1. FROM THE BATTLEFIELD TO THE BOTTOM LINE:
USING MULTI-NODAL NETWORK ANALYSIS TO
DETECT FINANCIAL RISK
Christopher Kenly
Aveshka, Inc.
Vice President
Innovative Analytic Solutions
2. 2
Bottom Line
We can leverage the
lessons learned from
the government’s
change in the way it
is doing business
after 9/11 and apply
them to managing
financial risk.
3. 3
Pre-9/11 Threats
Pre-9/11, how did we think about national security threats?
Regional Concerns
Economic Interests
Nation-States
Narcotics
4. 4
Post 9/11
We are faced with loosely
affiliated groups united by
ideology
• Not tied to nation-states
• Independently funded
• Operating globally
• Operating clandestinely
• Difficult to track/trace
How do we view the new threat environment?
5. 5
Post 9/11
What has the battlefield looked like?
• No frontline; attacks
throughout the country
• Limited visibility into
enemy organization &
operations
• Evolving adversary
tactics & techniques
6. 6
Post 9/11
How to deal with new threats?
• New government agencies and programs
to facilitate information sharing
• New efforts to collect and share critical
infrastructure information and exploit
open-source data sets
• Enhanced analytical techniques
throughout government
7. 7
Post 9/11
How to deal with new threats?
• Multi-nodal network analysis to
provide a 360⁰ view of threat
network
• Identifies previously unknown
linkages using Dark Network Theory
8. 8
Analytics in Action
• New tools to identify and analyze threat streams, financial networks,
and communications and conduct targeting and travel screening
• New technologies and techniques that allow an analyst to query data
from multiple databases rapidly and comprehensively to provide
information awareness and reveal hidden relationships
10. 10
Financial “Battlefield”
What does the financial “battlefield” look like?
• Globalization of risk
• Company brands at risk
• Millions of transactions
• Non-transactional data
• Social media sources
• Proprietary data sources
11. 11
How does it currently work?
Without analytics, researching and planning for the mitigation of risk is
a cumbersome and intricate task.
Financial
Risk and
Exposure
Unknown exposure to
risks results
misinformed decision
making
Sifting through massive
data sets, located in
disparate
databases, and existing
in various formats
Traditional mitigation
based on transactional
analysis often fails to
identify hidden
relationships and
latent risks
Analytical accuracy and
speed can vary across
organizational
divisions, experience
levels, and subject
domains
12. 12
Lessons Learned
• Use multi-nodal network analysis and data analytics to help mitigate risk
• Multi-nodal network analysis
• Identify conflicts of interest
• Identify derogatory information (e.g., bankruptcies)
• Map social media relationships
• Determine front companies
• Identify operational tactics
• Data analytics
• Connect the dots
• Identify linkages quickly
What lessons can we take from the battlefield and apply to
financial risks?
15. 15
Benefits of Analytically Enabled Multi-Nodal Network Analysis
Seamless Single intuitive interface enables access to vast quantities
of data from disparate sources
Flexible Cloud-based scalable architecture enables tailored inputs
and outputs – analysis and reporting on your terms
Cost-effective Independent of your infrastructure so that it can be
accessed anywhere with lower total lifecycle costs
Powerful
Customizable visualization tools highlight hidden
relationships and new avenues of inquiry
Efficient Automated data collection and prioritization of results
shrink the analytic timeline
17. 17
Points of Contact
SCOTT SCHNEIDERMAN
o | 571.814.5737
m | 917.209.8829
e | sschneiderman@aveshka.com
PRINCIPAL
Innovative Analytic Solutions
ADAM KILLIAN
o | 571.814.5729
m | 571.243.4388
e | akillian@aveshka.com
PRINCIPAL
Innovative Analytic Solutions
CHRIS KENLY
o | 571.814.5702
m | 609.372.5120
e | ckenly@aveshka.com
VICE PRESIDENT
Innovative Analytic Solutions
KELLI COOPER
o | 571.814.5745
m | 706.564.2411
e | kcooper@aveshka.com
ANALYST
Innovative Analytic Solutions
Editor's Notes
Over the past decade, we are witnessed changes in the risk environment. Risks to companies have increased due to the continued lack of transparency in accounting, leaks, and loss of IP. From a government perspective, the Internet has also increased the ability to perpetuate fraudThere is clearly a need and demand for solutions that can detect these frauds before they occur and investigate them rapidly once they are detected.
Over the past decade, we are witnessed changes in the risk environment. Risks to companies have increased due to the continued lack of transparency in accounting, leaks, and loss of IP. From a government perspective, the Internet has also increased the ability to perpetuate fraudThere is clearly a need and demand for solutions that can detect these frauds before they occur and investigate them rapidly once they are detected.
Traditional investigation is “follow-the-money” approach. Social media information provides a different lens that reveals additional relationships. Traditional investigation involves background searches on individuals of interest, interviews, generating leads, more interviews, etc. Automation of data collection can short-cut this process.
Just as the government is turning to the private sector to manage and consolidate all of its data, the financial sector is as well.New York, London, and Zurich all understand that risk can be managed when the “dots are connected” Analytic platforms can provide linkages in seconds that a team of analysts might not find for years
