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Cognitive computing for automated internal fraud detection
June 2020
Internal fraud – the biggest competitor you didn’t know you had
1 in 2organizations
fall victim to fraud every year
52%of fraud cases
are insider fraud
32%of fraud cases committed by
business partners
2
*PWC 2018 Report: Pulling Fraud Out Of The Shadows. Global Economic Crime And Fraud Survey.
30
25
20
15
10
5
0
IT controls
Surveillancemonitoring
Account reconciliation
Internal audit
Managementreview
Documentexamination
Tip Externalaudit
By accident
Confession Notified bypolice
Med
ian
mon
ths t
o de
tect
ion
5months
6months
11 months
12months
14months
18months
18months
23months
24months
24months
24months
3
IT controls are the most efficient way to detect internal fraud*ACFE 2020 report to the nations: Global study on occupational fraud and abuse (Pg. 19)
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
TipInternalaudit
Managementreview
Byaccident
Accountreconciliation
Documentexamination
Externalaudit
Surveillancemonitoring
Notified by lawenforcement
IT controlsConfession
4
Yet they are hardly used*ACFE 2020 report to the nations: Global study on occupational fraud and abuse (Pg. 20)
5
Existing technologies are inadequate for purposeDeep learning or rules-based engines analyzing the single transaction are limited in their ability to distinguish a fraud from legitimate business transaction
Truly automating fraud detection requires a new approachMoving from examination of anomalies
to detecting actual fraud by understanding the context in which transactions take place
WHY IS THIS?
An automated and customizable platform that utilizes cognitive computing technology to continuously monitor organizational IT systems and detect business transactions/events that correspond to high risk fraud patterns.
6
COGNITIVE COMPUTING FOR INTERNAL FRAUD DETECTION
The advantages of cognitive computing
Cognitive computing is the use of computerized models to simulate the human thought process in complex situations where the answers may be ambiguous and uncertain.
Valire applies these cognitive principles to the identification of high-risk, potentially improper or fraudulent financial transactions.
01.
02.
7
How Valire’s fraud detection works
8
IT system
Track transactionsIn real-time
Put transactionin context
Analyze
Map tablesModel business
processes
!Alert!
Fraud scenariodata set
Fraud schemes vary widely, but they all reflect human concepts
Internal fraud requires a business process circumvention, created by a fraudster
Fraud schemes are often disguised as legitimate, normal transactions
Anticipate what a fraudster could do
Identify events that corresponds to fraud risk scenarios
Provide actionable alerts on suspicious transactions and entities
How cognitive fraud detection works
9
Understand the context and the relationship of each single transaction
Guide the machine to learn the business processes
Embed human logic & anti-fraud expertise into pre-configured algorithms
Fraud scenarios vary widely, but they all reflect human concepts
Embed human logic & anti-fraud expertise into pre-configured algorithms
Anticipate what a fraudster could do
Internal fraud requires a business process circumvention, committed by a fraudster
Guide the machine to learn the business processes
Identify events that corresponds to fraud risk scenarios
Fraud scenarios are often disguised as legitimate, normal transactions
Understand the context and the relationship of each single transaction
Provide actionable alerts on suspicious transactions and entities
Fraud Attributes Cognitive InsightCognitive Computing
Fraud scheme examples
Collusion in procurement cycle
Shell or high-risk companies
Bribery and corruption, kickback schemes
Conflicts of interest
Segregation of duty schemes, e.g., requestor / approver conflicts
Vendor's overbilling
Ghost employees
10
Vendor created inmaster data
01.
Vendor’sInvoicerecorded
02.
Vendor’sInvoiceapproved
03.
Payment
04.
B
ACTIVE VENDOR USER B
How does context detection look?
LEGITIMATEPROCESSA
USER A
How does fraud detection look in context?
Vendor’sInvoiceapproved
How does fraud detection look in context?
Vendor’sInvoiceapproved
Trigger activating
Valire’sdetector
Vendor filecreated inmaster data
01.
Vendor’sInvoicerecorded
02.Change of Vendor’sbank account details
03.
Payment
05.
B
USER B
How does fraud detection look in context?
Vendor’sInvoiceapproved
04.
VENDOR
A
USER A
Track invoice history
Vendor filecreated inmaster data
01.
Vendor’sInvoicerecorded
02.Change of Vendor’sbank account details
05.
B
DORMANT VENDOR USER B
How does fraud detection look in context?
Vendor’sInvoiceapproved
04.03.
A
USER A
Reveal context &
connections
Payment
Vendor filecreated inmaster data
01.
Vendor’sInvoicerecorded
02.Change of Vendor’sbank account details
05.
B
DORMANT VENDOR USER B
How does fraud detection look in context?
Vendor’sInvoiceapproved
04.03.
A
USER A
POSSIBLEFRAUD!
• Dormant vendor• Change of vendor
bank account • Same user change
bank & approved invoice
• No purchase order
Payment
VALIRE’S OPERATIONAL FEATURES
17
Data analysis portal Vulnerability test
Single casedrill down
Fine tune parameters as you go
Access to source documentation
Real timealerts
Adjustable risk scoring
Case management
Data analysisportal
Risk level monitoring
Real timealerts
Data analysisportal Vulnerability test
Single casedrill down
Adjustable risk scoring
Case management
Access to source documentation
Fine tune parameters as you go
Risk level monitoring
Valire’s Edge
18
Avoid false positive alarms by fine tuning the system to your unique
business processes
Leave no corner unchecked by monitoring 100% of transactions,
business events, data entries and entities
Reveal complex fraud scenarios by examining the entire process and the
context in which transactions are performed
Dramatically enhance auditing and monitoring capacity
thanks to hands free operation
Minimize corporate risk and financial losses
with continuous surveillance and real-time, specific, actionable alerts
Maximize corporate compliance through active
fraud risk monitoring
The entire data remains within the
enterprise IT system
Short deployment and configuration
Proof of Concept on historical data including
a vulnerability test
Off-the-shelfpre-configured library
of fraud scenarios
Tailored solutions to client’s unique needs
SaaS model with ongoing upgrades
VALIRE’S OFFERING
19
Where do you see your primary fraud risk areas?
20
Uri Tyroler – VP Business Development
• CEO, VP BD, VP Sales & Marketing in mid-size & public companies • 30 years experience in business development in foreign markets• Education – BA Economics, MBA Finance & Marketing
Amir Weinberg – CEO• R&D director in Mercury Interactive • Founder & President of Orsus Solutions • Education – BA Mathematics, Computer Science
Founders
21
Leaders
Leonard Vona, CPAHead of Fraud Risk
Assessment
Gal Amir, CPAHead of SAP
solutions
Max Kholmyansky, M.ScVP R&D
Vadim Levin,Product Dev.
Manager
Oren Gavish,Customer Solutions
Manager
22
THANK YOU!
23
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