The document discusses how financial institutions are facing opposing forces of increased regulatory pressure and rapid global expansion. As these institutions grow rapidly, manual risk and compliance processes become inefficient and introduce risks. Automating decision-making through predictive analytics can help control these risks by focusing employees on high-value activities like difficult decisions. Automated decisions should be based on a hybrid of policies and business/analytics-generated hypotheses. Where human intervention is still needed, analytics can provide curated, risk-scored data on networks, locations, and behaviors to inform decisions.

1. Moving from prediction
to decision:
Automating decision-making in the financial
services risk and compliance arena

2. The financial industry is being driven by the opposing forces of regulatory
pressure and rapid, global expansion
Financial Institutions
Revenue
deflation
Increased
regulatory
pressure
Unknown
market risks
Trade
Finance
Industry Complex
relationships
Rapid
expansion
Global scale

3. As organizations rapidly expand, manual risk and compliance processes
become embedded and growth becomes a function of headcount

4. Manual processes introduce a number of risks to risk and compliance
organizations
Low
adaptability
Inconsistent
application or
execution
Introduction of
cognitive biases
Absence of
feedback
mechanisms

5. Automation can be employed to control risks and focus employees on high
value activities
Data gathering,
75%
Data transformation,
15%
Investigation,
10%
Data gathering,
5%
Data transformation,
5%
Investigation,
90%
In a manual process, analysts spend
only 10% of their time in the key
decision-making phase (investigations)
With automation, analyst attention is now
focused upon understanding the data and
drawing a conclusion

6. By automating clear-cut decisions, expert resources are enabled to
concentrate on making difficult decisions
Fully
automate
Fully
automate
Partially
automate
Predictive
Analytics
Customer
Profiling
Network
Analysis
SuspiciousNot Suspicious

7. In the risk and compliance space, automated decisions should be based
upon a hybrid of policy and business- and analytics-generated hypothesesPolicy
driven
Hypothesisdriven
Policy identification
Business hypothesis
generation
Analytics hypothesis
generation
Iterative analysis
and review
Requirements
definition and
development
ApprovalandImplementation
Automated
decisions

8. Where manual intervention is still required, analytics can enable improved
decision-making through presentation of curated and relevant information
Network analysis
Entities and networks are risk-scored to provide a holistic and risk-based
view of a client relationship, enabling analysts to pinpoint transactions
with previously identified suspicious or high risk parties
Location-based analysis
Geographic indicators may provide significant insight into a
transaction’s potential to be associated with criminal activity
In some businesses, such as trade finance, the geographic
information associated with ancillary parties, such as a shipping
vessel may be highly relevant
Behavioral profiling analysis
Profiling of a customer against historical norms and
peer activity can aid in identifying or confirming the
presence of suspicious activity

9. Key considerations in using network analysis to inform decisions
 How reliable is your entity
resolution?
 How many degrees of
separation should be
considered?
 What relevant historical events
should be considered?
 How does risk of the network
factor into risk of the
individual?

10. Key considerations in using location-based analysis to inform decisions
 What level of detail in geographic
information is available consistently
across your organization?
 What locations are relevant to a
transaction’s risk?
 What external sources can be used
to enhance or cross-reference
geographic information?

11. Key considerations in using behavioral profiling analysis to inform decisions
 How should customers and accounts
be segmented?
 What aspects of a customer are
available for profiling?
 How will a deviation be defined from
both a policy and analytics standpoint?

12. About the presenter
Carl Case is a Senior Manager in the Financial Services Organization of Ernst &
Young LLP (EY). Carl is a Certified Anti-Money Laundering Specialist and
specializes in data analytics, predictive modeling, model validation, and risk
management in the regulatory compliance and financial crimes space.
Carl's recent experience involves supporting global financial services institutions
in enhancing and transforming their financial crimes monitoring programs through
the use of advanced analytics and robotic process automation, specifically within
the areas of AML monitoring and investigation. He has facilitated numerous
examinations and instructional sessions with federal regulatory agencies on the
topic of AML monitoring and tuning.
Carl also serves on the steering committee of the EY Veterans Network, a national network of more
than 800 EY professionals dedicated to strong leadership principles and devoted to professional
development through networking with companies that share a commitment to veterans and
community service.
Carl completed his undergraduate studies at the United States Naval Academy and MBA at Columbia
Business School. Prior to joining EY, Carl served in the Global War on Terrorism as an officer in the
U.S. Navy.

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