IN2015CH00232A - - Google Patents

Info

Publication number
IN2015CH00232A
IN2015CH00232A IN232CH2015A IN2015CH00232A IN 2015CH00232 A IN2015CH00232 A IN 2015CH00232A IN 232CH2015 A IN232CH2015 A IN 232CH2015A IN 2015CH00232 A IN2015CH00232 A IN 2015CH00232A
Authority
IN
India
Prior art keywords
processor
suspected transaction
suspected
transaction
fraudulent activity
Prior art date
Application number
Inventor
Ramasubramanian Guha
Manjunath Shreya
Mahesh Siddharth
Ranganathan Raghuraman
Original Assignee
Wipro Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Wipro Ltd filed Critical Wipro Ltd
Priority to IN232CH2015 priority Critical patent/IN2015CH00232A/en
Priority to US14/661,298 priority patent/US20160210631A1/en
Publication of IN2015CH00232A publication Critical patent/IN2015CH00232A/en

Links

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06QINFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q20/00Payment architectures, schemes or protocols
    • G06Q20/38Payment protocols; Details thereof
    • G06Q20/40Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
    • G06Q20/401Transaction verification
    • G06Q20/4016Transaction verification involving fraud or risk level assessment in transaction processing

Landscapes

  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Accounting & Taxation (AREA)
  • Computer Security & Cryptography (AREA)
  • Finance (AREA)
  • Strategic Management (AREA)
  • Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

An organizational fraud detection (OFD) system and method for flagging one or more transactions as a potential fraudulent activity, in an organization is disclosed. The OFD system comprises: a processor; and a memory communicatively coupled to the processor, wherein the memory stores processor-executable instructions, which, on execution, cause the processor to: receive a suspected transaction for investigation, classify the suspected transaction into one or more groups of fraudulent activity; select, based on the classification, a set of investigation rules for investigating the suspected transaction; determine, based on data selection rules, the data associated with the suspected transaction; ascertain an accuracy score and an impact score associated with the suspected transaction; and classify the suspected transaction as a potential fraudulent activity on at least one of the accuracy score and the impact score exceeding a pre-defined threshold.
IN232CH2015 2015-01-15 2015-01-15 IN2015CH00232A (en)

Priority Applications (2)

Application Number Priority Date Filing Date Title
IN232CH2015 IN2015CH00232A (en) 2015-01-15 2015-01-15
US14/661,298 US20160210631A1 (en) 2015-01-15 2015-03-18 Systems and methods for flagging potential fraudulent activities in an organization

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
IN232CH2015 IN2015CH00232A (en) 2015-01-15 2015-01-15

Publications (1)

Publication Number Publication Date
IN2015CH00232A true IN2015CH00232A (en) 2015-09-18

Family

ID=54393552

Family Applications (1)

Application Number Title Priority Date Filing Date
IN232CH2015 IN2015CH00232A (en) 2015-01-15 2015-01-15

Country Status (2)

Country Link
US (1) US20160210631A1 (en)
IN (1) IN2015CH00232A (en)

Cited By (1)

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CN113870009A (en) * 2021-09-30 2021-12-31 浙江创邻科技有限公司 Anti-money laundering management and control method, device and system based on graph database and storage medium

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US10496992B2 (en) * 2015-11-24 2019-12-03 Vesta Corporation Exclusion of nodes from link analysis
US20230316284A1 (en) 2016-03-25 2023-10-05 State Farm Mutual Automobile Insurance Company Reducing false positives using customer data and machine learning
US11170375B1 (en) 2016-03-25 2021-11-09 State Farm Mutual Automobile Insurance Company Automated fraud classification using machine learning
US10129274B2 (en) * 2016-09-22 2018-11-13 Adobe Systems Incorporated Identifying significant anomalous segments of a metrics dataset
US20180158063A1 (en) * 2016-12-05 2018-06-07 RetailNext, Inc. Point-of-sale fraud detection using video data and statistical evaluations of human behavior
US10721336B2 (en) * 2017-01-11 2020-07-21 The Western Union Company Transaction analyzer using graph-oriented data structures
US20180308099A1 (en) * 2017-04-19 2018-10-25 Bank Of America Corporation Fraud Detection Tool
EP4369275A3 (en) 2017-06-30 2024-07-24 Equifax, Inc. Detecting synthetic online entities facilitated by primary entities
US10965696B1 (en) * 2017-10-30 2021-03-30 EMC IP Holding Company LLC Evaluation of anomaly detection algorithms using impersonation data derived from user data
US11038886B1 (en) * 2018-02-08 2021-06-15 Wells Fargo Bank, N.A. Compliance management system
US20200151824A1 (en) 2018-11-13 2020-05-14 QuarterSpot Inc. Predicting entity outcomes using taxonomy classifications of transactions
US20200258181A1 (en) * 2019-02-13 2020-08-13 Yuh-Shen Song Intelligent report writer
CN110413707A (en) * 2019-07-22 2019-11-05 百融云创科技股份有限公司 The excavation of clique's relationship is cheated in internet and checks method and its system
US10607080B1 (en) * 2019-10-25 2020-03-31 7-Eleven, Inc. Feedback and training for a machine learning algorithm configured to determine customer purchases during a shopping session at a physical store
US11947643B2 (en) * 2019-12-26 2024-04-02 Rakuten Group, Inc. Fraud detection system, fraud detection method, and program
EP3866087A1 (en) * 2020-02-12 2021-08-18 KBC Groep NV Method, use thereoff, computer program product and system for fraud detection
US11765221B2 (en) 2020-12-14 2023-09-19 The Western Union Company Systems and methods for adaptive security and cooperative multi-system operations with dynamic protocols
US11916753B2 (en) 2021-07-30 2024-02-27 Ciena Corporation Governance and interactions of autonomous pipeline-structured control applications
US20240144275A1 (en) * 2022-10-28 2024-05-02 Hint, Inc. Real-time fraud detection using machine learning
WO2024113317A1 (en) * 2022-12-01 2024-06-06 Paypal, Inc. Computer-based systems and methods for building and implementing attack narrative tree to improve successful fraud detection and prevention
CN117914919B (en) * 2024-03-20 2024-05-24 江苏中威科技软件系统有限公司 Device for detecting communication tool in OFD file

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US8082349B1 (en) * 2005-10-21 2011-12-20 Entrust, Inc. Fraud protection using business process-based customer intent analysis
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113870009A (en) * 2021-09-30 2021-12-31 浙江创邻科技有限公司 Anti-money laundering management and control method, device and system based on graph database and storage medium

Also Published As

Publication number Publication date
US20160210631A1 (en) 2016-07-21

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