Fraud Analytics Using Machine-learning & Engineering on Big Data (FAME) for Telecom
Telecom industries lose globally 46.3 Billion USD due to fraud. Data mining and machine learning techniques (apart from rules oriented approach) have been used in past, but efficiency has been low as fraud pattern changes very rapidly. This paper presents an industrialized solution approach with sel...
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| creator | Pratihar, Sudarson Roy Paul, Subhadip Dash, Pranab Kumar Das, Amartya Kumar |
| description | Telecom industries lose globally 46.3 Billion USD due to fraud. Data mining
and machine learning techniques (apart from rules oriented approach) have been
used in past, but efficiency has been low as fraud pattern changes very
rapidly. This paper presents an industrialized solution approach with self
adaptive data mining technique and application of big data technologies to
detect fraud and discover novel fraud patterns in accurate, efficient and cost
effective manner. Solution has been successfully demonstrated to detect
International Revenue Share Fraud with |
| doi_str_mv | 10.48550/arxiv.2311.00724 |
| format | Article |
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and machine learning techniques (apart from rules oriented approach) have been
used in past, but efficiency has been low as fraud pattern changes very
rapidly. This paper presents an industrialized solution approach with self
adaptive data mining technique and application of big data technologies to
detect fraud and discover novel fraud patterns in accurate, efficient and cost
effective manner. Solution has been successfully demonstrated to detect
International Revenue Share Fraud with <5% false positive. More than 1 Terra
Bytes of Call Detail Record from a reputed wholesale carrier and overseas
telecom transit carrier has been used to conduct this study.</description><identifier>DOI: 10.48550/arxiv.2311.00724</identifier><language>eng</language><subject>Computer Science - Artificial Intelligence ; Computer Science - Distributed, Parallel, and Cluster Computing ; Computer Science - Learning</subject><creationdate>2023-10</creationdate><rights>http://creativecommons.org/licenses/by-nc-nd/4.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,776,881</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2311.00724$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2311.00724$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Pratihar, Sudarson Roy</creatorcontrib><creatorcontrib>Paul, Subhadip</creatorcontrib><creatorcontrib>Dash, Pranab Kumar</creatorcontrib><creatorcontrib>Das, Amartya Kumar</creatorcontrib><title>Fraud Analytics Using Machine-learning & Engineering on Big Data (FAME) for Telecom</title><description>Telecom industries lose globally 46.3 Billion USD due to fraud. Data mining
and machine learning techniques (apart from rules oriented approach) have been
used in past, but efficiency has been low as fraud pattern changes very
rapidly. This paper presents an industrialized solution approach with self
adaptive data mining technique and application of big data technologies to
detect fraud and discover novel fraud patterns in accurate, efficient and cost
effective manner. Solution has been successfully demonstrated to detect
International Revenue Share Fraud with <5% false positive. More than 1 Terra
Bytes of Call Detail Record from a reputed wholesale carrier and overseas
telecom transit carrier has been used to conduct this study.</description><subject>Computer Science - Artificial Intelligence</subject><subject>Computer Science - Distributed, Parallel, and Cluster Computing</subject><subject>Computer Science - Learning</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotj7FOwzAURb0woMIHMNUTgiHB9rOdMIaSFKRWHQhz9OLYwVLqIKcg-vc0henqnitd6RByw1kqc6XYA8Yf_50K4DxlLBPykrxVEb86WgQcjgdvJvo--dDTLZoPH2wyWIxhBre0DP2J2Di3MdAn39NnPCC9q4pteU_dGGltB2vG_RW5cDhM9vo_F6Suynr1kmx269dVsUlQZzLhmTatMXnLu04jY1qCs7JTwIWSQmgHjHGjda5Bukdw6jSD0miNlQhtBguy_Ls9WzWf0e8xHpvZrjnbwS_QdUgt</recordid><startdate>20231031</startdate><enddate>20231031</enddate><creator>Pratihar, Sudarson Roy</creator><creator>Paul, Subhadip</creator><creator>Dash, Pranab Kumar</creator><creator>Das, Amartya Kumar</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20231031</creationdate><title>Fraud Analytics Using Machine-learning & Engineering on Big Data (FAME) for Telecom</title><author>Pratihar, Sudarson Roy ; Paul, Subhadip ; Dash, Pranab Kumar ; Das, Amartya Kumar</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a674-176cbcc8b1dd6a00643fe4d531254226f3001c668634f93f53fe356aece4a3b73</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Computer Science - Artificial Intelligence</topic><topic>Computer Science - Distributed, Parallel, and Cluster Computing</topic><topic>Computer Science - Learning</topic><toplevel>online_resources</toplevel><creatorcontrib>Pratihar, Sudarson Roy</creatorcontrib><creatorcontrib>Paul, Subhadip</creatorcontrib><creatorcontrib>Dash, Pranab Kumar</creatorcontrib><creatorcontrib>Das, Amartya Kumar</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Pratihar, Sudarson Roy</au><au>Paul, Subhadip</au><au>Dash, Pranab Kumar</au><au>Das, Amartya Kumar</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Fraud Analytics Using Machine-learning & Engineering on Big Data (FAME) for Telecom</atitle><date>2023-10-31</date><risdate>2023</risdate><abstract>Telecom industries lose globally 46.3 Billion USD due to fraud. Data mining
and machine learning techniques (apart from rules oriented approach) have been
used in past, but efficiency has been low as fraud pattern changes very
rapidly. This paper presents an industrialized solution approach with self
adaptive data mining technique and application of big data technologies to
detect fraud and discover novel fraud patterns in accurate, efficient and cost
effective manner. Solution has been successfully demonstrated to detect
International Revenue Share Fraud with <5% false positive. More than 1 Terra
Bytes of Call Detail Record from a reputed wholesale carrier and overseas
telecom transit carrier has been used to conduct this study.</abstract><doi>10.48550/arxiv.2311.00724</doi><oa>free_for_read</oa></addata></record> |
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| identifier | DOI: 10.48550/arxiv.2311.00724 |
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| language | eng |
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| subjects | Computer Science - Artificial Intelligence Computer Science - Distributed, Parallel, and Cluster Computing Computer Science - Learning |
| title | Fraud Analytics Using Machine-learning & Engineering on Big Data (FAME) for Telecom |
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