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Machine Learning & AI in Quantitative Finance Conference
New York City: February 28th, March 1st & 2nd 2018


 Monday February 26th 2018: Workshop Agenda Day One 


Tuesday February 27th 2018: Workshop Agenda Day Two


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 

An 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.

Location

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

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). 

Course Takeaways

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
  • ntegrate 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 use outdated approaches

Capitalize your opportunities ℠

  • Assess increased demand and use 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

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

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

Booking

 

Register to ANY ONE day of the workshop
Register to BOTH days of the workshop and receive $200 discount

Model Management Workshop day 1
Model Management Workshop day 2
 
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Academic Discount 70% (FULL-TIME students only.
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