Behavioral Analysis for Financial Crime Threat Mitigation

FOR FINANCIAL CRIME
THREAT MITIGATION
BEHAVIORAL
ANALYSIS

2
CONTENTS
WHAT IS BEHAVIORAL ANALYSIS? (p3)
ENABLERS (p4)
CAPABILITIES (p5)
PROCESS FLOW (p6)
TECHNIQUES FOR DEPLOYMENT (p7)
USE CASES (p8)
VALUE PROPOSITION (p10)
HOW WE CAN HELP (p11)
Copyright © 2018 Accenture. All rights reserved.

WHAT IS BEHAVIORAL ANALYSIS?
Behavioral Analysis is the utilisation of the customer and their network’s financial and non-financial data to understand the degree of
financial crime risk posed to the bank. This is facilitated through data-driven assessment of a customer’s behavior against known
suspicious and non-suspicious behavioral attributes and can be delivered through the deployment of various business and technology
capabilities.
• Today’s Financial Crime Risk Management is often characterized by a reactive, rules-driven, detection approach. Such an approach, which relies on static, filter-driven
data often fails to recognize subtle differences in behavior patterns or links between customers that could be prime indicators of suspicious activity.
• Analytics technology including behavior centric, network-driven analysis create an opportunity for improving outcomes and efficiency in financial crime mitigation.
• Behavioral analysis works by understanding the proximity of customer behavior to indicators of potential financial crime threat. This includes both known threats such as
confirmed money launderers in addition to known patterns of behavior indicative of financial crime risk.
• Behavioral analysis allows financial institutions to go beyond the normal anti-money laundering (AML) investigative protocols and look for and report on unusual patterns
of activity between seemingly unrelated accounts that have no apparent economic purpose. This can lead to an increased chance of detecting suspicious behavior and
aids better prioritization of alerts.
• To improve the effectiveness of behavioral analysis, banks should develop their foundational business and technology capabilities as part of the wider financial crime
ecosystem, including consolidation of data across sources and risk types, behavioral attributes, and entity resolution.
Current State Overview
Traditionally identification
of financial crime risk has
been based on an
individual transaction
view …
… and direct
transactional
relationships …
… supported by
internal data sources
Financial crime risk would
be identified through
customer scoring
indicating proximity to
potential threats
… and by entity resolution
and segmenting of
customers with similar
behaviors
…supported by compilation of
financial and non-financial
information across internal and
external sources
Future State Overview
Copyright © 2018 Accenture. All rights reserved. 3

Consolidation of Data Sources
• Compilation of internal and external data both
financial and non-financial across threat types.
• Continual data monitoring, ingestion and
consolidation over time.
• Add additional data sets to increase the number
of unique features available for holistic threat
assessment and manual investigation.
Behavioral Attributes
• Use of data to create features used to build and
maintain a customer specific risk score or “behavioral
fingerprint.”
• Continual update and timely reflection of a customer’s
changing data attributes within the Customer Risk
Score.
• Ultimately permits the identification of behavior
matched to known AML behavioral patterns.
Internal Client Data e.g. Know Your
Customer (KYC), Fraud and
Transaction History.
Internal Records, e.g. Internal
Watchlists and suspicious Activity
Reports (SARs) Records.
Data from Governmental Bodies e.g.
Watchlists.
News and Social Media Searches.
Paid for 3rd Party Consumer Data e.g.
credit data.
Public Domain, e.g. public accounts.
BEHAVIORAL ANALYSIS - ENABLERS
Customers employment industry
Sector.
Geographic, product and behavioral
risks.
Volume, frequency and sentiment of
media coverage.
Reported to actual income ratio.
Establish connections with high risk
individuals / entities and
geographies.
In order to carry out true Behavioral Analysis, key enablers such as those indicated below should form part of the transaction
monitoring process.
Entity Resolution
• Connection of disparate data sources to allow for
validation, resolution and deduplication of data
attached to customers.
• Continual entity resolution as new data becomes
available and new customer relationships are
established.
• Provide a single customer view to aide customer
segmentation and cluster analysis.
Ingestion and consolidation of
data sets from multiple sources.
Link data which references a
given customer and deduplicate
this data.
Create a singular customer view.
4

Comparison Against Similar Customers
• Grouping together customers with similar
characteristics and behavioral attributes into a
network cluster.
• Customers can be analyzed against clusters with
expected similar behavioral attributes for behavior
which does not align.
• An understanding of the degree of separation and
identification of physical links between customers
and known suspicious can be established.
Typology Matching
• Past investigations and associated information provide
a wealth of information which can help with future
investigations.
• The historical decisioning and rationale is combined
with case attributes to help trigger future alerts by
applying similar logical decisioning.
BEHAVIORAL ANALYSIS - CAPABILITIES
Behavioral Analysis aids with the identification of suspicious activity by identifying customers whose actions do not align with
expected behavior or are similar to the behavioral attributes of known guilty parties.
Analysis Against Guilty Party Data
• A lot of effort today is spent on defining and ring
fencing non-suspicious behavior.
• By combining regulatory and enforcement agent
data of known guilty parties and their
characteristics, together with known non-
suspicious behavioral attributes, inferences can
be drawn to highlight events or entities requiring
scrutiny.
A subject runs a convenience store
and is grouped into a community
based on similarity of the behavioral
attributes.
Analysis identifies that the subject’s
behavioral attributes are diverging
from the expected behavior of the
segment with large transactions to an
overseas manufacturing firm.
An alert is generated on this subject
for further investigation.
Ingestion of know guilty party
data and associated behavioral
attributes.
Subjects in client database are
analyzed for behavioral similarity
to behavior attributes which
indicate a known higher
propensity for suspicion.
Insights inform alert generation and
prioritization.
Internal data provides a record
of transaction history and past
case outcomes.
Subject’s behavioral attributes
are analyzed for behavioral
similarity to subjects upon which
cases were previously reported.
Insights from analysis hep with case
discounting and alert generation.
5

BEHAVIORAL ANALYSIS - PROCESS FLOW
Using behavioral and network analysis capabilities can enhance current transaction monitoring processes and permit a holistic
overview and assessment across internal and external data to identify financial crime.
Behavioral Analysis Enablers
Entity
resolution
Behavioral
attributes
Data
consolidation
Behavioral Analysis Capabilities
Comparison against
similar customers
Typology
matching
Analysis against known
guilty party data
6

BEHAVIORAL ANALYSIS -
TECHNIQUES FOR DEPLOYMENT
Comparison against similar customers
Typology Matching
Analysis against
known guilty party data
Filtering, Weighting, Prioritization
External Data
Consolidation
In addition to the high
level view of the core
components of
our Behavioral
Analysis, what
follows are the key
ecosystem vendor
capabilities to
support this vision.
Arachnys Information
Services, Ltd.
encompass corporation
Pitney Bowes Inc.
Ripjar Limited
Quantexa Limited
Ripjar Limited
SAS Institute Inc.
ThetaRay Ltd.
Accenture Digital
Ayasdi, Inc.
Nice Ltd (Actimize™)
Oracle Corporation
7
Current vendor capabilities would enable
consolidation of data from external
sources (e.g. negative news searches).
However, access to crime agency /
regulator data and ability to model this
data has not been considered.
Several vendors provide risk scoring and
prioritization as part of their offering. A
tailored solution to consolidate the output of
vendor products and to provide a holistic risk
score would be required, and dependent on
the capabilities deployed.

BEHAVIORAL ANALYSIS - USE CASES (1 OF 2)
USE
CASE #1:
DETECTION
Behavioral Analysis detects
new money-laundering
threats by identifying
emerging communities and
assessing the patterns of
behavioral when compared to
subjects of a similar type.
Ben’s behavioral patterns are identified as
unusual in relation to his peer group.
Contextual analysis on this outlier
identifies Ben and counterparties as part of
an emerging community which pose a
financial crime risk for example Fine Art
Trafficking.
BENEFIT
Targeted behavioral
insights accelerate threat
detection, help discover
new typologies and focus
efforts on higher risk
cases.
USE
CASE #2:
DETECTION
While Ben’s transaction pattern of
behavior is not itself suspicious, Ben is
shown to transact with a known money
launderer.
BENEFIT
Increase in both the quality of
investigation and the ability to
identify previously
unknown suspicious
behavior.
Behavioral Analysis allows an
understanding of the degree of
separation across the
customer base and the
identification of physical links
between individuals.
Confirmed
money
launderer
Behavioral Analysis can assist banks to detect and prioritize potential suspicious behavior for alert investigation.
8

BEHAVIORAL ANALYSIS - USE CASES (2 OF 2)
USE
CASE #3:
PRIORITIZ-
ATION
Behavioral Analysis can
focus investigators’
efforts on real financial
crime by using
behavioral risk insights
alongside traditional
detection solutions to
prioritize alerts.
Confirmed
Financial
Crime
BENEFIT
Enhanced alert
prioritization, increased
efficiency of
investigations and
reduced cost of
compliance.
Behavioral analysis identifies Amy’s
violation behavior as resembling that of
non-suspicious customers, whereas
Ben has a strong behavioral similarity to
that of a customer who has previously
been escalated for structuring.
USE
CASE #4:
FALSE
POSITIVE
REDUCTION
Behavioral Analysis can
drive down false
positives by identifying
the likelihood of a
subject being involved
in suspicious activity.
BENEFIT
Reduction in false
positives and therefore
investigator workstack,
freeing capacity to work
on more complex cases.
Behavioral analysis demonstrates
that Amy’s behavioral fingerprint
is similar to that of non-
suspicious subjects, for example,
young professional first-time house
buyers, hence there is a low-
likelihood of the transaction being
suspicious and an alert is not
generated.
Alerts are generated
for Amy and Ben
Amy executes a series of
transactions in larger amounts than
her typical transaction – traditional
transaction monitoring systems
would have alerted Amy for a
“change in behavior” scenario.
Behavioral Analysis can assist banks to detect and prioritize potential suspicious behavior for alert investigation.
9

BEHAVIORAL ANALYSIS – VALUE PROPOSITION
Typology Matching
False Positives
can reach up to
20%
SUGGESTED APPROACH
Alert volume
reductions
can reach up
40%
Machine Learning was used to analyze
customers‘ transactions and prioritize most likely
suspicious transactions for investigation, isolating
those of lower risk.
Comparison Against
Similar Customers
Behavioral Analysis enhances identification of potential financial crime threats and facilitates complex investigation by exposing the
link between customer behavior and likely suspicious activity.
Filtering, Weighting
and Prioritization
SUGGESTED APPROACH
Through the use of Predictive Analytics, historical
investigations and case information were analyzed to
identify the relative risk of each alerted activity. A
self-learning algorithm delivered ever improving
refinement and ongoing alert reduction.
An increased number of identified lower risk
alerts, a significant reduction In false positives
which allowed resources and efforts to be
reassigned to higher risk customers and
behavior.
OUTCOME
By combining the capabilities of network and cluster analysis, internal typology development and filtering, weighting and prioritization, behavioral analysis allows banks
to better prioritize alerts, help reduce false positives and enable more focused investigation efforts on higher risk customers and behavior.
Use Network and Cluster Analysis to group
customers and transactions into specific segments
and understand links or behavioral similarity to
potential suspicious entities.
OUTCOME
SUGGESTED APPROACH
Enable investigation effort to be
focused on higher risk alerts, improving
quality and compliance and
generating efficiencies.
OUTCOME
False Positives
reductions can
reach up to
30%
Using machine learning to spot and identify
patterns in behavior at a micro segment
level, we have been able to better apply risk
and detection rules and drive a reduction in
false positive alerts.
10

BEHAVIORAL ANALYSIS - HOW WE CAN HELP
Accenture is uniquely positioned to support financial institutions in their Financial Crime Compliance journey. Building on our deep
experience in analytics, data and technology transformations, we are able to bring industry leading knowledge, assets and scalable
capabilities to deliver desired outcomes for clients.
Key Capabilities
People
Data Foundation
At Accenture we understand the importance of balancing
trustworthiness and quality with relevance and variability. Our
intelligent data foundation is built on four key capabilities:
• Data ingestion, provisioning and modelling
• Features engineering – identifying which key “features” are
important in financial crime risk identification
• Data trust and quality
• Data governance
Advanced Analytics and Machine Learning
Across a number of assignments, Accenture uses analytical tooling to help
reduce false positives, improve detection and increase operational
effectiveness. Capabilities include:
• Contextual scoring
• Customer segmentation
• Negative news screening
• Text mining
Partners and Vendor Relationships
Arachnys Information
Services, Ltd.
Ayasdi, Inc.
Fenergo Ltd
ForgeRock Inc.
Oracle Corporation
Quantexa Limited
Ripjar Limited
SAS Institute Inc.
ThetaRay Ltd.
Trulioo Inc.
11
Accenture’s alliances & relationships with leading vendors
allows us to deliver at pace across the Financial Crime Ecosystem:
• Digital ID, single customer view and entity resolution
• Threat identification and risk scoring
• Lifecycle management, workflow and intelligent investigation
• Data monitoring
• Data aggregation and segmentation
• Foundation technology
• Visualization
Accenture’s blended and scalable workforce capabilities and skills means we can provide knowledge and know-how across an array of
services, spanning globally. We can also provide financial services institutions with a unique combination of delivery experience alongside
tailored digital assets to support them in their Financial Crime Compliance journey.
• Predictive analytics
• Network analysis
• Digital identity

BEHAVIORAL ANALYSIS
About Accenture
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Disclaimer
This presentation is intended for general informational purposes only and does not take
into account the reader’s specific circumstances, and may not reflect the most current
developments. Accenture disclaims, to the fullest extent permitted by applicable law,
any and all liability for the accuracy and completeness of the information in this
presentation and for any acts or omissions made based on such
information. Accenture does not provide legal, regulatory, audit, or tax advice. Readers
are responsible for obtaining such advice from their own legal counsel or other
licensed professionals.

TO FIND OUT MORE
Carl Welford
Accenture Financial Services
Senior Manager
carl.welford@accenture.com
Archit Chamaria
Accenture Finance & Risk
Manager
archit.chamaria@accenture.com
Victoria Hale
Manager
victoria.a.hale@accenture.com
Matthew Roderick
Consultant
matthew.roderick@accenture.com

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