Code:-
JC02
Abstract:-
Identity crime has become prominent because there is so much real identity data available on the Web, and confidential data accessible through unsecured mailboxes. It has also become easy for perpetrators to hide their true identities. This can happen in a myriad of insurance, credit, and telecommunications fraud, as well as other more serious crimes. In addition to this, identity crime is prevalent and costly in developed countries that do not have nationally registered identity numbers. Credit card fraud is an element of identity fraud. It can have far reaching effects, since the information on the card can be used to perpetrate other types of identity theft crimes. From using the signature on the back of a card that is stolen, to loaning a credit card to a friend or family member can cause someone to obtain what they need to open other credit card accounts or bank accounts in the victim�s name. Credit applications are Internet or paper-based forms with written requests by potential customers for credit cards, mortgage loans, and personal loans. Credit application fraud is a specific case of identity crime, involving synthetic identity fraud and real identity theft. This paper proposes a new multilayered detection system complemented with two additional layers: communal detection (CD) and spike detection (SD). CD finds real social relationships to reduce the suspicion score, and is tamper resistant to synthetic social relationships. It is the whitelist-oriented approach on a fixed set of attributes. SD finds spikes in duplicates to increase the suspicion score, and is probe-resistant for attributes. It is the attribute-oriented approach on a variable-size set of attributes.
Existing System:-
The Existing System use business rules and scorecards. In Australia, one business rule is the hundred-point physical identity check test which requires the applicant to provide sufficient point-weighted identity documents face-to-face. They must add up to at least 100 points, where a passport is worth 70 points. Another business rule is to contact (or investigate) the applicant over the telephone or Internet. The business rules and scorecards, and known fraud matching have limitations. Another existing is known as fraud matching. Here, known frauds are complete applications which were confirmed to have the intent to defraud and usually periodically recorded into a blacklist. Subsequently, the applications are matched against the blacklist due to long time delays, in days or months, for fraud to reveal itself, and be reported and recorded. This provides a window of opportunity for fraudsters. Second, recording of frauds is highly manual. This means known frauds can be incorrect, expensive, and difficult to obtain, and have the potential of breaching privacy.</p>
Proposed System:-
The Proposed System proposes a new multilayered detection system complemented with two additional layers: communal detection (CD) and spike detection (SD). CD finds real social relationships to reduce the suspicion score, and is tamper resistant to synthetic social relationships. It is the white list-oriented approach on a fixed set of attributes. SD finds spikes in duplicates to increase the suspicion score, and is probe-resistant for attributes. It is the attribute-oriented approach on a variable-size set of attributes. Together, CD and SD can detect more types of attacks, better account for changing legal behavior, and remove the redundant attributes.</p>
Hardware Requirements:-
- Processor -Pentium �III</li> <li>Speed – 1 Ghz</li> <li>RAM – 256 MB(min)</li> <li>Hard Disk – 20 GB</li> <li>Floppy Drive – 44 MB</li> <li>Key Board – Standard Windows Keyboard</li> <li>Mouse – Two or Three Button Mouse</li> <li>Monitor – SVGA</li>
Software Requirements:-
- Operating System : Windows XP/ 7 /10</li> <li style="text-align: justify;">Application Server : Tomcat5.0/6.X</li> <li style="text-align: justify;">Front End : Java, JSP</li> <li style="text-align: justify;">Script : JavaScript.</li> <li style="text-align: justify;">Server side Script : Java Server Pages.</li> <li style="text-align: justify;">Database : MYSQL</li>
Cost:-
Rs 2000
Crime Detection in Credit Card Fraud
Code:
JC02
Abstract:
Identity crime has become prominent because there is so much real identity data available on the Web, and confidential data accessible through unsecured mailboxes. It has also become easy for perpetrators to hide their true identities. This can happen in a myriad of insurance, credit, and telecommunications fraud, as well as other more serious crimes. In addition to this, identity crime is prevalent and costly in developed countries that do not have nationally registered identity numbers. Credit card fraud is an element of identity fraud. It can have far reaching effects, since the information on the card can be used to perpetrate other types of identity theft crimes. From using the signature on the back of a card that is stolen, to loaning a credit card to a friend or family member can cause someone to obtain what they need to open other credit card accounts or bank accounts in the victim�s name. Credit applications are Internet or paper-based forms with written requests by potential customers for credit cards, mortgage loans, and personal loans. Credit application fraud is a specific case of identity crime, involving synthetic identity fraud and real identity theft. This paper proposes a new multilayered detection system complemented with two additional layers: communal detection (CD) and spike detection (SD). CD finds real social relationships to reduce the suspicion score, and is tamper resistant to synthetic social relationships. It is the whitelist-oriented approach on a fixed set of attributes. SD finds spikes in duplicates to increase the suspicion score, and is probe-resistant for attributes. It is the attribute-oriented approach on a variable-size set of attributes.
Existing System:
The Existing System use business rules and scorecards. In Australia, one business rule is the hundred-point physical identity check test which requires the applicant to provide sufficient point-weighted identity documents face-to-face. They must add up to at least 100 points, where a passport is worth 70 points. Another business rule is to contact (or investigate) the applicant over the telephone or Internet. The business rules and scorecards, and known fraud matching have limitations. Another existing is known as fraud matching. Here, known frauds are complete applications which were confirmed to have the intent to defraud and usually periodically recorded into a blacklist. Subsequently, the applications are matched against the blacklist due to long time delays, in days or months, for fraud to reveal itself, and be reported and recorded. This provides a window of opportunity for fraudsters. Second, recording of frauds is highly manual. This means known frauds can be incorrect, expensive, and difficult to obtain, and have the potential of breaching privacy.</p>
Proposed System:
The Proposed System proposes a new multilayered detection system complemented with two additional layers: communal detection (CD) and spike detection (SD). CD finds real social relationships to reduce the suspicion score, and is tamper resistant to synthetic social relationships. It is the white list-oriented approach on a fixed set of attributes. SD finds spikes in duplicates to increase the suspicion score, and is probe-resistant for attributes. It is the attribute-oriented approach on a variable-size set of attributes. Together, CD and SD can detect more types of attacks, better account for changing legal behavior, and remove the redundant attributes.</p>
Hardware Requirements:
- Processor -Pentium �III</li> <li>Speed – 1 Ghz</li> <li>RAM – 256 MB(min)</li> <li>Hard Disk – 20 GB</li> <li>Floppy Drive – 44 MB</li> <li>Key Board – Standard Windows Keyboard</li> <li>Mouse – Two or Three Button Mouse</li> <li>Monitor – SVGA</li>
Software Requirements:
- Operating System : Windows XP/ 7 /10</li> <li style="text-align: justify;">Application Server : Tomcat5.0/6.X</li> <li style="text-align: justify;">Front End : Java, JSP</li> <li style="text-align: justify;">Script : JavaScript.</li> <li style="text-align: justify;">Server side Script : Java Server Pages.</li> <li style="text-align: justify;">Database : MYSQL</li>
Cost:
Rs 2000
Additional Information
Tools Used:
Java
Cost:
₹Rs 2000
