GARP: Professionalizing Risk Management

October 3rd, 2019

Who are we?

“Being a member is very important for my career development. It offers invaluable opportunities to continue learning, sharing and networking.” Adela Baho

Committed to advance the risk profession through education,research and the promotion of best practices globally. Founded in 1996, we serve 200,000 members in 165 countries.

‣ FRM®

and ERP®

Certification

‣ GARP Research Institute

‣ Benchmarking for Global Financial Institutions

‣ Global Risk Leadership Forum

‣ Buy Side Risk Managers Forum

‣ Risk Intelligence

‣ Webcasts & On Demand Content

‣ Global Chapters

‣ Membership

‣ Academic Partnerships

‣ Continuing Professional Development

Certifying the Next Generation of Risk Professionals

80,000+ in 2019

Become FRM or ERP Certified. Join 4,200 USA candidates who register each year. Registration for the November 2019 exam closes October 15.

Benchmarking Industry TrendsThe GRI Climate Risk Study

‣ A Good Start But More Work To Do

• Survey results at a group and participating firm level

‣ Challenges and Opportunities

• A useful introduction to climate change and its associated risks

Visit garp.org/garp-risk-institute to read both reports and ask your Chapter Director for printed copies

Two reports based on a global survey of leading financial firms about their ability to manage climate risk

‣ Operational, Credit and Market Risk

‣ Energy

Keep up with the latest at garp.org/risk-intelligence

Providing News, Analysis & Insights Risk Intelligence

‣ Technology

‣ Culture & Governance

Articles, videos, webcasts, podcasts, white papersand more on topics including:

Educating risk professionals

‣ October 8, Webcast: The Transition from LIBOR

‣ October 8, Luxembourg: Artificial Intelligence: Impact, Risks and Opportunities

‣ October 15, Paris: The Future of Risk Management: Insights from a CRO

‣ October 17, London: Machine Learning and AI Models

‣ October 22, Calgary: Hedging and Risk Management in Energy Markets

‣ October 23, Houston: Energy Trading Risk Summit

‣ October 30, Webcast: Corruption and Corporate Governance in the Financial Sector

‣ November 7, Madrid: Risk Innovations, Challenges and Future Opportunities: Insights from the Top

‣ November 12, Webcast: Future of Risk Analytics: Re-imagining Risk Modelling

‣ November 20, London: Getting Ahead of the Curve: How Financial Institutions are Addressing Climate Change Risk

… and strengthening your network

#GARPriskcon

Accelerating Innovation and

Strengthening Resilience

March 9-11, 2020 | NYC

The New Risk Playbook:

Artificial Intelligence and Risk Management in Energy Markets

Glen Swindle, Managing Partner, Scoville Risk Partners, LLC

Landscape

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‣ What settings are suited to machine learning algorithms?

‣ Do these pertain to energy markets?

‣ High-dimensional risks

• Many delivery points at which prices print.

• Even more futures and options contracts.

‣ Large data sets as potential explanatory variables.

• Weather, inventory, pipe flows, customer data.

‣ Hard optimization problems.

• Generator dispatch, storage optimization.

Hierarchical organization of delivery locations.

Understanding a System

11

‣ ”A neural net is the second best way to solve any problem – the first is to understand the system you are modeling.”

• (From the early 1990’s)

‣ This spawns some questions.

• Is this still true?

• What does is mean to “understand” a system?

• Do we need to know what an algorithm is ”thinking”?

‣ “Simple” models help us make inferences from our own (extensive) knowledge.

Choices

12

‣ “If a neural net produces a better out-of-sample forecast, why wouldn’t you use it?”

• (From a participant at a recent panel discussion)

‣ Demand forecasts on short time scales.

• This is the same situation as the previous figure, exception there are many potential relevant variables.

• These variables can act in myriad ways.

• Degree of non-linearity.

• Lagged effects.

• Effects depending on time of year.

‣ Approaches:

• Neural nets are commonly applied in settings like this – the drawback: No recourse under malfunction.

• We prefer methods that search the state-space of explanatory variables among classes of linear models:

• Genetic algorithms.

• LASSO methods.

No Choice

13

‣ ”Quantity has a quality all its own.”

• (Callaghan? Stalin?)

‣ Large retail customer datasets

• State eligibility files (OH ~4.5m)

• Historical usage and tags.

‣ Daily pricing of the population

• Regressions and simulations (weather-driven) of reference loads.

• Price by projecting customers onto such.

‣ This is not machine learning – just automation.

• We are doing a very large number of relatively simple calculations.

No Choice

14

‣ Missing customer data is another matter.

‣ Large retail customer datasets (cont).

• Key commercial data can be missing.

• Usage data or capacity/transmission requirements.

• The goal is to use the expansive data sets to make sensible inferences.

• The figure shows relative usage clustering of ~340K CEI RG customers that we find useful.

‣ This is machine learning.

• The clustering methods used could not be affected via simpler methods or by the human eye.

What is Machine Learning?

15

‣ Filling the gaps in spot price data.

• Diminished enthusiasm for trade data submissions to Price Reporting Agencies.

• What do you do when you don’t have any submissions at random locations on random dates?

• Price relationships vary depending upon:

• Weather / demand.

• Price levels.

• Infrastructure.

• Brute force scan of locally linear relationships supplemented by methods to reject variables/models machine learning?

• Would it be better to model the physical system directly?

Some Remarks

16

‣ Machine learning methods are essential.

• Many problems in energy are simply too complex to yield to traditional methods.

‣ Unfettered enthusiasm has potential risks.

• Discounting the cost of malfunction if (when?) aberrant behavior occurs.

• Limiting your ability to make inferences outside of the scope of models driven by historical data.

• An easy way out – it’s hard to model a physical system.

‣ A grain of salt - this figure illustrates the relative levels of enthusiasm versus due diligence in the field.

Artificial Intelligence and Risk Management in Energy Markets

Ratanak Heng, Manager, Advanced Analytics, Exelon Utilities Analytics - Grid Exelon

Exelon Corporation – Who are we?

18

Analytics will power Exelon’s Utility of the Future…

19

Marketing models identify

what messages, timing,

channels, etc. will best drive

community solar adoption

Electrification propensity model can

prioritize which companies are most

likely to electrify

Intelligent traffic signals use

historical data and routing

algorithms to minimize

commuting times

Asset health models proactively notify grid

operators about needed maintenance

/equipment replacement

Models suggest where EV

charging infrastructure is most

needed and how many charging

stations to install

Sensors on streetlights and other devices to

monitor air quality, traffic, etc. are rolled into

dashboards for Smart Cities managers

NOTIFICATION

Estimated time remaining: 45 minutes

Propensity / churn models

identifies which rooftop solar

customers most likely to use

P2P marketplace

Vegetation management models

prioritize and optimize jobs,

routing, etc.

EU Grid Analytics Overview

20

Case Study: Why AI and machine learning for resiliency?

21

June 29-30, 2012 Derecho Storm Total Impact to Mid-Atlantic

Service Interruption

4.2 million customers

Damage $2.9 billion1

Impact to Baltimore Gas & Electric

Service Interruption

760,000 (61% of service territory)

Duration of restoration

95% restored within 7 days

Number of jobs 11,117 outage / 4,316 Non-outage

Resources Over 5,600 total – 2,800 field crews

1"Billion-Dollar Weather/Climate Disasters". National Climatic

Data Center. National Oceanic and Atmospheric

Administration. June 2013. Retrieved June 16, 2013.

Storm Readiness Analytics uses machine learning for high resolution damage prediction models to drive improvements in reliability and customer satisfaction

22

Leverages best in

class weather forecasts

Reduce the duration of storm restoration and

increase customer satisfaction

Tool will support our dedicated

Emergency Response Teams

Reduce annual storm expenditures across all

Operating Companies

Machine Learning for Storm Response

▪ Damage Prediction

▪ Staffing Level Recommendations

▪ Pinpoint Crew Placement

▪ ETR Estimates

EP TEAM–

"We can't wait to start using this

tool!"

"This will provide better resolution

on the actual weather that caused

the damage"

Machine Learning Techniques

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Approach

o Binary Model

- Predicts if a township will or will not have outage(s)

o Severity Model

- If a township is predicted to have outage(s), further predict a severity level of outage counts specified by a range (“high” or “low”)

Types

o Distributed random forest

o Gradient boosting machine

o Generalized linear model

o Deep neural networks

o Naïve Bayes classifier

o Stacked ensembles

Features (~2,000)

o Statistical Aggregations

- Conventional and shape-based

o Rolling window

- Cumulative view of the past

o N-Dimensional / Region-Specific

- E.g., Accumulation of ice followed by sustaining strong wind

o Time_Related

- Month, Season, Week of Day, etc.

Imbalanced Class

o Under Sampling, SMOTE, etc.

Models Tested (~2,400)

Beth Gould Creller(Moderator),

Vice President, ERP Program, GARP

Ratanak Heng,Manager, Advanced Analytics, Exelon

Utilities Analytics - Grid Exelon

Featured Panelists:

Glen Swindle,Managing Partner, Scoville

Risk Partners, LLC

Artificial Intelligence and Risk Management in Energy Markets