½ÃÀ庸°í¼­
»óǰÄÚµå
1590938

ÇコÄÉ¾î ºÎÁ¤ ºÐ¼® ½ÃÀå : ¼Ö·ç¼Ç À¯Çü, Á¦°ø ¸ðµ¨, ¿ëµµ, ÃÖÁ¾ »ç¿ëÀÚº° - ¼¼°è ¿¹Ãø(2025-2030³â)

Healthcare Fraud Analytics Market by Solution Type (Descriptive Analytics, Predictive Analytics, Prescriptive Analytics), Delivery Model (On-Demand, On-Premise), Application, End-User - Global Forecast 2025-2030

¹ßÇàÀÏ: | ¸®¼­Ä¡»ç: 360iResearch | ÆäÀÌÁö Á¤º¸: ¿µ¹® 198 Pages | ¹è¼Û¾È³» : 1-2ÀÏ (¿µ¾÷ÀÏ ±âÁØ)

    
    
    




¡á º¸°í¼­¿¡ µû¶ó ÃֽŠÁ¤º¸·Î ¾÷µ¥ÀÌÆ®ÇÏ¿© º¸³»µå¸³´Ï´Ù. ¹è¼ÛÀÏÁ¤Àº ¹®ÀÇÇØ Áֽñ⠹ٶø´Ï´Ù.

ÇコÄÉ¾î ºÎÁ¤ ºÐ¼® ½ÃÀåÀº 2023³â¿¡ 69¾ï 2,000¸¸ ´Þ·¯·Î Æò°¡µÇ¾ú½À´Ï´Ù. 2024³â¿¡´Â 81¾ï 8,000¸¸ ´Þ·¯¿¡ À̸¦ °ÍÀ¸·Î ¿¹ÃøµÇ¸ç, CAGR 19.61%·Î ¼ºÀåÇÏ¿© 2030³â¿¡´Â 242¾ï 7,000¸¸ ´Þ·¯¿¡ À̸¦ °ÍÀ¸·Î ¿¹ÃøµË´Ï´Ù.

ÇコÄÉ¾î ºÎÁ¤ ºÐ¼®ÀÇ·á ½Ã½ºÅÛ¿¡¼­ ºÎÁ¤ÇàÀ§¸¦ ŽÁö, ¿¹¹æ, ¿ÏÈ­Çϱâ À§ÇØ °í±Þ µ¥ÀÌÅÍ ºÐ¼® µµ±¸¿Í ±â¹ýÀ» »ç¿ëÇÏ´Â °ÍÀ» ¸»ÇÕ´Ï´Ù. ¿©±â¿¡´Â ÇãÀ§ û±¸, °ú´Ù û±¸ ¹× ±âŸ ºÎÁ¤ °Å·¡¸¦ ½Äº°Çϱâ À§ÇÑ ¿¹Ãø ¸ðµ¨¸µ, ¸Ó½Å·¯´×, µ¥ÀÌÅÍ ¸¶ÀÌ´× µî ´Ù¾çÇÑ ºÐ¼® ±â¹ýÀÌ Æ÷ÇԵ˴ϴÙ. ÀÇ·á »ç±â ºÐ¼®ÀÇ Çʿ伺Àº ÀÇ·á ½Ã½ºÅÛ¿¡ ¸Å³â ¼ö½Ê¾ï ´Þ·¯ÀÇ ºñ¿ëÀ» ÃÊ·¡ÇÏ´Â ºÎÁ¤ÇàÀ§ÀÇ ºÎ´ãÀÌ Áõ°¡Çϸ鼭 ÀÚ¿øÀ» ¾Ð¹ÚÇϰí ȯÀÚ¿Í ÀÇ·á ¼­ºñ½º Á¦°ø¾÷üÀÇ ºñ¿ëÀ» Áõ°¡½ÃŰ´Â µ¥¼­ ¾Ë ¼ö ÀÖ½À´Ï´Ù. ÇコÄÉ¾î »ç±â ºÐ¼®Àº º¸Çè»ç, Á¤ºÎ ±â°ü, ÀÇ·á ¼­ºñ½º Á¦°ø¾÷ü µî ´Ù¾çÇÑ ºÐ¾ß¿¡¼­ Ȱ¿ëµÇ°í ÀÖÀ¸¸ç, ÀÌµé ±â¾÷Àº »ç±â ÇàÀ§·ÎºÎÅÍ ºñÁî´Ï½º¸¦ º¸È£Çϱâ À§ÇØ ÀÌ·¯ÇÑ µµ±¸¸¦ Ȱ¿ëÇϰí ÀÖ½À´Ï´Ù. ÃÖÁ¾ »ç¿ë ¹üÀ§¿¡ ÀÖ¾î ÀÌ·¯ÇÑ ºÐ¼® ¼Ö·ç¼ÇÀº º¸Çè»ç, ÀÇ·á IT ±â¾÷, ÀÇ·áºñ °¨½Ã¸¦ ´ã´çÇÏ´Â ±ÔÁ¦ ±â°ü¿¡ ¸Å¿ì Áß¿äÇÑ ¿ªÇÒÀ» Çϰí ÀÖ½À´Ï´Ù.

ÁÖ¿ä ½ÃÀå Åë°è
±âÁØ ¿¬µµ(2023³â) 69¾ï 2,000¸¸ ´Þ·¯
¿¹Ãø ¿¬µµ(2024³â) 81¾ï 8,000¸¸ ´Þ·¯
¿¹Ãø ¿¬µµ(2030³â) 242¾ï 7,000¸¸ ´Þ·¯
CAGR(%) 19.61%

ÁÖ¿ä ¼ºÀå ¿äÀÎÀ¸·Î´Â AI¿Í ¸Ó½Å·¯´×ÀÇ ¹ßÀüÀ¸·Î ºÎÁ¤ÇàÀ§ ŽÁö ½Ã½ºÅÛÀÇ Á¤È®µµ°¡ ³ô¾ÆÁø °ÍÀ» ²ÅÀ» ¼ö ÀÖ½À´Ï´Ù. ¶ÇÇÑ, ÀÇ·á ±â·Ï ¹× û±¸ µ¥ÀÌÅÍÀÇ µðÁöÅÐÈ­´Â ºÐ¼® ¿ëµµ¸¦ À§ÇÑ ¹æ´ëÇÑ µ¥ÀÌÅÍ ¼¼Æ®¸¦ Á¦°øÇÕ´Ï´Ù. ¶ÇÇÑ, ±ÔÁ¦ ¾Ð·Â°ú ºÎÁ¤ »ç·Ê Áõ°¡´Â ½ÃÀå ¼ö¿ä¸¦ Áõ°¡½Ã۰í ÀÖ½À´Ï´Ù. ÀÌ·¯ÇÑ ½Ã½ºÅÛÀ» ½Ç½Ã°£ ºÐ¼® ¹× Ŭ¶ó¿ìµå ¼Ö·ç¼Ç°ú ÅëÇÕÇÏ¿© Á¢±Ù¼º°ú ó¸® ¼Óµµ¸¦ Çâ»ó½ÃŰ´Â µ¥¼­ ºñÁî´Ï½º ±âȸ¸¦ ãÀ» ¼ö ÀÖ½À´Ï´Ù. ¶ÇÇÑ, ¹Î°¨ÇÑ µ¥ÀÌÅ͸¦ º¸È£Çϱâ À§ÇÑ »çÀ̹ö º¸¾È ´ëÃ¥¿¡ ´ëÇÑ ÅõÀÚµµ ¼ºÀåÀÇ ±æÀ» Á¦½ÃÇϰí ÀÖ½À´Ï´Ù.

µ¥ÀÌÅÍ ÇÁ¶óÀ̹ö½Ã¿¡ ´ëÇÑ ¿ì·Á¿Í Ä§ÇØ¿¡ ´ëÇÑ °­·ÂÇÑ º¸È£ ´ëÃ¥ÀÌ ÇÊ¿äÇÏ´Ù´Â Á¡, ºÐ¼® ¼Ö·ç¼Ç°ú ±âÁ¸ ÇコÄɾî IT ÀÎÇÁ¶óÀÇ ÅëÇÕÀÌ º¹ÀâÇÏ´Ù´Â Á¡ µîÀ» ²ÅÀ» ¼ö ÀÖ½À´Ï´Ù. ¶ÇÇÑ, °í±Þ ºÐ¼® °á°ú¸¦ °ü¸®Çϰí ÇØ¼®ÇÒ ¼ö ÀÖ´Â ¼÷·ÃµÈ ÀηÂÀÌ ÇÊ¿äÇÕ´Ï´Ù. Çõ½ÅÀº »ç¿ëÀÚ Ä£È­ÀûÀÎ ÀÎÅÍÆäÀ̽º¸¦ °³¹ßÇÏ°í ºÎÁ¤ÇàÀ§ ŽÁö ½Ã½ºÅÛ¿¡¼­ ¿ÀŽÀ» ÁÙÀÌ´Â µ¥ ÃÊÁ¡À» ¸ÂÃß¾î¾ß ÇÕ´Ï´Ù. ÀûÀÀÇü ÇнÀ ±â¼ú¿¡ ´ëÇÑ Áö¼ÓÀûÀÎ ¿¬±¸¸¦ ÅëÇØ ½Ã½ºÅÛÀÇ Á¤È®µµ¿Í È¿À²¼ºÀ» ³ôÀÏ ¼ö ÀÖÀ» °ÍÀ¸·Î º¸ÀÔ´Ï´Ù. ½ÃÀåÀº ±Þ¼ÓÇÑ ±â¼ú ¹ßÀüÀÌ Æ¯Â¡À̸ç, ¸¹Àº ¾÷üµéÀÌ ÃÖ÷´Ü ¼Ö·ç¼ÇÀ» Á¦°øÇϱâ À§ÇØ °æÀïÇϰí ÀÖ¾î Çõ½Å°ú Àü·«Àû Çù·ÂÀÌ Àß ÀÌ·ç¾îÁö´Â °æÀïÀûÀÌ°í ¿ªµ¿ÀûÀΠȯ°æÀ» Á¶¼ºÇϰí ÀÖ½À´Ï´Ù. ±â¾÷Àº ÀûÀÀÇü ÀÎÅÚ¸®Àü½º¿Í »óÈ£¿î¿ë¼ºÀ» Áß½ÃÇÔÀ¸·Î½á ÁøÈ­ÇÏ´Â ½ÃÀå ȯ°æ¿¡¼­ È®°íÇÑ ÀÔÁö¸¦ ±¸ÃàÇÒ ¼ö ÀÖ½À´Ï´Ù.

½ÃÀå ¿ªÇÐ: ºü¸£°Ô ÁøÈ­ÇÏ´Â ÇコÄÉ¾î ºÎÁ¤ ºÐ¼® ½ÃÀå¿¡¼­ Áß¿äÇÑ ½ÃÀå ÀλçÀÌÆ® ÆÄ¾ÇÇϱâ

ÇコÄÉ¾î ºÎÁ¤ ºÐ¼® ½ÃÀåÀº ¼ö¿ä ¹× °ø±ÞÀÇ ¿ªµ¿ÀûÀÎ »óÈ£ÀÛ¿ë¿¡ ÀÇÇØ º¯È­Çϰí ÀÖ½À´Ï´Ù. ÀÌ·¯ÇÑ ½ÃÀå ¿ªÇÐÀÇ ÁøÈ­¸¦ ÀÌÇØÇÔÀ¸·Î½á ±â¾÷Àº Á¤º¸¿¡ ÀÔ°¢ÇÑ ÅõÀÚ °áÁ¤, Àü·«Àû ÀÇ»ç°áÁ¤, »õ·Î¿î ºñÁî´Ï½º ±âȸ¸¦ Æ÷ÂøÇÒ ¼ö ÀÖ½À´Ï´Ù. ÀÌ·¯ÇÑ Æ®·»µå¸¦ Á¾ÇÕÀûÀ¸·Î ÆÄ¾ÇÇÔÀ¸·Î½á ±â¾÷Àº Á¤Ä¡Àû, Áö¸®Àû, ±â¼úÀû, »çȸÀû, °æÁ¦Àû ¿µ¿ª¿¡ °ÉÄ£ ´Ù¾çÇÑ ¸®½ºÅ©¸¦ ÁÙÀÏ ¼ö ÀÖÀ¸¸ç, ¼ÒºñÀÚ Çൿ°ú ±×°ÍÀÌ Á¦Á¶ ºñ¿ë ¹× ±¸¸Å µ¿Çâ¿¡ ¹ÌÄ¡´Â ¿µÇâÀ» º¸´Ù ¸íÈ®ÇÏ°Ô ÀÌÇØÇÒ ¼ö ÀÖ½À´Ï´Ù.

  • ½ÃÀå ¼ºÀå ÃËÁø¿äÀÎ
    • ÇコÄÉ¾î ºÐ¾ß¿¡¼­ÀÇ ºÎÁ¤ÇàÀ§ Áõ°¡
    • Àü ¼¼°è ÀǷẸÇèÁ¦µµ µµÀÔ È®´ë
    • ÇコÄÉ¾î ¿µ¿ª¿¡¼­ ºÎÁ¤ ¹× ³²¿ëÀ» È¿À²ÀûÀ¸·Î ÃßÀûÇØ¾ß ÇÒ Çʿ伺ÀÌ ´ëµÎµÇ°í ÀÖ½À´Ï´Ù.
  • ½ÃÀå ¼ºÀå ¾ïÁ¦¿äÀÎ
    • ÀÇ·á ºÎÁ¤ ºÐ¼® ¼­ºñ½º¿Í °ü·ÃµÈ ³ôÀº ºñ¿ë
  • ½ÃÀå ±âȸ
    • ÇコÄɾî BPO ¹× ºÎÁ¤ ID °ü¸® ¼ÒÇÁÆ®¿þ¾î ¼Ò°³
    • ÇコÄÉ¾î ºÎÁ¤ ºÐ¼® ¼ÒÇÁÆ®¿þ¾î¿¡ °í±Þ ºÐ¼®, ÀΰøÁö´É(AI), ¸Ó½Å·¯´×(ML)À» ÅëÇÕÇÑ ÇコÄÉ¾î ºÎÁ¤ ºÐ¼® ¼ÒÇÁÆ®¿þ¾î
  • ½ÃÀå °úÁ¦
    • ÇコÄÉ¾î ºÎÁ¤ ºÐ¼® ¼ÒÇÁÆ®¿þ¾î ¹× ¼­ºñ½º »ç¿ë¿¡ ´ëÇÑ Á¦ÇÑ »çÇ×

Portre's Five Forces: ÇコÄÉ¾î ºÎÁ¤ ºÐ¼® ½ÃÀåÀ» Ž»öÇÏ´Â Àü·« µµ±¸

Portre's Five Forces ÇÁ·¹ÀÓ¿öÅ©´Â ÇコÄÉ¾î ºÎÁ¤ ºÐ¼® ½ÃÀå °æÀï ±¸µµ¸¦ ÀÌÇØÇÏ´Â µ¥ Áß¿äÇÑ µµ±¸ÀÔ´Ï´Ù. Portre's Five Forces ÇÁ·¹ÀÓ¿öÅ©´Â ±â¾÷ÀÇ °æÀï·ÂÀ» Æò°¡Çϰí Àü·«Àû ±âȸ¸¦ Ž»öÇÒ ¼ö ÀÖ´Â ¸íÈ®ÇÑ ¹æ¹ýÀ» Á¦°øÇÕ´Ï´Ù. ÀÌ ÇÁ·¹ÀÓ¿öÅ©´Â ±â¾÷ÀÌ ½ÃÀå ³» ¼¼·Âµµ¸¦ Æò°¡ÇÏ°í ½Å±Ô »ç¾÷ÀÇ ¼öÀͼºÀ» ÆÇ´ÜÇÏ´Â µ¥ µµ¿òÀÌ µË´Ï´Ù. ÀÌ·¯ÇÑ ÅëÂû·ÂÀ» ÅëÇØ ±â¾÷Àº °­Á¡À» Ȱ¿ëÇϰí, ¾àÁ¡À» ÇØ°áÇϰí, ÀáÀçÀûÀÎ µµÀüÀ» ÇÇÇϰí, º¸´Ù °­·ÂÇÑ ½ÃÀå Æ÷Áö¼Å´×À» È®º¸ÇÒ ¼ö ÀÖ½À´Ï´Ù.

PESTLE ºÐ¼® : ÇコÄÉ¾î ºÎÁ¤ ºÐ¼® ½ÃÀåÀÇ ¿ÜºÎ ¿µÇâ ÆÄ¾Ç

PESTLE ºÐ¼® : ÇコÄÉ¾î ºÎÁ¤ ºÐ¼® ½ÃÀåÀÇ ¿ÜºÎ ¿µÇâ ÆÄ¾Ç

¿ÜºÎ °Å½Ã ȯ°æ ¿äÀÎÀº ÇコÄÉ¾î ºÎÁ¤ ºÐ¼® ½ÃÀåÀÇ ¼º°ú ¿ªÇÐÀ» Çü¼ºÇÏ´Â µ¥ ÀÖ¾î ¸Å¿ì Áß¿äÇÑ ¿ªÇÒÀ» ÇÕ´Ï´Ù. Á¤Ä¡Àû, °æÁ¦Àû, »çȸÀû, ±â¼úÀû, ¹ýÀû, ȯ°æÀû ¿äÀο¡ ´ëÇÑ ºÐ¼®Àº ÀÌ·¯ÇÑ ¿µÇâÀ» Ž»öÇÏ´Â µ¥ ÇÊ¿äÇÑ Á¤º¸¸¦ Á¦°øÇϸç, PESTLE ¿äÀÎÀ» Á¶»çÇÔÀ¸·Î½á ±â¾÷Àº ÀáÀçÀû À§Çè°ú ±âȸ¸¦ ´õ Àß ÀÌÇØÇÒ ¼ö ÀÖ½À´Ï´Ù. ÀÌ·¯ÇÑ ºÐ¼®À» ÅëÇØ ±â¾÷Àº ±ÔÁ¦, ¼ÒºñÀÚ ¼±È£µµ, °æÁ¦ µ¿ÇâÀÇ º¯È­¸¦ ¿¹ÃøÇÏ°í ¼±Á¦ÀûÀÌ°í ´Éµ¿ÀûÀÎ ÀÇ»ç°áÁ¤À» ³»¸± Áغñ¸¦ ÇÒ ¼ö ÀÖ½À´Ï´Ù.

½ÃÀå Á¡À¯À² ºÐ¼® ÇコÄÉ¾î ºÎÁ¤ ºÐ¼® ½ÃÀå¿¡¼­°æÀï ±¸µµ ÆÄ¾Ç

ÇコÄÉ¾î ºÎÁ¤ ºÐ¼® ½ÃÀåÀÇ »ó¼¼ÇÑ ½ÃÀå Á¡À¯À² ºÐ¼®À» ÅëÇØ º¥´õÀÇ ¼º°ú¸¦ Á¾ÇÕÀûÀ¸·Î Æò°¡ÇÒ ¼ö ÀÖ½À´Ï´Ù. ±â¾÷Àº ¼öÀÍ, °í°´ ±â¹Ý, ¼ºÀå·ü µî ÁÖ¿ä ÁöÇ¥¸¦ ºñ±³ÇÏ¿© °æÀïÀû À§Ä¡¸¦ ÆÄ¾ÇÇÒ ¼ö ÀÖ½À´Ï´Ù. ÀÌ ºÐ¼®Àº ½ÃÀåÀÇ ÁýÁßÈ­, ´ÜÆíÈ­ ¹× ÅëÇÕ Ãß¼¼¸¦ ÆÄ¾ÇÇÒ ¼ö ÀÖÀ¸¸ç, °ø±Þ¾÷ü´Â Ä¡¿­ÇÑ °æÀï ¼Ó¿¡¼­ ÀÚ½ÅÀÇ ÀÔÁö¸¦ °­È­ÇÒ ¼ö ÀÖ´Â Àü·«Àû ÀÇ»ç°áÁ¤À» ³»¸®´Â µ¥ ÇÊ¿äÇÑ ÅëÂû·ÂÀ» ¾òÀ» ¼ö ÀÖ½À´Ï´Ù.

FPNV Æ÷Áö¼Å´× ¸ÅÆ®¸¯½º ÇコÄÉ¾î ºÎÁ¤ ºÐ¼® ½ÃÀå¿¡¼­ÀÇ º¥´õ ¼º°ú Æò°¡

FPNV Æ÷Áö¼Å´× ¸ÅÆ®¸¯½º´Â ÇコÄÉ¾î ºÎÁ¤ ºÐ¼® ½ÃÀå¿¡¼­ º¥´õ¸¦ Æò°¡ÇÒ ¼ö ÀÖ´Â Áß¿äÇÑ µµ±¸ÀÔ´Ï´Ù. ÀÌ ¸ÅÆ®¸¯½º¸¦ ÅëÇØ ºñÁî´Ï½º Á¶Á÷Àº º¥´õÀÇ ºñÁî´Ï½º Àü·«°ú Á¦Ç° ¸¸Á·µµ¸¦ ±â¹ÝÀ¸·Î Æò°¡ÇÏ¿© ¸ñÇ¥¿¡ ºÎÇÕÇÏ´Â Á¤º¸¿¡ ÀÔ°¢ÇÑ ÀÇ»ç°áÁ¤À» ³»¸± ¼ö ÀÖÀ¸¸ç, 4°³ÀÇ »çºÐ¸éÀ¸·Î º¥´õ¸¦ ¸íÈ®Çϰí Á¤È®ÇÏ°Ô ¼¼ºÐÈ­ÇÏ¿© Àü·« ¸ñÇ¥¿¡ °¡Àå ÀûÇÕÇÑ ÆÄÆ®³Ê¿Í ¼Ö·ç¼ÇÀ» ½Äº°ÇÒ ¼ö ÀÖ½À´Ï´Ù. Àü·« ¸ñÇ¥¿¡ °¡Àå ÀûÇÕÇÑ ÆÄÆ®³Ê¿Í ¼Ö·ç¼ÇÀ» ½Äº°ÇÒ ¼ö ÀÖ½À´Ï´Ù.

ÇコÄÉ¾î ºÎÁ¤ ºÐ¼® ½ÃÀå¿¡¼­ ¼º°øÇϱâ À§ÇÑ Àü·« ºÐ¼® ¹× ±ÇÀå »çÇ×

ÇコÄÉ¾î ºÎÁ¤ ºÐ¼® ½ÃÀåÀÇ Àü·«Àû ºÐ¼®Àº ¼¼°è ½ÃÀå¿¡¼­ÀÇ ÀÔÁö¸¦ °­È­ÇϰíÀÚ ÇÏ´Â ±â¾÷¿¡°Ô ÇʼöÀûÀÔ´Ï´Ù. ÁÖ¿ä ÀÚ¿ø, ¿ª·® ¹× ¼º°ú ÁöÇ¥¸¦ °ËÅäÇÔÀ¸·Î½á ±â¾÷Àº ¼ºÀå ±âȸ¸¦ ½Äº°ÇÏ°í °³¼±ÇÒ ¼ö ÀÖ½À´Ï´Ù. ÀÌ·¯ÇÑ Á¢±Ù ¹æ½ÄÀ» ÅëÇØ °æÀï ȯ°æÀÇ µµÀüÀ» ±Øº¹ÇÏ°í »õ·Î¿î ºñÁî´Ï½º ±âȸ¸¦ Ȱ¿ëÇÏ¿© Àå±âÀûÀÎ ¼º°øÀ» °ÅµÑ ¼ö ÀÖµµ·Ï ÁغñÇÒ ¼ö ÀÖ½À´Ï´Ù.

ÀÌ º¸°í¼­´Â ÁÖ¿ä °ü½É ºÐ¾ß¸¦ Æ÷°ýÇÏ´Â ½ÃÀå¿¡ ´ëÇÑ Á¾ÇÕÀûÀÎ ºÐ¼®À» Á¦°øÇÕ´Ï´Ù.

1. ½ÃÀå ħÅõµµ : ÇöÀç ½ÃÀå ȯ°æÀÇ »ó¼¼ÇÑ °ËÅä, ÁÖ¿ä ±â¾÷ÀÇ ±¤¹üÀ§ÇÑ µ¥ÀÌÅÍ, ½ÃÀå µµ´Þ ¹üÀ§ ¹× Àü¹ÝÀûÀÎ ¿µÇâ·Â Æò°¡.

2. ½ÃÀå °³Ã´µµ: ½ÅÈï ½ÃÀå¿¡¼­ÀÇ ¼ºÀå ±âȸ¸¦ ÆÄ¾ÇÇϰí, ±âÁ¸ ºÐ¾ßÀÇ È®Àå °¡´É¼ºÀ» Æò°¡Çϸç, ¹Ì·¡ ¼ºÀåÀ» À§ÇÑ Àü·«Àû ·Îµå¸ÊÀ» Á¦°øÇÕ´Ï´Ù.

3. ½ÃÀå ´Ù°¢È­ : ÃÖ±Ù Á¦Ç° Ãâ½Ã, ¹Ì°³Ã´ Áö¿ª, ¾÷°èÀÇ ÁÖ¿ä ¹ßÀü, ½ÃÀåÀ» Çü¼ºÇÏ´Â Àü·«Àû ÅõÀÚ¸¦ ºÐ¼®ÇÕ´Ï´Ù.

4. °æÀï Æò°¡ ¹× Á¤º¸ : °æÀï ±¸µµ¸¦ öÀúÈ÷ ºÐ¼®ÇÏ¿© ½ÃÀå Á¡À¯À², »ç¾÷ Àü·«, Á¦Ç° Æ÷Æ®Æú¸®¿À, ÀÎÁõ, ±ÔÁ¦ ´ç±¹ÀÇ ½ÂÀÎ, ƯÇã µ¿Çâ, ÁÖ¿ä ±â¾÷ÀÇ ±â¼ú ¹ßÀü µîÀ» °ËÅäÇÕ´Ï´Ù.

5. Á¦Ç° °³¹ß ¹× Çõ½Å : ¹Ì·¡ ½ÃÀå ¼ºÀåÀ» °¡¼ÓÇÒ °ÍÀ¸·Î ¿¹»óµÇ´Â ÷´Ü ±â¼ú, ¿¬±¸ °³¹ß Ȱµ¿ ¹× Á¦Ç° Çõ½ÅÀ» °­Á¶ÇÕ´Ï´Ù.

ÀÌÇØ°ü°èÀÚµéÀÌ ÃæºÐÇÑ Á¤º¸¸¦ ¹ÙÅÁÀ¸·Î ÀÇ»ç°áÁ¤À» ³»¸± ¼ö ÀÖµµ·Ï ´ÙÀ½°ú °°Àº Áß¿äÇÑ Áú¹®¿¡ ´ëÇÑ ´äº¯µµ Á¦°øÇÕ´Ï´Ù.

1. ÇöÀç ½ÃÀå ±Ô¸ð¿Í ÇâÈÄ ¼ºÀå Àü¸ÁÀº?

2. ÃÖ°íÀÇ ÅõÀÚ ±âȸ¸¦ Á¦°øÇÏ´Â Á¦Ç°, ºÎ¹®, Áö¿ªÀº?

3. ½ÃÀåÀ» Çü¼ºÇÏ´Â ÁÖ¿ä ±â¼ú µ¿Çâ°ú ±ÔÁ¦ÀÇ ¿µÇâÀº?

4. ÁÖ¿ä º¥´õÀÇ ½ÃÀå Á¡À¯À²°ú °æÀï Æ÷Áö¼ÇÀº?

5.º¥´õ ½ÃÀå ÁøÀÔ ¹× ö¼ö Àü·«ÀÇ ¿øµ¿·ÂÀÌ µÇ´Â ¼öÀÍ¿ø°ú Àü·«Àû ±âȸ´Â ¹«¾ùÀΰ¡?

¸ñÂ÷

Á¦1Àå ¼­¹®

Á¦2Àå Á¶»ç ¹æ¹ý

Á¦3Àå ÁÖ¿ä ¿ä¾à

Á¦4Àå ½ÃÀå °³¿ä

Á¦5Àå ½ÃÀå ÀλçÀÌÆ®

  • ½ÃÀå ¿ªÇÐ
    • ¼ºÀå ÃËÁø¿äÀÎ
    • ¼ºÀå ¾ïÁ¦¿äÀÎ
    • ±âȸ
    • °úÁ¦
  • ½ÃÀå ¼¼ºÐÈ­ ºÐ¼®
  • PorterÀÇ Five Forces ºÐ¼®
  • PESTEL ºÐ¼®
    • Á¤Ä¡
    • °æÁ¦
    • »çȸ
    • ±â¼ú
    • ¹ý·ü
    • ȯ°æ

Á¦6Àå ÇコÄÉ¾î ºÎÁ¤ ºÐ¼® ½ÃÀå : ¼Ö·ç¼Ç À¯Çüº°

  • ±â¼úÀû ºÐ¼®
  • ¿¹Ãø ºÐ¼®
  • ó¹æÀû ºÐ¼®

Á¦7Àå ÇコÄÉ¾î ºÎÁ¤ ºÐ¼® ½ÃÀå : À¯Åë ¸ðµ¨º°

  • ¿Âµð¸Çµå
  • ¿ÂÇÁ·¹¹Ì½º

Á¦8Àå ÇコÄÉ¾î ºÎÁ¤ ºÐ¼® ½ÃÀå : ¿ëµµº°

  • º¸Çè±Ý û±¸ ½É»ç
    • ÁöºÒ ÈÄ ¸®ºä
    • ¼±ºÒ Àç°ËÅä
  • ÁöºÒ Á¤ÇÕ¼º
  • ¾à±¹ û±¸¼­ ºÎÁ¤ »ç¿ë

Á¦9Àå ÇコÄÉ¾î ºÎÁ¤ ºÐ¼® ½ÃÀå : ÃÖÁ¾»ç¿ëÀÚº°

  • °í¿ëÁÖ
  • ¹Î°£º¸Çè ÁöºÒÀÚ
  • °ø°ø±â°ü ¹× Á¤ºÎ±â°ü
  • ½áµåÆÄƼ ¼­ºñ½º Á¦°ø¾÷ü

Á¦10Àå ¾Æ¸Þ¸®Ä«ÀÇ ÇコÄÉ¾î ºÎÁ¤ ºÐ¼® ½ÃÀå

  • ¾Æ¸£ÇîÆ¼³ª
  • ºê¶óÁú
  • ij³ª´Ù
  • ¸ß½ÃÄÚ
  • ¹Ì±¹

Á¦11Àå ¾Æ½Ã¾ÆÅÂÆò¾çÀÇ ÇコÄÉ¾î ºÎÁ¤ ºÐ¼® ½ÃÀå

  • È£ÁÖ
  • Áß±¹
  • Àεµ
  • Àεµ³×½Ã¾Æ
  • ÀϺ»
  • ¸»·¹À̽þÆ
  • Çʸ®ÇÉ
  • ½Ì°¡Æ÷¸£
  • Çѱ¹
  • ´ë¸¸
  • ű¹
  • º£Æ®³²

Á¦12Àå À¯·´, Áßµ¿ ¹× ¾ÆÇÁ¸®Ä«ÀÇ ÇコÄÉ¾î ºÎÁ¤ ºÐ¼® ½ÃÀå

  • µ§¸¶Å©
  • ÀÌÁýÆ®
  • Çɶõµå
  • ÇÁ¶û½º
  • µ¶ÀÏ
  • À̽º¶ó¿¤
  • ÀÌÅ»¸®¾Æ
  • ³×´ú¶õµå
  • ³ªÀÌÁö¸®¾Æ
  • ³ë¸£¿þÀÌ
  • Æú¶õµå
  • īŸ¸£
  • ·¯½Ã¾Æ
  • »ç¿ìµð¾Æ¶óºñ¾Æ
  • ³²¾ÆÇÁ¸®Ä«°øÈ­±¹
  • ½ºÆäÀÎ
  • ½º¿þµ§
  • ½ºÀ§½º
  • ÅÍŰ
  • ¾Æ¶ø¿¡¹Ì¸®Æ®(UAE)
  • ¿µ±¹

Á¦13Àå °æÀï ±¸µµ

  • ½ÃÀå Á¡À¯À² ºÐ¼®, 2023³â
  • FPNV Æ÷Áö¼Å´× ¸ÅÆ®¸¯½º, 2023³â
  • °æÀï ½Ã³ª¸®¿À ºÐ¼®
  • Àü·« ºÐ¼®°ú Á¦¾È

±â¾÷ ¸®½ºÆ®

  • Atos SE
  • CGI Inc.
  • Change Healthcare Inc.
  • Claroty Ltd.
  • Codoxo, Inc.
  • Conduent, Inc.
  • Coviti, Inc.
  • DXC Technology Company
  • ExlService Holdings, Inc.
  • Fair Isaac Corporation
  • Fortified Health Security
  • FraudLens Inc.
  • FRISS
  • H2O.ai, Inc.
  • HCL Technologies Ltd.
  • Healthcare fraud Shield
  • Hewlett Packard Enterprise Development LP
  • Imperva, Inc.
  • Intel Corporation
  • International Business Machines Corporation
  • LexisNexis Risk Solutions Group
  • Mckesson Corporation
  • Multuplan Corporaton
  • Northrop Grumman Corporation
  • OneSpan Inc.
  • OSP Labs
  • Pondera Solutions
  • Qlarant Inc.
  • RELX Group Plc
  • SAS Institute Inc.
  • Sharecare, Inc.
  • United Health Group Incorporated
  • Wipro Limited
LSH

The Healthcare Fraud Analytics Market was valued at USD 6.92 billion in 2023, expected to reach USD 8.18 billion in 2024, and is projected to grow at a CAGR of 19.61%, to USD 24.27 billion by 2030.

Healthcare Fraud Analytics refers to the use of sophisticated data analysis tools and methodologies to detect, prevent, and mitigate fraudulent activities within the healthcare system. The scope encompasses various analytics techniques, including predictive modeling, machine learning, and data mining, aimed at identifying false claims, overbilling, and other fraudulent transactions. The necessity of healthcare fraud analytics is underscored by the increasing burden of fraud, which costs healthcare systems billions annually, thereby straining resources and increasing costs for patients and providers alike. Its application spans insurers, governmental agencies, and healthcare providers, who leverage these tools to secure their operations against fraudulent activities. In terms of end-use scope, these analytics solutions are crucial for insurance companies, healthcare IT firms, and regulatory bodies tasked with monitoring healthcare expenditures.

KEY MARKET STATISTICS
Base Year [2023] USD 6.92 billion
Estimated Year [2024] USD 8.18 billion
Forecast Year [2030] USD 24.27 billion
CAGR (%) 19.61%

Key growth factors include advancements in AI and machine learning which enhance the precision of fraud detection systems. The increasing digitization of healthcare records and claims data also provides an expansive dataset for analytics applications. Additionally, regulatory pressures and the rising incidence of fraud cases are propelling market demand. Opportunities lie in integrating these systems with real-time analytics and cloud solutions to improve accessibility and processing speed. Investing in cybersecurity measures to protect sensitive data also offers pathways for growth.

Challenges include data privacy concerns, which necessitate robust safeguarding measures against breaches, and the complexity of integrating analytics solutions with existing healthcare IT infrastructure. There's also a need for skilled personnel to manage and interpret sophisticated analytics outputs. Innovations should focus on developing user-friendly interfaces and reducing false positives in fraud detection systems. Continued research into adaptive learning technologies could also enhance system accuracy and efficiency. The market is characterized by rapid technological advancements, with numerous players vying to offer cutting-edge solutions, thus fostering a competitive and dynamic environment ripe for innovation and strategic collaboration. Emphasizing adaptive intelligence and interoperability can strongly position firms in this evolving market landscape.

Market Dynamics: Unveiling Key Market Insights in the Rapidly Evolving Healthcare Fraud Analytics Market

The Healthcare Fraud Analytics Market is undergoing transformative changes driven by a dynamic interplay of supply and demand factors. Understanding these evolving market dynamics prepares business organizations to make informed investment decisions, refine strategic decisions, and seize new opportunities. By gaining a comprehensive view of these trends, business organizations can mitigate various risks across political, geographic, technical, social, and economic domains while also gaining a clearer understanding of consumer behavior and its impact on manufacturing costs and purchasing trends.

  • Market Drivers
    • Increasing number of fraudulent activities in the healthcare sector
    • Growing adoption of health insurance plans across the globe
    • Rising need to track fraud & abuse efficiently in healthcare domain
  • Market Restraints
    • High cost associated with healthcare fraud analytics services
  • Market Opportunities
    • Introduction of healthcare BPO and fraud identity management software
    • Integration of advanced analytics, artificial intelligence (AI) and, machine learning (ML) in healthcare fraud analytics software
  • Market Challenges
    • Limitations pertaining to the use of healthcare fraud analytics software and services

Porter's Five Forces: A Strategic Tool for Navigating the Healthcare Fraud Analytics Market

Porter's five forces framework is a critical tool for understanding the competitive landscape of the Healthcare Fraud Analytics Market. It offers business organizations with a clear methodology for evaluating their competitive positioning and exploring strategic opportunities. This framework helps businesses assess the power dynamics within the market and determine the profitability of new ventures. With these insights, business organizations can leverage their strengths, address weaknesses, and avoid potential challenges, ensuring a more resilient market positioning.

PESTLE Analysis: Navigating External Influences in the Healthcare Fraud Analytics Market

External macro-environmental factors play a pivotal role in shaping the performance dynamics of the Healthcare Fraud Analytics Market. Political, Economic, Social, Technological, Legal, and Environmental factors analysis provides the necessary information to navigate these influences. By examining PESTLE factors, businesses can better understand potential risks and opportunities. This analysis enables business organizations to anticipate changes in regulations, consumer preferences, and economic trends, ensuring they are prepared to make proactive, forward-thinking decisions.

Market Share Analysis: Understanding the Competitive Landscape in the Healthcare Fraud Analytics Market

A detailed market share analysis in the Healthcare Fraud Analytics Market provides a comprehensive assessment of vendors' performance. Companies can identify their competitive positioning by comparing key metrics, including revenue, customer base, and growth rates. This analysis highlights market concentration, fragmentation, and trends in consolidation, offering vendors the insights required to make strategic decisions that enhance their position in an increasingly competitive landscape.

FPNV Positioning Matrix: Evaluating Vendors' Performance in the Healthcare Fraud Analytics Market

The Forefront, Pathfinder, Niche, Vital (FPNV) Positioning Matrix is a critical tool for evaluating vendors within the Healthcare Fraud Analytics Market. This matrix enables business organizations to make well-informed decisions that align with their goals by assessing vendors based on their business strategy and product satisfaction. The four quadrants provide a clear and precise segmentation of vendors, helping users identify the right partners and solutions that best fit their strategic objectives.

Strategy Analysis & Recommendation: Charting a Path to Success in the Healthcare Fraud Analytics Market

A strategic analysis of the Healthcare Fraud Analytics Market is essential for businesses looking to strengthen their global market presence. By reviewing key resources, capabilities, and performance indicators, business organizations can identify growth opportunities and work toward improvement. This approach helps businesses navigate challenges in the competitive landscape and ensures they are well-positioned to capitalize on newer opportunities and drive long-term success.

Key Company Profiles

The report delves into recent significant developments in the Healthcare Fraud Analytics Market, highlighting leading vendors and their innovative profiles. These include Atos SE, CGI Inc., Change Healthcare Inc., Claroty Ltd., Codoxo, Inc., Conduent, Inc., Coviti, Inc., DXC Technology Company, ExlService Holdings, Inc., Fair Isaac Corporation, Fortified Health Security, FraudLens Inc., FRISS, H2O.ai, Inc., HCL Technologies Ltd., Healthcare fraud Shield, Hewlett Packard Enterprise Development LP, Imperva, Inc., Intel Corporation, International Business Machines Corporation, LexisNexis Risk Solutions Group, Mckesson Corporation, Multuplan Corporaton, Northrop Grumman Corporation, OneSpan Inc., OSP Labs, Pondera Solutions, Qlarant Inc., RELX Group Plc, SAS Institute Inc., Sharecare, Inc., United Health Group Incorporated, and Wipro Limited.

Market Segmentation & Coverage

This research report categorizes the Healthcare Fraud Analytics Market to forecast the revenues and analyze trends in each of the following sub-markets:

  • Based on Solution Type, market is studied across Descriptive Analytics, Predictive Analytics, and Prescriptive Analytics.
  • Based on Delivery Model, market is studied across On-Demand and On-Premise.
  • Based on Application, market is studied across Insurance Claims Review, Payment Integrity, and Pharmacy Billing Misuse. The Insurance Claims Review is further studied across Post payment Review and Prepayment Review.
  • Based on End-User, market is studied across Employers, Private Insurance Payers, Public & Government Agencies, and Third-party service providers.
  • Based on Region, market is studied across Americas, Asia-Pacific, and Europe, Middle East & Africa. The Americas is further studied across Argentina, Brazil, Canada, Mexico, and United States. The United States is further studied across California, Florida, Illinois, New York, Ohio, Pennsylvania, and Texas. The Asia-Pacific is further studied across Australia, China, India, Indonesia, Japan, Malaysia, Philippines, Singapore, South Korea, Taiwan, Thailand, and Vietnam. The Europe, Middle East & Africa is further studied across Denmark, Egypt, Finland, France, Germany, Israel, Italy, Netherlands, Nigeria, Norway, Poland, Qatar, Russia, Saudi Arabia, South Africa, Spain, Sweden, Switzerland, Turkey, United Arab Emirates, and United Kingdom.

The report offers a comprehensive analysis of the market, covering key focus areas:

1. Market Penetration: A detailed review of the current market environment, including extensive data from top industry players, evaluating their market reach and overall influence.

2. Market Development: Identifies growth opportunities in emerging markets and assesses expansion potential in established sectors, providing a strategic roadmap for future growth.

3. Market Diversification: Analyzes recent product launches, untapped geographic regions, major industry advancements, and strategic investments reshaping the market.

4. Competitive Assessment & Intelligence: Provides a thorough analysis of the competitive landscape, examining market share, business strategies, product portfolios, certifications, regulatory approvals, patent trends, and technological advancements of key players.

5. Product Development & Innovation: Highlights cutting-edge technologies, R&D activities, and product innovations expected to drive future market growth.

The report also answers critical questions to aid stakeholders in making informed decisions:

1. What is the current market size, and what is the forecasted growth?

2. Which products, segments, and regions offer the best investment opportunities?

3. What are the key technology trends and regulatory influences shaping the market?

4. How do leading vendors rank in terms of market share and competitive positioning?

5. What revenue sources and strategic opportunities drive vendors' market entry or exit strategies?

Table of Contents

1. Preface

  • 1.1. Objectives of the Study
  • 1.2. Market Segmentation & Coverage
  • 1.3. Years Considered for the Study
  • 1.4. Currency & Pricing
  • 1.5. Language
  • 1.6. Stakeholders

2. Research Methodology

  • 2.1. Define: Research Objective
  • 2.2. Determine: Research Design
  • 2.3. Prepare: Research Instrument
  • 2.4. Collect: Data Source
  • 2.5. Analyze: Data Interpretation
  • 2.6. Formulate: Data Verification
  • 2.7. Publish: Research Report
  • 2.8. Repeat: Report Update

3. Executive Summary

4. Market Overview

5. Market Insights

  • 5.1. Market Dynamics
    • 5.1.1. Drivers
      • 5.1.1.1. Increasing number of fraudulent activities in the healthcare sector
      • 5.1.1.2. Growing adoption of health insurance plans across the globe
      • 5.1.1.3. Rising need to track fraud & abuse efficiently in healthcare domain
    • 5.1.2. Restraints
      • 5.1.2.1. High cost associated with healthcare fraud analytics services
    • 5.1.3. Opportunities
      • 5.1.3.1. Introduction of healthcare BPO and fraud identity management software
      • 5.1.3.2. Integration of advanced analytics, artificial intelligence (AI) and, machine learning (ML) in healthcare fraud analytics software
    • 5.1.4. Challenges
      • 5.1.4.1. Limitations pertaining to the use of healthcare fraud analytics software and services
  • 5.2. Market Segmentation Analysis
  • 5.3. Porter's Five Forces Analysis
    • 5.3.1. Threat of New Entrants
    • 5.3.2. Threat of Substitutes
    • 5.3.3. Bargaining Power of Customers
    • 5.3.4. Bargaining Power of Suppliers
    • 5.3.5. Industry Rivalry
  • 5.4. PESTLE Analysis
    • 5.4.1. Political
    • 5.4.2. Economic
    • 5.4.3. Social
    • 5.4.4. Technological
    • 5.4.5. Legal
    • 5.4.6. Environmental

6. Healthcare Fraud Analytics Market, by Solution Type

  • 6.1. Introduction
  • 6.2. Descriptive Analytics
  • 6.3. Predictive Analytics
  • 6.4. Prescriptive Analytics

7. Healthcare Fraud Analytics Market, by Delivery Model

  • 7.1. Introduction
  • 7.2. On-Demand
  • 7.3. On-Premise

8. Healthcare Fraud Analytics Market, by Application

  • 8.1. Introduction
  • 8.2. Insurance Claims Review
    • 8.2.1. Post payment Review
    • 8.2.2. Prepayment Review
  • 8.3. Payment Integrity
  • 8.4. Pharmacy Billing Misuse

9. Healthcare Fraud Analytics Market, by End-User

  • 9.1. Introduction
  • 9.2. Employers
  • 9.3. Private Insurance Payers
  • 9.4. Public & Government Agencies
  • 9.5. Third-party service providers

10. Americas Healthcare Fraud Analytics Market

  • 10.1. Introduction
  • 10.2. Argentina
  • 10.3. Brazil
  • 10.4. Canada
  • 10.5. Mexico
  • 10.6. United States

11. Asia-Pacific Healthcare Fraud Analytics Market

  • 11.1. Introduction
  • 11.2. Australia
  • 11.3. China
  • 11.4. India
  • 11.5. Indonesia
  • 11.6. Japan
  • 11.7. Malaysia
  • 11.8. Philippines
  • 11.9. Singapore
  • 11.10. South Korea
  • 11.11. Taiwan
  • 11.12. Thailand
  • 11.13. Vietnam

12. Europe, Middle East & Africa Healthcare Fraud Analytics Market

  • 12.1. Introduction
  • 12.2. Denmark
  • 12.3. Egypt
  • 12.4. Finland
  • 12.5. France
  • 12.6. Germany
  • 12.7. Israel
  • 12.8. Italy
  • 12.9. Netherlands
  • 12.10. Nigeria
  • 12.11. Norway
  • 12.12. Poland
  • 12.13. Qatar
  • 12.14. Russia
  • 12.15. Saudi Arabia
  • 12.16. South Africa
  • 12.17. Spain
  • 12.18. Sweden
  • 12.19. Switzerland
  • 12.20. Turkey
  • 12.21. United Arab Emirates
  • 12.22. United Kingdom

13. Competitive Landscape

  • 13.1. Market Share Analysis, 2023
  • 13.2. FPNV Positioning Matrix, 2023
  • 13.3. Competitive Scenario Analysis
    • 13.3.1. Huize Announces Strategic Partnership with Ping An Health Insurance and the Joint Launch of "Chang Xiang An" - A Customized Long-term Medical Insurance Product
    • 13.3.2. ACKO Acquires Digital Health Platform Parentlane To Expand Beyond Core Insurance Offerings
    • 13.3.3. Amazon completes $3.9B acquisition of One Medical
    • 13.3.4. Paul Merchants partners with Care Health Insurance
    • 13.3.5. Reliance General Insurance partners with Paytm to offer customisable health policy
    • 13.3.6. Healthcos Are Accumulating Patents Aggressively In Electronic Records And Personalized Medicine
    • 13.3.7. SBI General Insurance launches new health insurance vertical
    • 13.3.8. Health insurance startup ClaimBuddy raises $3 million in funding
    • 13.3.9. AI-Powered Dental Insurance Fraud, Waste and Abuse Detection AI System
  • 13.4. Strategy Analysis & Recommendation

Companies Mentioned

  • 1. Atos SE
  • 2. CGI Inc.
  • 3. Change Healthcare Inc.
  • 4. Claroty Ltd.
  • 5. Codoxo, Inc.
  • 6. Conduent, Inc.
  • 7. Coviti, Inc.
  • 8. DXC Technology Company
  • 9. ExlService Holdings, Inc.
  • 10. Fair Isaac Corporation
  • 11. Fortified Health Security
  • 12. FraudLens Inc.
  • 13. FRISS
  • 14. H2O.ai, Inc.
  • 15. HCL Technologies Ltd.
  • 16. Healthcare fraud Shield
  • 17. Hewlett Packard Enterprise Development LP
  • 18. Imperva, Inc.
  • 19. Intel Corporation
  • 20. International Business Machines Corporation
  • 21. LexisNexis Risk Solutions Group
  • 22. Mckesson Corporation
  • 23. Multuplan Corporaton
  • 24. Northrop Grumman Corporation
  • 25. OneSpan Inc.
  • 26. OSP Labs
  • 27. Pondera Solutions
  • 28. Qlarant Inc.
  • 29. RELX Group Plc
  • 30. SAS Institute Inc.
  • 31. Sharecare, Inc.
  • 32. United Health Group Incorporated
  • 33. Wipro Limited
ºñ±³¸®½ºÆ®
0 °ÇÀÇ »óǰÀ» ¼±Åà Áß
»óǰ ºñ±³Çϱâ
Àüü»èÁ¦