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E commerce frauds and anti-fraudulent tools

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ClementVivian Selwin

A study on E-commerce fraud and Anti-fraud tools and the general spending pattern of e-commerce customer. It included the methods of operations of e-commerce retailing and the general process flow in e-commerce

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E-Commerce frauds and anti- fraudulent tools •E-commerce order fulfillment •Major models of e-commerce •E-commerce frauds • Features of e-commerce tools •PCI DSS »Clement Vivian S »Chaarumathi M
Objective of the study • To ascertain the flow of an e-commerce order • To identify the various models of e-commerce • To identify the major types if e-commerce frauds Buyer side frauds Merchant side frauds • To identify the common features of anti fraudulent tools available for e-commerce • To identify the purchasing pattern of e- commerce consumers based in Chennai
E-commerce order fulfillment Step 1: Order is entered in the system Step2 : Custom shipping documents are placed in the fulfillment Step 3: Order is picked using hand held bar code scanner Step 4: Order is double checked, packed and billed by a second person Step 5: Order is shipped and inventory is automatically adjusted
E-commerce order fulfillment Step 6: E-mail is automatically sent to client with all necessary information and tracking numbers Step 7: Order delivered and feed back collected Step 8: Return procedures and after sale support
E-commerce order fulfillment 1.Drop ship model: • The seller has his product listed and updated in the retailer’s online platform ↓↓ • The customer places an order for the product ↓↓ • Seller directly ships the goods to the door step of the customer.
E-commerce order fulfillment 2. 3rd Party fulfillment model: • Products are stored or brought by the retailer and shipped to the end consumer by the retailer ↓↓ • Inventory of the seller is linked to the retailer to keep track of the inventory and plan on manufacturing.
E-commerce order fulfillment 3.Self fulfillment model: • Manufacturer and the retailer are the same ↓↓ • Customers directly place order with the manufacturer of the the product ↓↓ • The manufacturer directly ships it to the end consumer.
E-Commerce frauds - buyer side • Credit card fraud – Payment made using stolen card or stolen information about the card. Business is responsible for ensuring the genuineness of the transaction • Refund fraud – Intentional over payment made from a stolen credit card and refund initiated to a different account
E-Commerce frauds - buyer side • Merchant fraud – Fraudster sells and receives the payment for no-existent items. It is the business who is responsible for the reimbursement • Card testing – Practice of creating and testing the validity of a credit card number. Fraudster target websites which gives a reason for their card number declined
E-Commerce frauds - buyer side • Friendly fraud – Also known as Charge back Fraud, the consumer makes an online purchase and then claims his credit card has been stolen and asks for charge back after receiving the purchased goods or services • Phishing – E-mail asks for user ID, passwords, credit card details and other personal information
E-Commerce frauds - merchant side  Create a new account  List popular/fast moving items at extremely low prices • Non-fulfillment • Selling counterfeit • Pricing over MRP • Fake reviews and rating
Anti fraud tools • Anti-fraud software Simility Subuno Riskified Signifyd Swift Science • Personal Card Industry Data Security Services (PCI DSS)
Anti fraud software • Machine Learning – Provides the needed algorithms, application and framework to bring greater predictive accuracy
Anti fraud software • Automated work flow – Automated checking of payment fraud checks like identity theft – Reduces chargeback fees, investigation fees • Device Fingerprinting – Browser version – Operating system – Items installed (plugins/fonts etc.) – Location & time zone settings
Anti fraud software • Insight dashboard – This feature provides the retailer with the insight of every move of customer and the device from which the customer is shopping i.e (Mobile phone, laptop, desk top or tablet) • Chargeback guarantee – The clients are guaranteed charge back for all the transaction approved by the application. This makes the investment in these applications risk-free
Personal Card Industry Data Security Services (PCI DSS) • Personal Card Industry Security Standards Council (PCI SSC) formed in 2004 • Five major payment brands Master Card, VISA, JCB, American Express and Discover  Aim:  Reducing credit card fraud by strengthening security • Minimum requirements (PCI DSS) set in June 2005 • Applies to every organization who accept payment card • Four levels of organization → Level 1 to Level 4
Personal Card Industry Data Security Services (PCI DSS) • PCI DSS requirements: – Secure network system • Install and maintain a firewall configuration • Use of vendor-supplied defaults for system passwords disallowed – Protection to card holder data • Protect stored cardholder data • Encrypt transmission of cardholder data – Strong online security system • Protect all systems against malware and regularly update anti-virus
Personal Card Industry Data Security Services (PCI DSS) • PCI DSS requirements: – Access restrictions • Restrict access to cardholder data • Identify and authenticate access • Restrict physical access to cardholder data – Periodical monitoring and testing • Track and monitor all access to network resources and cardholder data • Regularly test security systems and process – Information security policy
Data analysis and interpretation: • Demographic profile: From the above chart it is identified that 65% of the respondents were female and 35% were male. This information was used to identified the relationship between gender and their behavior with respect to e- commerce.
Analysis 1: Frequency of online purchase of e- commerce consumers • Observed frequency of purchase and expected frequency of a purchases:
Analysis 1: Frequency of online purchase of e- commerce consumers • Test for normality using Chi-square: – H0 = There is no significance difference between expected and observed frequencies – H1 = There is significance difference between expected and observed frequencies
Analysis 1: Frequency of online purchase of e- commerce consumers • Observed frequency and expected frequency: • Calculation of median:
Interpretation 1: • The purchasing pattern of consumers from online stores is not normally distributed • Further the analysis of arithmetic mean and standard deviation shows that the data is widely spread from the mean. • On an average a person shops online once is 7 months
Analysis 2:Analysis of relationship between gender and spending pattern: – H0 There is no relationship between gender and purchasing pattern – H1 There is relationship between gender and purchasing pattern
Interpretation 2: • p-value of the mean ranks (Table.b) is 0.005 (<0.5) the mean ranks (Table a) are not equal • There is a relationship between gender and purchasing pattern • Female have a higher mean rank indicating that they shop online more frequent than male
Analysis 3:Average spending of e-commerce consumers
Analysis 3:Average spending of e- commerce consumers • Mann-Whitney Test: – Ho There is no significant relationship between gender and average spending – H1 There is significant relationship between gender and average spending
Analysis 3:Average spending of e- commerce consumers • Calculation of median of spending based on gender:
Interpretation 3: • Level of significance in both the tests of normality is 0.000 (<0.5), Hence the distribution of spending pattern of consumers is not normal as we see the • the median of average spending on each purchase by the consumers based on their gender was identified to be Rs 1858 for male and Rs 1720 for female • p value in table b is 0.551 (>0.5). Therefore, the mean ranks in table a are equal. Thus, males have a higher average spending pattern compared to female
Analysis 4:Products purchased through online retailing • Products purchased through online retailing (Expressed in percentages)
Interpretation 4: • Approximately 70% of male and female consumers buy clothing and accessories online • 70% male consumer buy electronics while only 38% of female consumers buy electronic products online. • The market for Consumer durables attract just 6% of the total consumers • Only 3% of consumers in the survey buy grocery and other products from online stores
Analysis 5: Merchant side fraud • Duplicate products delivered • Non-Delivery of product • Return not approved • Cash not refunded • Damaged product received • Wrong product delivered • Product sold over MRP • Significant difference between the product displayed and the one delivered • Items missing in the package • Product not delivered on time • Identity theft
Analysis 5: Merchant side fraud Merchant side frauds
Interpretation 5: • 74% (100-26) of customers have been a victim of e-commerce frauds by the merchant. • 3 in every 10 orders processed ends up with a damaged. • 24% of the consumers complain that the product delivered is significantly different from the product displayed. • While there is also a 21% compliant that the product ordered has not been delivered.
Findings: • Significant growth in the use of data consumption in India there has been a significant level of growth in online retailing in the country • Increase in scale of operations the retailers and manufacturers have to work on selecting the best suitable model for their product to keep the cost as low as possible • Due to increase in the volume of online transactions there is a high possibility of frauds through cyber crimes • As a result of the above the market for anti-fraudulent tools in e-commerce business is growing
Findings: • As the fraudsters come up with new methods to commit a cyber fraud there is a need for constant development in the security system of the business • Major credit card suppliers have formed a council in 2005 to curtail the activities of cyber fraud by protecting the identity of credit card holders • The online market for Electronics and Clothing is doing well. While other product lines share a significantly low market share • There is significant level of fraud activities on the merchant side as well.
Conclusion: • Due to the rapid growth the industry has also become a target industry for fraudsters as there is ample of gaps in the process. Therefore, there is a requirement for development of anti- fraudulent tools for e-commerce industry to grain the trust of the consumers which is very vital for the growth of e-commerce industry

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E commerce frauds and anti-fraudulent tools

  • 1. E-Commerce frauds and anti- fraudulent tools •E-commerce order fulfillment •Major models of e-commerce •E-commerce frauds • Features of e-commerce tools •PCI DSS »Clement Vivian S »Chaarumathi M
  • 2. Objective of the study • To ascertain the flow of an e-commerce order • To identify the various models of e-commerce • To identify the major types if e-commerce frauds Buyer side frauds Merchant side frauds • To identify the common features of anti fraudulent tools available for e-commerce • To identify the purchasing pattern of e- commerce consumers based in Chennai
  • 3. E-commerce order fulfillment Step 1: Order is entered in the system Step2 : Custom shipping documents are placed in the fulfillment Step 3: Order is picked using hand held bar code scanner Step 4: Order is double checked, packed and billed by a second person Step 5: Order is shipped and inventory is automatically adjusted
  • 4. E-commerce order fulfillment Step 6: E-mail is automatically sent to client with all necessary information and tracking numbers Step 7: Order delivered and feed back collected Step 8: Return procedures and after sale support
  • 5. E-commerce order fulfillment 1.Drop ship model: • The seller has his product listed and updated in the retailer’s online platform ↓↓ • The customer places an order for the product ↓↓ • Seller directly ships the goods to the door step of the customer.
  • 6. E-commerce order fulfillment 2. 3rd Party fulfillment model: • Products are stored or brought by the retailer and shipped to the end consumer by the retailer ↓↓ • Inventory of the seller is linked to the retailer to keep track of the inventory and plan on manufacturing.
  • 7. E-commerce order fulfillment 3.Self fulfillment model: • Manufacturer and the retailer are the same ↓↓ • Customers directly place order with the manufacturer of the the product ↓↓ • The manufacturer directly ships it to the end consumer.
  • 8. E-Commerce frauds - buyer side • Credit card fraud – Payment made using stolen card or stolen information about the card. Business is responsible for ensuring the genuineness of the transaction • Refund fraud – Intentional over payment made from a stolen credit card and refund initiated to a different account
  • 9. E-Commerce frauds - buyer side • Merchant fraud – Fraudster sells and receives the payment for no-existent items. It is the business who is responsible for the reimbursement • Card testing – Practice of creating and testing the validity of a credit card number. Fraudster target websites which gives a reason for their card number declined
  • 10. E-Commerce frauds - buyer side • Friendly fraud – Also known as Charge back Fraud, the consumer makes an online purchase and then claims his credit card has been stolen and asks for charge back after receiving the purchased goods or services • Phishing – E-mail asks for user ID, passwords, credit card details and other personal information
  • 11. E-Commerce frauds - merchant side  Create a new account  List popular/fast moving items at extremely low prices • Non-fulfillment • Selling counterfeit • Pricing over MRP • Fake reviews and rating
  • 12. Anti fraud tools • Anti-fraud software Simility Subuno Riskified Signifyd Swift Science • Personal Card Industry Data Security Services (PCI DSS)
  • 13. Anti fraud software • Machine Learning – Provides the needed algorithms, application and framework to bring greater predictive accuracy
  • 14. Anti fraud software • Automated work flow – Automated checking of payment fraud checks like identity theft – Reduces chargeback fees, investigation fees • Device Fingerprinting – Browser version – Operating system – Items installed (plugins/fonts etc.) – Location & time zone settings
  • 15. Anti fraud software • Insight dashboard – This feature provides the retailer with the insight of every move of customer and the device from which the customer is shopping i.e (Mobile phone, laptop, desk top or tablet) • Chargeback guarantee – The clients are guaranteed charge back for all the transaction approved by the application. This makes the investment in these applications risk-free
  • 16. Personal Card Industry Data Security Services (PCI DSS) • Personal Card Industry Security Standards Council (PCI SSC) formed in 2004 • Five major payment brands Master Card, VISA, JCB, American Express and Discover  Aim:  Reducing credit card fraud by strengthening security • Minimum requirements (PCI DSS) set in June 2005 • Applies to every organization who accept payment card • Four levels of organization → Level 1 to Level 4
  • 17. Personal Card Industry Data Security Services (PCI DSS) • PCI DSS requirements: – Secure network system • Install and maintain a firewall configuration • Use of vendor-supplied defaults for system passwords disallowed – Protection to card holder data • Protect stored cardholder data • Encrypt transmission of cardholder data – Strong online security system • Protect all systems against malware and regularly update anti-virus
  • 18. Personal Card Industry Data Security Services (PCI DSS) • PCI DSS requirements: – Access restrictions • Restrict access to cardholder data • Identify and authenticate access • Restrict physical access to cardholder data – Periodical monitoring and testing • Track and monitor all access to network resources and cardholder data • Regularly test security systems and process – Information security policy
  • 19. Data analysis and interpretation: • Demographic profile: From the above chart it is identified that 65% of the respondents were female and 35% were male. This information was used to identified the relationship between gender and their behavior with respect to e- commerce.
  • 20. Analysis 1: Frequency of online purchase of e- commerce consumers • Observed frequency of purchase and expected frequency of a purchases:
  • 21. Analysis 1: Frequency of online purchase of e- commerce consumers • Test for normality using Chi-square: – H0 = There is no significance difference between expected and observed frequencies – H1 = There is significance difference between expected and observed frequencies
  • 22. Analysis 1: Frequency of online purchase of e- commerce consumers • Observed frequency and expected frequency: • Calculation of median:
  • 23. Interpretation 1: • The purchasing pattern of consumers from online stores is not normally distributed • Further the analysis of arithmetic mean and standard deviation shows that the data is widely spread from the mean. • On an average a person shops online once is 7 months
  • 24. Analysis 2:Analysis of relationship between gender and spending pattern: – H0 There is no relationship between gender and purchasing pattern – H1 There is relationship between gender and purchasing pattern
  • 25. Interpretation 2: • p-value of the mean ranks (Table.b) is 0.005 (<0.5) the mean ranks (Table a) are not equal • There is a relationship between gender and purchasing pattern • Female have a higher mean rank indicating that they shop online more frequent than male
  • 26. Analysis 3:Average spending of e-commerce consumers
  • 27. Analysis 3:Average spending of e- commerce consumers • Mann-Whitney Test: – Ho There is no significant relationship between gender and average spending – H1 There is significant relationship between gender and average spending
  • 28. Analysis 3:Average spending of e- commerce consumers • Calculation of median of spending based on gender:
  • 29. Interpretation 3: • Level of significance in both the tests of normality is 0.000 (<0.5), Hence the distribution of spending pattern of consumers is not normal as we see the • the median of average spending on each purchase by the consumers based on their gender was identified to be Rs 1858 for male and Rs 1720 for female • p value in table b is 0.551 (>0.5). Therefore, the mean ranks in table a are equal. Thus, males have a higher average spending pattern compared to female
  • 30. Analysis 4:Products purchased through online retailing • Products purchased through online retailing (Expressed in percentages)
  • 31. Interpretation 4: • Approximately 70% of male and female consumers buy clothing and accessories online • 70% male consumer buy electronics while only 38% of female consumers buy electronic products online. • The market for Consumer durables attract just 6% of the total consumers • Only 3% of consumers in the survey buy grocery and other products from online stores
  • 32. Analysis 5: Merchant side fraud • Duplicate products delivered • Non-Delivery of product • Return not approved • Cash not refunded • Damaged product received • Wrong product delivered • Product sold over MRP • Significant difference between the product displayed and the one delivered • Items missing in the package • Product not delivered on time • Identity theft
  • 33. Analysis 5: Merchant side fraud Merchant side frauds
  • 34. Interpretation 5: • 74% (100-26) of customers have been a victim of e-commerce frauds by the merchant. • 3 in every 10 orders processed ends up with a damaged. • 24% of the consumers complain that the product delivered is significantly different from the product displayed. • While there is also a 21% compliant that the product ordered has not been delivered.
  • 35. Findings: • Significant growth in the use of data consumption in India there has been a significant level of growth in online retailing in the country • Increase in scale of operations the retailers and manufacturers have to work on selecting the best suitable model for their product to keep the cost as low as possible • Due to increase in the volume of online transactions there is a high possibility of frauds through cyber crimes • As a result of the above the market for anti-fraudulent tools in e-commerce business is growing
  • 36. Findings: • As the fraudsters come up with new methods to commit a cyber fraud there is a need for constant development in the security system of the business • Major credit card suppliers have formed a council in 2005 to curtail the activities of cyber fraud by protecting the identity of credit card holders • The online market for Electronics and Clothing is doing well. While other product lines share a significantly low market share • There is significant level of fraud activities on the merchant side as well.
  • 37. Conclusion: • Due to the rapid growth the industry has also become a target industry for fraudsters as there is ample of gaps in the process. Therefore, there is a requirement for development of anti- fraudulent tools for e-commerce industry to grain the trust of the consumers which is very vital for the growth of e-commerce industry