Transcript - CCCOBIN

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Transaction Due Diligence & Monitoring
Using Data Mining and Forensic Techniques to detect
fraud and collusive relationships
June 13 2014
By
Dr Abiodun Osiyemi
Forensic Science Consultants
(Training Copyright)
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Presenter: Dr Abiodun Osiyemi
Dr Abiodun Osiyemi
BSc, MBBS, MSc (Distinction, NG), MBA, MSc (Forensic Science UK), PhD (Mgt),
CMC, FIMC, MITD, MCFI
CEO , Forensic Science Consultants
Email: [email protected], Tel: +234-803-300-8505
Website: www.forensicscienceng.com
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Crime Scene Investigator and Forensic Science Expert (Trained at Staffordshire University,
Stoke – on – Trent)
Certified in document examiner, Trained in criminology and financial crime at Forensic &
Compliance Institute USA.
Dr Osiyemi is an award winning scholar & graduated with a GPA of 4.86 at the post graduate
school He possess varied experience as a crime investigator, forensic document examiner
and Certified Forensic Examiner for different clients
Dr Osiyemi has investigated numerous anti-counterfeiting investigation on currencies,
document and bank cheques He is the author-‘The Rules of Outstanding Achievements, &
Director, Harvard Associates Lagos.
Dr Osiyemi was nominated into ‘who is who in Nigeria ‘in 2003.
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Can you spot me?
Criminalists-UK 2011!
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Crime Scene Investigation !
Doing the right thing
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What we do for our clients
• Support clients to establish the forensic framework for collecting,
processing , analyze and store evidence
• We build capacity in forensic crime investigations
• We profile criminals and crime scenes
• Support clients to reconstruct crime scenes
• Conduct forensic investigations on documents, computer, mobile
phone, physical and trace evidence
• We represent clients in courts as experts
• We train and build capacity fop our clients
• We have our private forensic lab
• We partner with public & private sectors including Law Enfo. Agents
• We network with compliance and fraud examiners globally
• We represent PITAGORA SA (Switzerland) across West AfricaThey are a multimillion dollar company involved with forensic
equipment manufacturing and document examination expert
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Presentation outline
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Introduction
Definitions
Types of Fraud and Business Scams
Data mining and fraud detection
Digital Forensics & Fraud detection
Admissibility of expert evidence in law courts
Challenges of Fraud Investigation
Conclusion
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Introduction
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Do you agree?
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Introduction
• Fraud is a consistent fact of business life that
affects all sizes of organizations globally
• There is no full proof method to prevent fraud ,
however systematic approach van be used in
minimizing the risk.
• Two main types of fraud are carried out by the
perpetrators i.e. Internal and External fraud.
Internal fraud accounts for higher crime
occurrences in some countries such Australia
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• The financial loss as a result of financial
fraud accounts for billions of dollars
annually in various countries.
• Australian business and government lost
$5.8 billion a year and accounted for one
third of all crime occurring in the country
(Australian Institute of Criminology 2003)
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Financial Crime : The ‘Crack Team’
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Essence of Topic
• In order to accomplish analysis of large amount of
financial information when dealing with fraud and money
laundering, it is important to include Data Mining and
Digital Forensic in the strategies of the financial
institution.
• A combination of financial regulation compliance, digital
forensics and data mining is an efficient methodology
that enhance fraud detection , prevention and
investigation.
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Thinking and providing Solutions
Combating fraud & Corruption
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Definitions
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Fraud definition
• No precise legal definition of fraud exist. The term fraud is
used to describe acts such as deception, bribery, forgery,
extortion, corruption, theft, conspiracy, embezzlement,
misappropriation, false representation, concealment of material
facts, collusion etc.
• Fraud can be simply defined as deception.
• A known misrepresentation of the truth or concealment of a
material fact to induce another to act to his or her detriment Black Law
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• For practical purposes, fraud may be defined as the use of
deception with the intention of obtaining an advantage or
causing loss to another party
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The Fraud Triangle
Commonly Accepted Theory for Internal
Fraud:
• Motivation / Pressure
• Rationalisation
• Opportunity
Pressure
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Opportunity
AICPA: Condition of fraud
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Rationalization
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A Scam
A scheme designed with an intent to defraud a
group of individuals or an enterprise(s) or a
government(s) or a mix thereof, for getting some
personal benefit - financial or fame, which
requires violation of some law or flouting of
certain established controls and involves a team
work eg ‘419’
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Forensic Science
Definition:
• Relating to the use of science and technology in the investigation and
establishments of facts or evidence in a court of law
• Relating to or appropriate for courts of law (* Online English
Dictionary)
Forensics derives from Latin word ‘forum’ and applies to any thing that
relates to law
Law & Science
The philosophical foundation of the criminal justice system remains to
protect the innocent and to ensure that the truth emerges for any
matter before the court, thereby ensuring that justice is done.
For law enforcements to keep up with pace of criminal advancements,
other techniques of identifying criminals must develop and Science
has come permanently to the rescue with methods that depend less
on eye witness
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Digital Forensics
• The science which aims to identify ,
preserve, collect, validate, analyze,
interpret, document and present digital
evidence stored in electronic sources.
• Digital forensics applies to reconstruction
of events during criminal investigations, or
anticipates unauthorized actions
• DF when combined with Data mining can
be used to effectively pr
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Locard’s Principle
Principle of Exchange
• when a person comes into contact with an
object or another person, a cross transfer
of physical (also-Virtual / Electronic)
evidence can occur
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Collusive Relationships
• Secret agreement between parties for an
illegal purpose; conspiracy
• E.g. Collusive relationships to carry out
money laundering, financial fraud,
terrorism etc
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‘Partners’ at work
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Fraud Techniques
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The “Three-Call” Technique
The Infallible Forecaster
Baits to Lure in
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Aspect of Risk
Show of Familiarity
Doing you a favour
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An understanding of Psychology
Avoidance of Questions
High Pressure Sales Tactics
Fancy Corporate Names
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Howdy Partner
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Corruption
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Fraudulent statements
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Types of Frauds
Common financial fraud / crimes:
• Money laundering
• Financing of terrorism
• Telemarketing fraud
• Business opportunities
• Investment frauds
• Business scams
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Business Scams
Bank Fraud
Business Loans
Internet Services
Cheque Fraud
Counterfeiting
Cramming
Fake Ads & Directories
Fraudulent Orders
Supply Scams
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Money Laundering
• Practice that hide the connection between
the sources of funds and their ultimate use
• Disguising the source of ill-gotten money
and making it appear to have come from
legitimate sources (organized crime) drugs,
D&C etc
• Laundering process washes dirty money and
makes it appear clean
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• In organized crime , Dirty money is laundered to become
clean
• In Terrorism generally, Clean money is Laundered Dirty!
• In both cases above the origin of the money is disguised!
NB
Terrorist organisations are becoming syndicates that raise
money through illegal activities that generate large
amounts that cannot just be deposited in the bank!
Not all the money is in the bank , however they use wire
transfer, deposits etc as convenient way to move money
across borders
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Money laundering process
The basic phases are:
Placement
• Smuggler ABC Introduces illegally obtained profits into
financial system
Layering
• Smuggler MEXY transfer $1,000,000 to offshore company XYZ
which he owns ( NB: ownership of company XYZ is protected
by privacy law in the offshore country). Company EFG in USA
owned by MEXY sells a property to Company XYZ
Integration
• Smuggler MEXY is paid $500,000 for consulting work done for
company EFG
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Techniques in Money Laundering
Placement
• Smurfing
• Cash smuggling
• Negotiable
instruments
• Cash exchaneg for
negotiable goods
• ATM Deposits
• Cash Value
Insurance policies
• Corporate bank
accounts
Layering
Integration
• Tax Havens &
Offshore Banks
• Use of haven bank
credit cards
• Bank Secrecy Law
as a layering tool
• Receiving as
consulting or
directors fee
• Offshore (Offshore
trusts)
• Arrangement of
corporate loans
• Shell corporations
• Proceeds of
gambling
• Walking accounts
• Buy a bank
• Financial
intermediaries
• Real estate
transactions
• Stock Purchase
• Use of business
• International
importing and
exporting
• Buy a bank
• Buy a banker
• Use of free trade
zones
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Anti-Money Laundering Laws & Organisations
Financial Action Task Force (FATF)
• Established in 1989 by a group of 8 countries (Canada, France,
Germany, Italy, Japan, UK, US, Russia)
• The FATF produced 40 recommendations to combat money
laundering and 9 recommendations to combat terrorist financing
Some of the 40 recommendations
• Creating and implementing international anti-money laundering
conventions
• Criminalizing the act of money laundering
• Setting policies and procedures for seizing money laundering
proceeds
• Requiring financial (and certain nonfinancial ) institutions to
implement due diligence anti-money laundering programs
• Increasing international cooperation in anti-money laundering
investigations and prosecutions
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Fraud Control
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Risk assessment
Prevention
Investigation
Detection
Bankers role & compliance
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Transaction due diligence & monitoring
Bankers Role & Compliance
Banks Compliance program
• Purpose
• Benefits to examiners
• Benefits to Bank
Compliance program elements
• Senior management commitment
• Compliance officer
• Internal audit
• Internal controls
• Independent testing
• Training
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Fraud: High Risk Areas
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Deposit taking
Sale of official cheques and negotiable instruments
Wire transfers
Loans
International correspondent banking
Special use account
Private Banking
Trust department
Brokerage operations
Trade financing
Internet Banking
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Non Bank
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Securities dealers, brokers
Insurance companies
Credit unions
Saving Institutions
Cooperative and NGOs
Exchanges
Money transmitters
Credit card companies
Leasing firms
Privatization agencies
Factoring firms’
Pawn shops
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Non Bank (contd.)
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Casinos, lotteries & gaming rooms
Dealers in precious metals and jewels
Accountants
Auto dealers
Lawyers
Notaries
Artwork dealers
Antique dealers
Real estate sales
Pension funds
Investment advisors
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Fraud Detection
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Fraud detection process
• Involves identifying indicators of fraud that
suggest a need for further investigation
• Indicators are only suggestive of fraud and
if investigated might lead to discovery of
fraud
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Objective of Fraud Detection
• ‘Catching’ Fraud
• Preventing fraud
• Note that if the risk of rapid detection is
high, fraudster are generally less likely to
attempt fraud!
• Also rapid investigation and follow up can
deter repeated fraudulent activities
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Fraud detection
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Tips and hotlines
Accident
Financial statement Auditors (SAS No. 99)
Internal auditors
Inspectors General (e.g. US)
Security department (Information security
department)
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Fraud Detection in ERM Process
• Prevention – First line of defence
• Detection – second line of defence
• Correction – Third line of defence
Fraud detection is part of the larger
enterprise risk management!
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Data Mining & Fraud Detection
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Data Mining
This is an extractive process of Information
Also known as Pattern Data Analysis, has been used to
detect fraud as well as tool to improve business
processes and better compete in the market. (Beer &
Diapers story!)
Data is produced at a phenomenal rate and we have more
ability to store information therefore users expect more
sophisticated results
DM- Statistical Analysis + Artificial Intelligence
Objective:
• Fit data to a model
• Obtain potential result that may not be obvious from raw
data
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Data Mining
Similar Terms
• Pattern data analysis
• Exploratory data analysis
• Deductive learning
Tough one!
• Unaccompanied Animal couriers – The
donkey , the pigeon (Arab world!)
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Sources of Data
A: Internal control Data
• Reconciliation failure
• Control total failure
• Exception transactions
• Apparent Errors
B: Basic tips & hotlines
C: Security breaches
D: Pattern data
• Records and inventory falsifications
• Software manipulation
• Control override
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Getting started
How easy is it to Uncover hidden information
in these transactions from :
• 100,000,000 cards
• up to 400 transactions per second (peak
hours)
• up to 15,000,000 transactions per day
• 3000,000,000 transactions per year
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Know Your Customer (KYC)
Examine for coverage of:
• Citizens accounts
• Business accounts
• Monitored Accounts
NB!
Mine your customers but Remember the have a
right to their privacy too!
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The Typical financial fraudster
• Male
• 36-45 yrs
• Commits fraud against his own employer
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Data Mining
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Data mining Essence!
• Identification of profile variables (Know Your Customer)
• Sampling (very skewed distributions)
• Development of the scoring model
Optimization criterion: what do we optimize: (Before, During & AfterProfile recognition & predictability
• Number of detected fraud transactions?
• E.g. -Number of detected fraud cards?
• Amount of money saved?
Triggers of Likely fraud
• Red flags, Red flags that trigger SAR
• Suspicious Activity Reporting Requirements
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Data Mining Challenges
No “universal fraud patterns”
What is normal for one cardholder is
unusual for another
Fraud patterns changing dynamically
Thieves are clever: action => reaction
Huge volumes of data
Hundreds of transactions per second,
millions of accounts
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Data Mining
Data mining is also known as Pattern Data
Analysis.
• Data Mining employs different rules such as
association and classification rules therefore
different form database monitoring .
• Normal data monitoring fail because of random
error or loopholes in the controls, monitoring
process and reporting process
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Why standard fraud indicators fail!
Standard fraud indicators can fail when
fraudsters intentionally circumvent then by
manipulating the very data that are
normally used to signal possible fraud eg
• Records and inventory classification
• Software manipulation
• Control override
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Data Base Vs Data Mining
Database:
• Find all credit applicants with the last name Awolade
• Identify customers who have saved more that $15,000
last month
Data Mining
• Find all applicants who are poor credit risks
(Classification)
• Identify customers with similar buying habits (clustering)
• Find all items which are frequently purchased with beer
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Data Mining Models & Tasks
Data Mining has both models
• Predictive
• Descriptive
Predictive
• Classification
• Regression
• Time series analysis
• Prediction
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Descriptive
• Clustering – groups data together into clusters
• Summarization – maps data with associated descriptionCharacterization and generalization
• Association rules
• Sequence discovery
• Link Analysis Uncovers relationship among data – Affinity analysis
, association rules, sequential analysis determines sequential
patterns
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Predictive
• E.g. - Time series analysis
• Example of stack market
• Predict future values
• Determine similar pattern over time
• Classify behaviorr
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Data Mining & Knowledge Discovery in Databases
• Data mining involves use of algorithms to
extract the information and patterns
derived by the KDD process
• KDD is Knowledge Discovery in
Databases: It is the process of finding
useful information and patterns in data.
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Data mining process (SEMMA)
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Sampling
Exploration
Modification
Model
Assessment
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Data driven fraud detection
• Sample:
Select data with some fraud cases
• Explore: work with sample and identify fraud predictors
• Modify :
consider revising sample and set of
predictors
• Model:
use predictors to develop a prediction mode;
• Assess:
Apply model to test samples. Assess
performance
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Data mining Vs human processing
• Can uncover fraud that would go unnoticed
• Humans are not capable of processing more that a handful of data
items when making decisions
• Humans are not capable of monitoring huge volume of data
especially associated with modern business processes
• Humans can identify that a the risk of a credit card fraud is high with
new accounts but may not know that a card has an elevated risk for
fraud. E.g. the type of purchases card holders make can make credit
card companies cancelling card the card holders cards.
• Eg string of ATM charges made in casino during working hours
• Credit card charge for
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Data mining & Fraud detection
tools
DATA MINING & BUSINESS INTELLIGENCE SOFTWARE
• The Micro strategy Business Intelligence platform
• SAP Business object
• SAS Data mining
Other Data –driven fraud detection applications and tools
• FraudPoint
• Experian Detect
• The US IRS Electronic Fraud Detection system ,,,
• Actimize employee fraud solution
• FraudLabs – Frauds detection web service
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Fraud Detection Vs Collusive Relationships
Relationships can be via
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Email communications
Fund transfers
Financial exchange
Memberships in groups
Memberships in organisations
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Social Network Analysis:
The benefit of social network analysis is that it can
sometimes fully identify all major players in a fraud
scheme. Police investigators routinely analyze phone
rerecords to identify relationships between suspect and
the others . (collusive relationships)
Applications for fraud detection:
• SAS’s social networking analysis model
Content Analysis and Text analysis
Benford Analysis : Presents another interesting analysis
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Forensic Techniques & Fraud detection
Essence:
To forensically identify, collect and analyze
financial evidence .
The goal of the investigator is to collect evidence relevant to the fraud
under investigation. Such evidence , when well organised , provides
answers to the classic sleuth’s questions regarding to the possible
fraud: Who, what , when , when , where, how and why.
The most important questions are
What was the fraud?
What was the loss?
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Steps in building a fraud detection system
• Risk analysis and control development
• Exploitation of expert knowledge
• Knowledge discovery
Data mining process (SEMMA)
• Sampling
• Exploration
• Modification
• Model
• Assessment
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Data driven fraud detection
• Sample:
Select data with some fraud cases
• Explore: work with sample and identify fraud predictors
• Modify :
consider revising sample and set of
predictors
• Model:
use predictors to develop a prediction mode;
• Assess:
Apply model to test samples. Assess
performance
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Digital / Computer Forensics &
Fraud Detection
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Forensic Science is the application of science to legal
matters
special interest to financial crime investigations:
• Digital / Computer forensics
• Mobile phone forensics
• Criminalisitics
• Dactylography
• Forensic Evidence
• Forensic Identification
• Questioned Documentation Examination (Palaeography)
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General Rule of Forensic Investigations!
• Every Crime Scene must be processed in the
same manner bearing in mind the 7S’s of Crime
Scene investigation
• The forensic accountant focuses more on the
financial records, money or transaction trails but
must still abide with the general rule to avoid
inadmissibility of evidence
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7 S’s of CSI
1.
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6.
7.
Securing the Scene
Separating the Witnesses
Scanning the Scene
Seeing the Scene
Sketching the Scene
Searching for Evidence
Securing and Collecting the Evidence
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Sketch the scene & lable
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Digital / Computer Forensics &
Fraud Detection
Collection and Preservation of Evidence at the crime
Scene
• The criminalists are trained to collect evidence at the
crime scene
• They have special training in computer and information
systems, which is becoming more important as computer
criminals become more sophisticated!
- They are called Computer Analysis and Response Team
(CART) They are Computer – Specialize Criminalists)
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Digital Forensics- Extraction Methods
Logical
Extraction
File System
Extraction
Physical
Extraction
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Collusive Relationship: Mobile Forensic Analysis
Caught 3 suspects
Do they know each
other or have they
contacted mutual
parties?
What are the
important
connections?
How do they
communicate?
Is there
investigation
related data?
Were they in the
same place and in
the same time?
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Forensic Document Examination - Cheque
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Admissibility of expert evidence
• In the United States of America, there are two general tests for
admissibility. In 1923, in the case of Frye test versus United States
(also referred to as Frye test or general acceptance test) it was held
that the court will go a long way in admitting expert testimony
deduced from scientific principle or discovery which must have
gained general acceptance in a particular field it belongs.
• Also in 1993, in the case of Duabert versus Merrel Dow
pharmaceuticals Inc, the Supreme Court ruled that proof that
establishes the scientist reliability of expert testimony must be
produced before it must be admitted .
• Finally for the FDE to be of important relevance to the justice system
they must have appropriate qualification to qualify to give
expert testimony and be up to date with developments in their
field through continuing education.
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Expertise of Computer Analysis and Response Team
They have expertise in 9 areas
• Content
• Comparison
• Transactions
• Extraction
• Deletion
• Format conversion
• Keyword searching
• Password recovery
• Limited source code
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The Investigation teams
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Evidence Response Team (ERTs)
Questioned Documents Unit
Investigative and Prospective graphics unit
Racketeering Records unit
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Steps in Forensic Investigations
Note: Great care and discipline must be exercised in preserving
computer and physical evidence
Step 1: Size up the situation
Step 2: Log every detail
Step 3: Conduct the initial survey
Step 4: Assess the possibility of ongoing undesirable activity
Step 5: Power down
Step 6: Check for Booby traps
Step 7: Duplicate the hard drive or other permanent storage unit
Step 8: Analyze the Hard drive
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Law Enforcement Database Networks
Many databases are helpful during Investigations and Law
Enforcement agents have access to them especially in the G8
countries, and other ‘developed countries’!
The databases includes:
• Automated Fingerprint Identification System (AFIS=IAFIS)
• National DNA Index System (NDIS)
• Combined DNA Index System (CODIS)
• Financial Crimes Enforcement Network (FinCEN)
• National Law Enforcement Telecommunications Systems (NLETS)
• National Crime Information Center (NCIC) network
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Fraud Investigation Challenges
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Bankings’ role in facilitation of the activity
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Acceptance of flight capital by western
countries
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Laws and limitations of other countries
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Jurisdictional conflicts and lack of international
coordination
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Bank Secrecy
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Volume and complexity of international
transfers of funds
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Internet based banking
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Tax heavens as sanctuaries
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Offshore corporations
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Having to prove fraudulent transfer
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Shortfall of reporting requirements
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Criminals influencing Government and Bank
support
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The widespread use and acceptance of trade mispricing
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Combating Fraud: Measure of Success
“Is there anywhere that the Secret Service won’t come
looking?”
“Anti US regime country, are safest place”
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Conclusion
• Fraud detection is an unending challenge because fraudsters
are forever inventing fraud schemes
• Data Mining must be included in the strategies of financial
institutions to be effective in predictive and other modes of
fraud detection
• Forensic techniques are essential for detection and
investigating frauds if they are to be admissible in the law
courts
• The roles of the regulators, bankers , law enforcement agents
must be defined and effective in fraud control
• An integrated approach would not eliminate fraud completely
but would make a huge impact in detecting and curbing fraud
• CCCOBIN must partner with forensic scientists and other stake
holders to form a formidable winning team as a way forward
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Acknowledgements
The reference materials
• Anti money laundering methodology: financial regulations, information security and
digital forensic s working together. Flores . D. A et al
• Fraud : A guide to its prevention , detection and investigation:
PriceWaterHouseCoopers
• William S. Hopwood et al Forensic accounting and Fraud Examination : Second
ediction :
• James Wright : Bank Examinations Techniques
• Wojtek Kowalczyk : Detecting fraud with data mining
• Celebrite – Roy Shamir
• David A. Iacovetti. Financial crimes and emerging criminal trends
• Internet online sources
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Thank you
Contact details of Presenter
Dr Abiodun Osiyemi
+2348033008505
CEO: Forensic Science Consultants
[email protected]
www.forensicsciencng.com
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