Financial firms are under pressure to grow their business while containing risk and complying with many regulations world-wide. Computational simulation is a key set of analytic tools that provide methods for studying a wide variety of models of real-world systems through numerical evaluation. Agent Based Models (ABMs) are one innovative approach that has vast potential to improve our understanding of how micro-level behaviour interacts to produce emergent macro-level outcomes. In this webinar, Ovum Sr. Analyst Paige Bartley will discuss the importance of big data as the foundation for building better predictive models. Cloudera and Simudyne will show you how, by adding ABM simulations to your analytics toolbox, you can account for emergent phenomena, knife-edges of risk, contagion and complex states of the world in which traditional models are not sufficient anymore. 3 things to learn: Ovum Sr. Analyst Paige Bartley will discuss the importance of big data as the foundation for building better predictive models. Cloudera and Simudyne will show you how, by adding ABM simulations to your analytics toolbox, you can account for emergent phenomena, knife-edges of risk, contagion and complex states of the world in which traditional models are not sufficient anymore.

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Extend Your Analytics Toolkit
with Computational Simulations

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Agenda
The Need for Governance in Agent Based Modeling
Paige Bartley
Sr. Analyst, Ovum
Justin Lyon
CEO, Simudyne
Make better decisions with Computational
Simulation
Dr. Richard Harmon
Director Financial Services, Cloudera
Model, scale, deploy with Simudyne and Cloudera

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What are the Trends in IT
Ovum’s ICT Enterprise Insights survey is an annual, global survey of IT decision makers
More than 4,000 participants
More than 900 respondents from the financial industry
1. Firms of all verticals are progressively moving to the cloud
2. Increasing revenue and increasing operating efficiency are top-cited business challenges
3. AI and machine learning are getting deployed in the real world, particularly for decision-
making

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IT Trends: What about the financial industry?

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Use cases around AI are growing in popularity

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Digital capability depends on control of data
The axiom, “garbage in, garbage out” is truer than ever.
Challenges in the modern management of data:
• Architectural silos prevent the ready aggregation of data
• The data lake is becoming virtual, with multiple cloud repositories
• Data protection requires granular lineage, policies, and access controls
• The need to combine historical and streaming “fast data” for analysis
• Self-service era means increasing volume of data consumers and data producers

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Analytics sophistication compounds the need for well-managed
data
Prescriptive
Analytics
Predictive
Analytics
Descriptive
Analytics
Complexity of
analysis
Magnitude of impact on business decisions

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ABM: Driving the need for managed data
Agent-based modeling uses “agents” which are virtual copies of people, institutions, or other
autonomous entities
• Requires large amounts of data – ideally suited to the data lake
• Unlike machine learning, ABM and computational simulation work best when the
variables are large and organized
• Even small errors in data can become compounded, leading to substantial error later in
the simulation
Agent based modeling ideally needs to be paired with a strong governance framework or
platform for data within the enterprise

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The IT-centric governance model is outdated
• IT-driven model doesn’t
adequately scale for self-service
• Need to incorporate data use
context from business users
• Feedback loop between IT and
lines of business is needed
• Need a platform where both
business and IT can collaborate
IT-adjusted
policies for
data
Increased data
access and
leverage
Business
user
activity &
feedback

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Is the managed data lake the answer?
If done properly, then yes.
• Needs to incorporate both on-premise and cloud data sources
• Must be able to ingest all data, both raw and refined
• Must enable business users to participate in the data curation process
• Needs to be a shared, accessible resource available to all users
• Ideally allows for central management of policies, security, and access
The managed data lake allows for data transparency.

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Managed data lake reference architecture
Key takeaways:
• Hadoop in isolation does not
perform strong governance
• Needs to accommodate both
business and technical users
• Granular data security is
increasing in importance
• Virtual data lakes complicate
centralized governance
Data platform(s): often Hadoop
Cost optimization & integration
Data-level security
Query/analytics tools, programs
Availability/reliability
Monitoring&Troubleshooting
Perimetersecurity
Data curation
Data inventory
= self service “tier” of data access Source: Ovum

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Symudine + Cloudera Enterprise Data Hub:
ABM on the managed data lake
By sitting atop of the Cloudera Enterprise Data Hub, Simudyne gains access to a lake of
well-governed data
• Better data quality leads to better models
• Security and access controls facilitate compliance
• Data lineage capabilities provide transparency
• Can scale ABM simulations to manage billions of interacting agents
• Data remains readily available in the lake for use in other analytics applications

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Polling Questions #1
What is the relative maturity of the information governance initiative
within your organization?
1. Journey has just begun – Key roles are assigned to stakeholders,
but technology is largely disjointed
2. Progress underway – Human processes are in place, and some
architectural changes have been made
3. Mature and functional – People, processes, and technology are
tightly integrated and efficient

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• The Need for Governance in Agent Based Modeling
• Make better decisions with Computational Simulation
• Cloudera Simudyne Computational Simulation Solution
Agenda

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DRIVE CUSTOMER INSIGHTS
Customer360
Customer Journey Analytics
Recommendation Engine
PROTECT THE
BUSINESS
Enterprise Risk Management
AML (Anti-Money Laundering)
Fraud Detection/Prevention
Market Surveillance
Regulatory Compliance
Cybersecurity
IMPROVE PRODUCTS & SERVICES
Digital Transformation
Alternative Data
Operational Efficiency
PATTERN
RECOGNITION
DETECTION
PREDICTION
750+CUSTOMERS RUN
ON
SIMULATION
DRIVE BUSINESS INSIGHTS
Prescriptive Analytics
Stress Testing
Business Strategy
The enterprise platform for machine learning

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Key drivers of transformation in financial services
Proliferation
of Data
Unstructured and
structured data growing
at unprecedented rates,
due to mobile apps and
Io
Digital Disruption and Shift
Towards Omni-Channel
Banking
Consumers demand an
enhanced experience.
FinTech, InsureTech
startups threaten
the status-quo
Escalating Fraud Costs
and Increasing Cyber
Attacks
The complexity and
scale of financial fraud
and
cyber attacks is rising.
Regulations and
Compliance
Changing regulatory
environment requires
firms to be more agile
to meet compliance

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Risk Management Trends
CONTAGION RISK STRESS TESTING IFRS CHANGES
Understanding counterparty credit risk and
understanding your potential for default has
become a major risk management objective for
financial institutions
Since the financial crisis the levels of stress
testing required by regulators has increased
significantly. Modern stress tests require a high
degree of scenario analysis
Capital Adequacy and related Balance Sheet
calculations are being constantly refined by a
number of IFRS and GAAP Changes

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Evolution of Risk Management
RISK 1.0 RISK 2.0 RISK 3.0
Historical Data – VAR Models
Understanding potential downside risk by
calculating maximum losses according to
historical data
Static Scenarios – Stress Tests
Running Monte Carlo simulations to uncover
potential losses according to a number of
scenarios
Dynamic Interactions – Agent-based
Creating digital versions of each asset and
liability in order to simulate a wide range of
possible future scenarios

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Polling Questions #2
Where on this risk management maturity curve is your organization?
1. v1.0
2. v2.0
3. v3.0
4. Prefer not to say

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• The Need for Governance in Agent Based Modeling
• Make better decisions with Computational Simulation
• Cloudera Simudyne Computational Simulation Solution
Agenda

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“In the summer of 2007, … the markets for some
mortgage securities stopped functioning. Buyers
and sellers simply couldn’t agree on price, and
this impasse soon spread to other debt markets.
Banks began to doubt one another’s solvency.
Trust evaporated, and not until governments
jumped in, late in 2008, to guarantee that major
banks would not fail did the financial markets
settle down and begin fitfully to function again.”
Source: Harvard Business Review
The Financial Crisis
Source: Financial Times

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• Past models failed to capture interactions of finance and the real economy
• There was no historical precedent for models to draw data from
• Market conditions moved outside of the known parameter set
• Models assumed no changes in data generation process, which always
produces a future that looks like the past
• Models described reality linearly, which is not realistic
Traditional Models Failed During the Financial Crisis

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Source: Bank of England
• Computational simulation is the only way to model complex
adaptive systems like the financial system.
• Agent Based Modelling (ABM) is one innovative approach
that can improve our understanding of the markets.
• In an ABM, the modeler creates autonomous agents
that could represent, for example: households,
corporates, banks and funding providers.
• These agents:
− Learn from their experiences
− Adapt their behaviors
− Interact with and influence each other
Advancing Your Tool Kit: Computational Simulation

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• Heterogeneity: Models explicitly capture differences at the agent level.
• Realistic Behaviors: Accurately reflect real-world behavior by modelling
individual heuristics and deviations from rationality. Decouple the future from
the past – recognizing that future crises will not look like the past.
• Emergent Behavior: Differences in behaviors at the micro-level interact to
influence macroscopic outcomes – recreating emergent phenomena.
• Complexity: Recognize that financial markets are interconnected and exhibit
non-linear behavior that helps propagate stress through the financial system.
The Key Features of Agent Based Models

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• Capture complex behavior and look for tipping points or knife edges of risk
• Recognize the uncertainty in forward-looking projections
• Ask what-if? -- explore the impact of alternative actions in a virtual world
• Train intelligence, whether human or artificial.
The Key Benefits of Simulation for Risk Management

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The Challenges of Prescriptive Models
ENGINEERING CHALLENGES MODELLING CHALLENGES
Desktop simulation has limited scale and is
unfit for enterprise use
Supercomputer simulation is expensive, inflexible,
and hard to maintain
Different systems required for different types of
analytics and simulations
Few platforms support behavioural or complex
adaptive systems modelling
• High-definition simulation, needs high-performance compute
• Desktop simulation doesn’t scale and supercomputer simulations are
prohibitively expensive
• There is no single, secure solution for computational simulation built for the
needs of global firms
• Simulating real world complexity requires advanced modelling paradigms
like agent based modelling
• Current tools require specialist expertise and create ‘walled gardens’ of
knowledge within a company
• The adoption of an agreed set of tools across an organisation ensures
greater collaboration and reduces model risk

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Cloudera/Simudyne Computational Simulation Solution
Ingest ModelTransform Discover Visualise Decide

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Ingest
Cloudera/Simudyne Computational Simulation Solution
● Single place for all data
● Scale up to the cloud
● Highly governed and protected
● Shared data experience
● Simulations at massive scale
● High fidelity models
● Model complex adaptive systems
● Test decisions in a safe environment
ModelTransform Discover Visualise Decide
Machine Learning Computational Simulation

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Supply Chain Risk Stress Testing Credit Risk Group Strategy
Omni ChannelPricing Valuation Market Risk Liquidity Risk
Business accelerator for a number of use cases

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Polling Questions #3
Which use case would be your top choice for ABM?
1. Stress Testing
2. Credit Risk
3. Group Strategy
4. Pricing Valuation
5. Market Risk
6. Liquidity Risk
7. Omni-Channel
8. Supply Chain Risk
9. Do Not Know

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Simulation SDK for Financial Services

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Decision Making

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Decision Making

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Decision Making

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Decision Making

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Decision Making

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Decision Making
MACHINE LEARNING

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SIMUDYNE
Simulation SDK for Financial Services
DATA INGESTION
MODELLING
VISUALISATION
• Easily ingest data from a variety of sources
• Platform agnostic SDK deployed on-premise or
in the cloud
• Certified on Cloudera, the leading Hadoop
platform
• Machine Models: Combining data and
algorithms to create models
• Human Models: Describing complex adaptive
systems with agent based modelling
• Console: Out of the box visualisation tool that
helps developers to iterate at pace
• Custom UI: Toolkit for bespoke dashboards,
putting the power of advanced analytics in the
hands of senior decision makers

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Solution Brief:
https://www.cloudera.com/content/dam/www/marketing/resources/solution-briefs/cloudera-and-simudyne-
computational-simulation-solution.pdf.landing.html
Demo Video: Simulating a Bank’s Mortgage Book over 10 years on Cloudera
https://www.youtube.com/watch?v=vrg9r_cgeso
Cloudera:
Solution Lead: Dr. Richard Harmon, rharmon@cloudera.com
Marketing Lead: Mihaela Risca, mihaela@cloudera.com
Simudyne
Sales Lead: Rodney Taylor, rodney@simudyne.com
Marketing Lead: Dahlia Lamy, dahlia@simudyne.com
Resources

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Thank you