

12.   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

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Sponsors

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