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KRUPIN KIRILL
PROJECT
DATE NAME
11.01.2017
PROTECT FROM FRAUD
ADVANCED RESEARCH METHODS
Analyse and find most
effective way to fight
against fraud
Fraud and his types
Which combination of
methods and actions will
be the most effective?
Purpose
Subject
Research
question
AGENDA
LITERATURE REVIEW
“According to the 2008 edition of CyberSource’s Annual Online
Fraud Report, 38% of merchants reported a fraudulent order rate of
1% or more – that’s accepted orders; That means 38% of merchants
are in danger of losing their accounts as the 1% mark is where
merchant account providers start getting toey” - (Ecommerce)
“Data and device security will continue to be a major focus,
particularly for consumers. The more devices we allow our
technologies to connect with the more susceptible we become to
hacks and fraud – the October 2016 DDoS attack on Twitter, Spotify,
PayPal and other major services in the U.S. was a prime example” -
(Computerworld)
Monitoring
Detection
Response
MECHANISMS TO FIGHT AGAINST
FRAUD
REGRESSION MODEL
Y = α + β1X1+ β2X2+ β3X3+ β4X4+ β5X5+ β6X6+ ℮
Where,
Y = Fraud Score;
β = coefficient (fraud rate/number of purchases);
α - is the significance level;
X1 - IP range (checking the geographical location of the customer, compliance
between IP cardholder/ online store buyer and IP buyer;
X2 - Email;
X3 - Proxy;
X4 - Fraud level in particular country;
X5 - Distance between IP address of buyer and IP of delivering;
X6 - BIN Bank;
℮ - is a random error;
TAXONOMIC ANALYSE
Fraud
Web Network
Web

Advertising
Credit cards
Telecommunic
ation
SubscriptionSuperimposedCredit card
Credit
application
Online OfflineClick inflation Hit shaving
For example: Who is the source? - What is fraud category? - What is the type of fraud?
QUALITATIVE METHOD
Adaptive method
Guidance method
Analytical method
use the previous data to
predict normal behavior of
user
analyse real world
examples of fraud
investigation of violations
FORMALISATION OF FRAUD SCHEMES (DETECTION OF EVENTS NOT TYPICAL
FOR THIS TECHNOLOGY, DO NOT FIT INTO A CERTAIN PATTERN OR
TEMPLATE SPECIFIC TO THE BUSINESS PROCESS OR CUSTOMER);
TIME ANALYSIS OF CLICK;
DEVICE-INFORMATION ANALYSIS;
CONVERSION TIME;
PROXY TIME;
IP;
USER-AGENT;
MAIN QUALITATIVE PARAMETERS
DISCUSION
THANKS FOR ATTENTION!

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Krupin kirill (fraud) research proposal

  • 1. KRUPIN KIRILL PROJECT DATE NAME 11.01.2017 PROTECT FROM FRAUD ADVANCED RESEARCH METHODS
  • 2. Analyse and find most effective way to fight against fraud Fraud and his types Which combination of methods and actions will be the most effective? Purpose Subject Research question AGENDA
  • 3. LITERATURE REVIEW “According to the 2008 edition of CyberSource’s Annual Online Fraud Report, 38% of merchants reported a fraudulent order rate of 1% or more – that’s accepted orders; That means 38% of merchants are in danger of losing their accounts as the 1% mark is where merchant account providers start getting toey” - (Ecommerce) “Data and device security will continue to be a major focus, particularly for consumers. The more devices we allow our technologies to connect with the more susceptible we become to hacks and fraud – the October 2016 DDoS attack on Twitter, Spotify, PayPal and other major services in the U.S. was a prime example” - (Computerworld)
  • 5. REGRESSION MODEL Y = α + β1X1+ β2X2+ β3X3+ β4X4+ β5X5+ β6X6+ ℮ Where, Y = Fraud Score; β = coefficient (fraud rate/number of purchases); α - is the significance level; X1 - IP range (checking the geographical location of the customer, compliance between IP cardholder/ online store buyer and IP buyer; X2 - Email; X3 - Proxy; X4 - Fraud level in particular country; X5 - Distance between IP address of buyer and IP of delivering; X6 - BIN Bank; ℮ - is a random error;
  • 6. TAXONOMIC ANALYSE Fraud Web Network Web Advertising Credit cards Telecommunic ation SubscriptionSuperimposedCredit card Credit application Online OfflineClick inflation Hit shaving For example: Who is the source? - What is fraud category? - What is the type of fraud?
  • 7. QUALITATIVE METHOD Adaptive method Guidance method Analytical method use the previous data to predict normal behavior of user analyse real world examples of fraud investigation of violations
  • 8. FORMALISATION OF FRAUD SCHEMES (DETECTION OF EVENTS NOT TYPICAL FOR THIS TECHNOLOGY, DO NOT FIT INTO A CERTAIN PATTERN OR TEMPLATE SPECIFIC TO THE BUSINESS PROCESS OR CUSTOMER); TIME ANALYSIS OF CLICK; DEVICE-INFORMATION ANALYSIS; CONVERSION TIME; PROXY TIME; IP; USER-AGENT; MAIN QUALITATIVE PARAMETERS