New York City: Model Management Workshop: ML, AI, & Prescriptive Analytics within Business Design & Control Constraints, February 26th & 27th 2018

Model Management Workshop: ML, AI, & Prescriptive Analytics within Business Design & Control Constraints
New York City: February 26th & 27th 2018
When 2 colleagues attend the 3rd goes free!


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.


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.

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

Your Expert Trainers and Provider

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

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..

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


Published date

Tuesday, 28 November, 2017