ヘルスケア不正分析市場の2030年までの予測： ソリューションタイプ別、展開別、用途別、エンドユーザー別、地域別の世界分析

Stratistics MRCによると、世界の医療不正分析市場は2023年に23億米ドルを占め、予測期間中のCAGRは24.7%で成長し、2030年には109億米ドルに達する見込みです。

医療不正分析市場とは、不正行為の検出、防止、減少のために最先端の技術と分析を使用する医療ビジネスの新興セグメントを表しています。医療の現場がより複雑化し、電子カルテ、請求システム、クレームなど複数の情報源から生成されるデータ量が増加するにつれ、強固な不正検出手順がますます必要になってきています。

OIGによると、メディケイドのデータは不完全で不正確なことが多く、不正請求の検出プロセスに影響を与え、FWAによる数十億米ドルの浪費を招いています。

電子カルテの普及

医療システムがデジタル・プラットフォームに移行し、膨大な量の患者データを利用できるようになると、可能性と課題の両方が発生します。電子カルテ（EHR）の利用により、より広範で一元化された医療記録のデータベースを作成することが可能になり、これが不正の機会を提供します。さらに、これを防ぐために、医療機関は高度な分析ツールを使って電子医療データを精査し、不正を示す可能性のある不正や動向を探っています。

統合の複雑さ

既存の医療インフラへの高度な不正分析システムの統合は、複雑で時間のかかる一般的な実装作業です。この複雑さは、異なる情報フォーマット、医療機関間の一貫性のない標準、旧式のシステムとの互換性の問題によって増大します。効率的なデータフローとリアルタイムの分析を確保する必要があるため、多様なITシステムを持つ医療機関を相手にする場合、シームレスな統合を実現するのは難しいです。しかし、従来のワークフローに慣れたスタッフが医療プロバイダーに反発し、業務に支障をきたす可能性もあります。

技術の進歩

医療部門の不正防止能力は、分析ツール、機械学習アルゴリズム、人工知能の継続的な開発によって変貌を遂げています。こうした技術の進歩は、膨大な量の医療データをリアルタイムで処理し、より複雑で効果的な不正検知技術を可能にします。高度な分析により、複雑なパターン、異常、疑わしい手段を検出することで、不正検出の精度とスピードが向上します。さらに、最先端技術を取り入れることで、医療企業は金銭的損失を最小限に抑え、システムの完全性を維持しながら、ますます巧妙になる不正スキームに先手を打つことができます。

データ・セキュリティとプライバシーに関する懸念

セキュリティ侵害やプライバシー侵害に関する懸念は、膨大な量の機密性の高い患者データの管理によってもたらされるものであり、高度なアナリティクスを利用して不正行為に対抗する企業が増加する中で、医療企業にとっての懸念となっています。医療業界は規制が厳しいため、不正アクセスやデータ漏洩、サイバー攻撃のリスクが大きいです。複雑な問題を解決するには、HIPAA（医療保険の相互運用性と説明責任に関する法律）などのプライバシーに関する規則を厳格に遵守すると同時に、公平な方法で患者データから重要な洞察を収集する必要があります。

COVID-19の影響：

リソースを効率的に配分し、不正を防止することが世界中の医療システムに求められているため、不正分析ソリューションの重要性はこれまで以上に高まっています。その一方で、疫病の流行は医療システムに混乱を引き起こし、リソースを流用し、改善策に急速に注目が集まっています。新しい医療サービスの迅速な導入とCOVID-19に関連した取引の急増により、不正検知システムはより困難なものとなっています。さらに、パンデミックの経済効果は、虚偽の請求をさらに促進する可能性があります。

予測的分析分野は予測期間中に最大となる見込み

予測的分析分野は予測期間中に最大となる見込みです。予測分析は、高度なアルゴリズムと機械学習モデルを用いて、過去の情報を分析し、動向を特定し、将来の不正行為を予測します。医療事業者は、積極的なアプローチを採用し、新たな不正スキームを先取りすることで、財務上の損失を防ぎ、医療システムの完全性を守ることができます。さらに、予測分析は、大規模なデータセットをリアルタイムで分析し、疑わしい行動を発見する精度を高めながら、誤検出を減らすことで、不正検出の有効性を向上させます。

予測期間中にCAGRが最も高くなると予想されるのは薬局請求問題セグメントです。

薬局請求問題は、最も高いCAGRが見込まれる分野です。過剰請求、アンバンドリング、不正処方箋の請求など、薬局の請求問題は医療業界における不正の主要手段として浮上しています。こうした不正行為の増加により、薬局請求データの異常や不一致を特定するために設計された専門的な分析ソリューションの必要性が高まっています。予測モデリングや機械学習アルゴリズムなどのリアルタイム不正分析ツールは、薬局請求トランザクションの調査に使用されています。

最大のシェアを持つ地域：

この地域の急速な近代化とデジタルトランスフォーメーションにより、多くの国が電子カルテ（EHR）やその他のデジタルヘルス技術を導入しており、アジア太平洋が最大の割合を占めています。アジア太平洋の医療支払者とプロバイダーは、医療費の上昇と不正行為に関連する罰則の強化の結果、高度な分析ソリューションに投資しています。また、アジア太平洋では、医療システムの説明責任と透明性の向上を目的とした規制措置が明らかに増加しています。

CAGRが最も高い地域：

複雑な医療インフラと洗練された償還システムにより、北米地域は収益性の高い拡大を続けるのに有利な立場にあります。ヘルスケア不正がもたらす金銭的損害が拡大しているため、規制当局は、医療業界における詐欺を防止するために、米国ではFalse Claims Act（偽請求法）やHealth Insurance Portability and Accountability Act（HIPAA）などの広範な法律を制定しています。さらに、高度なアナリティクス・ソリューションの採用は、より多くの透明性、データ保護、不正検出機能を必要とするこれらの規制措置によって促されています。

無料のカスタマイズサービス：

本レポートをご購読のお客様には、以下の無料カスタマイズオプションのいずれかをご利用いただけます。

• 企業プロファイル
  • 追加市場参入企業の包括的プロファイリング（3社まで）
  • 主要企業のSWOT分析（3社まで）
• 地域セグメンテーション
  • 顧客の関心に応じた主要国の市場推定・予測・CAGR（注：フィージビリティチェックによる）
• 競合ベンチマーキング
  • 製品ポートフォリオ、地理的プレゼンス、戦略的提携に基づく主要企業のベンチマーキング

目次

第1章 エグゼクティブサマリー

第2章 序文

• 概要
• ステークホルダー
• 調査範囲
• 調査手法
  • データ鉱業
  • データ分析
  • データ検証
  • 調査アプローチ
• 調査情報源
  • 1次調査情報源
  • 2次調査情報源
  • 前提条件

第3章 市場動向分析

• イントロダクション
• 促進要因
• 抑制要因
• 機会
• 脅威
• 用途分析
• エンドユーザー分析
• 新興市場
• 新型コロナウイルス感染症（COVID-19）の影響

第4章 ポーターのファイブフォース分析

• 供給企業の交渉力
• 買い手の交渉力
• 代替品の脅威
• 新規参入業者の脅威
• 競争企業間の敵対関係

第5章 世界のヘルスケア不正分析市場：ソリューションタイプ別

• イントロダクション
• 予測的分析
• 規範的分析
• 記述的分析
• 他のタイプのソリューション

第6章 世界のヘルスケア不正分析市場：展開別

• イントロダクション
• クラウドベース
• オンプレミス

第7章 世界のヘルスケア不正分析市場：用途別

• イントロダクション
• 支払いの完全性
• 薬局請求問題
• 保険金請求の見直し
  • 前払い審査
  • 後払い審査
• その他の用途

第8章 世界のヘルスケア不正分析市場：エンドユーザー別

• イントロダクション
• サードパーティのサービスプロバイダー
• 民間保険の支払者
• 公共機関と政府機関
• その他のエンドユーザー

第9章 世界のヘルスケア不正分析市場：地域別

• イントロダクション
• 北米
  • 米国
  • カナダ
  • メキシコ
• 欧州
  • ドイツ
  • 英国
  • イタリア
  • フランス
  • スペイン
  • その他の欧州
• アジア太平洋
  • 日本
  • 中国
  • インド
  • オーストラリア
  • ニュージーランド
  • 韓国
  • その他のアジア太平洋
• 南米
  • アルゼンチン
  • ブラジル
  • チリ
  • その他の南米
• 中東とアフリカ
  • サウジアラビア
  • アラブ首長国連邦
  • カタール
  • 南アフリカ
  • その他の中東とアフリカ

第10章 主要発展

• 契約、パートナーシップ、コラボレーション、合弁事業
• 買収と合併
• 新製品の発売
• 事業拡大
• その他の主要戦略

第11章 企業プロファイル

• Conduent Inc
• Cotiviti Inc
• DXC Technology
• EXL Service Holdings Inc
• HCL Technologies Limited
• IBM
• Optum Inc.
• OSP Labs
• SAS Institute Inc
• Wipro Limited

List of Tables

• Table 1 Global Healthcare Fraud Analytics Market Outlook, By Region (2021-2030) ($MN)
• Table 2 Global Healthcare Fraud Analytics Market Outlook, By Solution Type (2021-2030) ($MN)
• Table 3 Global Healthcare Fraud Analytics Market Outlook, By Predictive Analytics (2021-2030) ($MN)
• Table 4 Global Healthcare Fraud Analytics Market Outlook, By Prescriptive Analytics (2021-2030) ($MN)
• Table 5 Global Healthcare Fraud Analytics Market Outlook, By Descriptive Analytics (2021-2030) ($MN)
• Table 6 Global Healthcare Fraud Analytics Market Outlook, By Other Solution Types (2021-2030) ($MN)
• Table 7 Global Healthcare Fraud Analytics Market Outlook, By Deployment (2021-2030) ($MN)
• Table 8 Global Healthcare Fraud Analytics Market Outlook, By Cloud-Based (2021-2030) ($MN)
• Table 9 Global Healthcare Fraud Analytics Market Outlook, By On-Premises (2021-2030) ($MN)
• Table 10 Global Healthcare Fraud Analytics Market Outlook, By Application (2021-2030) ($MN)
• Table 11 Global Healthcare Fraud Analytics Market Outlook, By Payment Integrity (2021-2030) ($MN)
• Table 12 Global Healthcare Fraud Analytics Market Outlook, By Pharmacy Billing Issue (2021-2030) ($MN)
• Table 13 Global Healthcare Fraud Analytics Market Outlook, By Insurance Claims Review (2021-2030) ($MN)
• Table 14 Global Healthcare Fraud Analytics Market Outlook, By Prepayment Review (2021-2030) ($MN)
• Table 15 Global Healthcare Fraud Analytics Market Outlook, By Postpayment Review (2021-2030) ($MN)
• Table 16 Global Healthcare Fraud Analytics Market Outlook, By Other Applications (2021-2030) ($MN)
• Table 17 Global Healthcare Fraud Analytics Market Outlook, By End User (2021-2030) ($MN)
• Table 18 Global Healthcare Fraud Analytics Market Outlook, By Third Party Service Providers (2021-2030) ($MN)
• Table 19 Global Healthcare Fraud Analytics Market Outlook, By Private Insurance Payers (2021-2030) ($MN)
• Table 20 Global Healthcare Fraud Analytics Market Outlook, By Public & Government Agencies (2021-2030) ($MN)
• Table 21 Global Healthcare Fraud Analytics Market Outlook, By Other End Users (2021-2030) ($MN)
• Table 22 North America Healthcare Fraud Analytics Market Outlook, By Country (2021-2030) ($MN)
• Table 23 North America Healthcare Fraud Analytics Market Outlook, By Solution Type (2021-2030) ($MN)
• Table 24 North America Healthcare Fraud Analytics Market Outlook, By Predictive Analytics (2021-2030) ($MN)
• Table 25 North America Healthcare Fraud Analytics Market Outlook, By Prescriptive Analytics (2021-2030) ($MN)
• Table 26 North America Healthcare Fraud Analytics Market Outlook, By Descriptive Analytics (2021-2030) ($MN)
• Table 27 North America Healthcare Fraud Analytics Market Outlook, By Other Solution Types (2021-2030) ($MN)
• Table 28 North America Healthcare Fraud Analytics Market Outlook, By Deployment (2021-2030) ($MN)
• Table 29 North America Healthcare Fraud Analytics Market Outlook, By Cloud-Based (2021-2030) ($MN)
• Table 30 North America Healthcare Fraud Analytics Market Outlook, By On-Premises (2021-2030) ($MN)
• Table 31 North America Healthcare Fraud Analytics Market Outlook, By Application (2021-2030) ($MN)
• Table 32 North America Healthcare Fraud Analytics Market Outlook, By Payment Integrity (2021-2030) ($MN)
• Table 33 North America Healthcare Fraud Analytics Market Outlook, By Pharmacy Billing Issue (2021-2030) ($MN)
• Table 34 North America Healthcare Fraud Analytics Market Outlook, By Insurance Claims Review (2021-2030) ($MN)
• Table 35 North America Healthcare Fraud Analytics Market Outlook, By Prepayment Review (2021-2030) ($MN)
• Table 36 North America Healthcare Fraud Analytics Market Outlook, By Postpayment Review (2021-2030) ($MN)
• Table 37 North America Healthcare Fraud Analytics Market Outlook, By Other Applications (2021-2030) ($MN)
• Table 38 North America Healthcare Fraud Analytics Market Outlook, By End User (2021-2030) ($MN)
• Table 39 North America Healthcare Fraud Analytics Market Outlook, By Third Party Service Providers (2021-2030) ($MN)
• Table 40 North America Healthcare Fraud Analytics Market Outlook, By Private Insurance Payers (2021-2030) ($MN)
• Table 41 North America Healthcare Fraud Analytics Market Outlook, By Public & Government Agencies (2021-2030) ($MN)
• Table 42 North America Healthcare Fraud Analytics Market Outlook, By Other End Users (2021-2030) ($MN)
• Table 43 Europe Healthcare Fraud Analytics Market Outlook, By Country (2021-2030) ($MN)
• Table 44 Europe Healthcare Fraud Analytics Market Outlook, By Solution Type (2021-2030) ($MN)
• Table 45 Europe Healthcare Fraud Analytics Market Outlook, By Predictive Analytics (2021-2030) ($MN)
• Table 46 Europe Healthcare Fraud Analytics Market Outlook, By Prescriptive Analytics (2021-2030) ($MN)
• Table 47 Europe Healthcare Fraud Analytics Market Outlook, By Descriptive Analytics (2021-2030) ($MN)
• Table 48 Europe Healthcare Fraud Analytics Market Outlook, By Other Solution Types (2021-2030) ($MN)
• Table 49 Europe Healthcare Fraud Analytics Market Outlook, By Deployment (2021-2030) ($MN)
• Table 50 Europe Healthcare Fraud Analytics Market Outlook, By Cloud-Based (2021-2030) ($MN)
• Table 51 Europe Healthcare Fraud Analytics Market Outlook, By On-Premises (2021-2030) ($MN)
• Table 52 Europe Healthcare Fraud Analytics Market Outlook, By Application (2021-2030) ($MN)
• Table 53 Europe Healthcare Fraud Analytics Market Outlook, By Payment Integrity (2021-2030) ($MN)
• Table 54 Europe Healthcare Fraud Analytics Market Outlook, By Pharmacy Billing Issue (2021-2030) ($MN)
• Table 55 Europe Healthcare Fraud Analytics Market Outlook, By Insurance Claims Review (2021-2030) ($MN)
• Table 56 Europe Healthcare Fraud Analytics Market Outlook, By Prepayment Review (2021-2030) ($MN)
• Table 57 Europe Healthcare Fraud Analytics Market Outlook, By Postpayment Review (2021-2030) ($MN)
• Table 58 Europe Healthcare Fraud Analytics Market Outlook, By Other Applications (2021-2030) ($MN)
• Table 59 Europe Healthcare Fraud Analytics Market Outlook, By End User (2021-2030) ($MN)
• Table 60 Europe Healthcare Fraud Analytics Market Outlook, By Third Party Service Providers (2021-2030) ($MN)
• Table 61 Europe Healthcare Fraud Analytics Market Outlook, By Private Insurance Payers (2021-2030) ($MN)
• Table 62 Europe Healthcare Fraud Analytics Market Outlook, By Public & Government Agencies (2021-2030) ($MN)
• Table 63 Europe Healthcare Fraud Analytics Market Outlook, By Other End Users (2021-2030) ($MN)
• Table 64 Asia Pacific Healthcare Fraud Analytics Market Outlook, By Country (2021-2030) ($MN)
• Table 65 Asia Pacific Healthcare Fraud Analytics Market Outlook, By Solution Type (2021-2030) ($MN)
• Table 66 Asia Pacific Healthcare Fraud Analytics Market Outlook, By Predictive Analytics (2021-2030) ($MN)
• Table 67 Asia Pacific Healthcare Fraud Analytics Market Outlook, By Prescriptive Analytics (2021-2030) ($MN)
• Table 68 Asia Pacific Healthcare Fraud Analytics Market Outlook, By Descriptive Analytics (2021-2030) ($MN)
• Table 69 Asia Pacific Healthcare Fraud Analytics Market Outlook, By Other Solution Types (2021-2030) ($MN)
• Table 70 Asia Pacific Healthcare Fraud Analytics Market Outlook, By Deployment (2021-2030) ($MN)
• Table 71 Asia Pacific Healthcare Fraud Analytics Market Outlook, By Cloud-Based (2021-2030) ($MN)
• Table 72 Asia Pacific Healthcare Fraud Analytics Market Outlook, By On-Premises (2021-2030) ($MN)
• Table 73 Asia Pacific Healthcare Fraud Analytics Market Outlook, By Application (2021-2030) ($MN)
• Table 74 Asia Pacific Healthcare Fraud Analytics Market Outlook, By Payment Integrity (2021-2030) ($MN)
• Table 75 Asia Pacific Healthcare Fraud Analytics Market Outlook, By Pharmacy Billing Issue (2021-2030) ($MN)
• Table 76 Asia Pacific Healthcare Fraud Analytics Market Outlook, By Insurance Claims Review (2021-2030) ($MN)
• Table 77 Asia Pacific Healthcare Fraud Analytics Market Outlook, By Prepayment Review (2021-2030) ($MN)
• Table 78 Asia Pacific Healthcare Fraud Analytics Market Outlook, By Postpayment Review (2021-2030) ($MN)
• Table 79 Asia Pacific Healthcare Fraud Analytics Market Outlook, By Other Applications (2021-2030) ($MN)
• Table 80 Asia Pacific Healthcare Fraud Analytics Market Outlook, By End User (2021-2030) ($MN)
• Table 81 Asia Pacific Healthcare Fraud Analytics Market Outlook, By Third Party Service Providers (2021-2030) ($MN)
• Table 82 Asia Pacific Healthcare Fraud Analytics Market Outlook, By Private Insurance Payers (2021-2030) ($MN)
• Table 83 Asia Pacific Healthcare Fraud Analytics Market Outlook, By Public & Government Agencies (2021-2030) ($MN)
• Table 84 Asia Pacific Healthcare Fraud Analytics Market Outlook, By Other End Users (2021-2030) ($MN)
• Table 85 South America Healthcare Fraud Analytics Market Outlook, By Country (2021-2030) ($MN)
• Table 86 South America Healthcare Fraud Analytics Market Outlook, By Solution Type (2021-2030) ($MN)
• Table 87 South America Healthcare Fraud Analytics Market Outlook, By Predictive Analytics (2021-2030) ($MN)
• Table 88 South America Healthcare Fraud Analytics Market Outlook, By Prescriptive Analytics (2021-2030) ($MN)
• Table 89 South America Healthcare Fraud Analytics Market Outlook, By Descriptive Analytics (2021-2030) ($MN)
• Table 90 South America Healthcare Fraud Analytics Market Outlook, By Other Solution Types (2021-2030) ($MN)
• Table 91 South America Healthcare Fraud Analytics Market Outlook, By Deployment (2021-2030) ($MN)
• Table 92 South America Healthcare Fraud Analytics Market Outlook, By Cloud-Based (2021-2030) ($MN)
• Table 93 South America Healthcare Fraud Analytics Market Outlook, By On-Premises (2021-2030) ($MN)
• Table 94 South America Healthcare Fraud Analytics Market Outlook, By Application (2021-2030) ($MN)
• Table 95 South America Healthcare Fraud Analytics Market Outlook, By Payment Integrity (2021-2030) ($MN)
• Table 96 South America Healthcare Fraud Analytics Market Outlook, By Pharmacy Billing Issue (2021-2030) ($MN)
• Table 97 South America Healthcare Fraud Analytics Market Outlook, By Insurance Claims Review (2021-2030) ($MN)
• Table 98 South America Healthcare Fraud Analytics Market Outlook, By Prepayment Review (2021-2030) ($MN)
• Table 99 South America Healthcare Fraud Analytics Market Outlook, By Postpayment Review (2021-2030) ($MN)
• Table 100 South America Healthcare Fraud Analytics Market Outlook, By Other Applications (2021-2030) ($MN)
• Table 101 South America Healthcare Fraud Analytics Market Outlook, By End User (2021-2030) ($MN)
• Table 102 South America Healthcare Fraud Analytics Market Outlook, By Third Party Service Providers (2021-2030) ($MN)
• Table 103 South America Healthcare Fraud Analytics Market Outlook, By Private Insurance Payers (2021-2030) ($MN)
• Table 104 South America Healthcare Fraud Analytics Market Outlook, By Public & Government Agencies (2021-2030) ($MN)
• Table 105 South America Healthcare Fraud Analytics Market Outlook, By Other End Users (2021-2030) ($MN)
• Table 106 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By Country (2021-2030) ($MN)
• Table 107 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By Solution Type (2021-2030) ($MN)
• Table 108 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By Predictive Analytics (2021-2030) ($MN)
• Table 109 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By Prescriptive Analytics (2021-2030) ($MN)
• Table 110 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By Descriptive Analytics (2021-2030) ($MN)
• Table 111 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By Other Solution Types (2021-2030) ($MN)
• Table 112 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By Deployment (2021-2030) ($MN)
• Table 113 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By Cloud-Based (2021-2030) ($MN)
• Table 114 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By On-Premises (2021-2030) ($MN)
• Table 115 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By Application (2021-2030) ($MN)
• Table 116 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By Payment Integrity (2021-2030) ($MN)
• Table 117 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By Pharmacy Billing Issue (2021-2030) ($MN)
• Table 118 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By Insurance Claims Review (2021-2030) ($MN)
• Table 119 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By Prepayment Review (2021-2030) ($MN)
• Table 120 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By Postpayment Review (2021-2030) ($MN)
• Table 121 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By Other Applications (2021-2030) ($MN)
• Table 122 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By End User (2021-2030) ($MN)
• Table 123 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By Third Party Service Providers (2021-2030) ($MN)
• Table 124 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By Private Insurance Payers (2021-2030) ($MN)
• Table 125 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By Public & Government Agencies (2021-2030) ($MN)
• Table 126 Middle East & Africa Healthcare Fraud Analytics Market Outlook, By Other End Users (2021-2030) ($MN)

According to Stratistics MRC, the Global Healthcare Fraud Analytics Market is accounted for $2.3 billion in 2023 and is expected to reach $10.9 billion by 2030 growing at a CAGR of 24.7% during the forecast period. The term "Healthcare Fraud Analytics Market" describes the emerging segment of the healthcare business that uses cutting-edge technology and analytics to detect, prevent, and lessen fraudulent activity. Robust fraud detection procedures are becoming more and more necessary as the healthcare landscape grows more complicated and involves a growing amount of data generated from several sources, such as electronic health records, billing systems, and claims.

According to the OIG, Medicaid data is frequently incomplete and inaccurate, affecting the process of detecting fraudulent claims and resulting in the waste of billions of dollars due to FWA.

Market Dynamics:

Driver:

Increasing adoption of electronic health records

There are both potential and challenges when healthcare systems move to digital platforms and make enormous volumes of patient data available. The use of electronic health records (EHRs) makes it possible to create a more extensive and centralized database of medical records, which offers an opportunity for fraud. Additionally, in order to prevent this, healthcare institutions are using advanced analytics tools to closely examine electronic health data in order to search for irregularities and trends that may indicate fraud.

Restraint:

Complexity of integration

The integration of advanced fraud analytics systems into pre-existing healthcare infrastructures is a common implementation task that can be complex and time-consuming. The complexity is increased by different information formats, inconsistent standards among healthcare institutions, and compatibility problems with outdated systems. It is difficult to achieve seamless integration when dealing with institutions that have diverse IT systems, as it is necessary to ensure efficient data flow and real-time analysis. However, staff members used to traditional workflows may oppose healthcare providers and cause operational interruptions.

Opportunity:

Advancements in technology

The healthcare sector's ability to prevent fraud has been transformed by the ongoing development of analytical tools, machine learning algorithms, and artificial intelligence. These technological advancements process enormous volumes of healthcare data in real time, enabling more complex and effective fraud detection techniques. Advanced analytics improve the accuracy and speed of fraud detection by detecting complex patterns, anomalies, and suspicious measures. Moreover, by incorporating cutting-edge technologies, healthcare companies may minimize financial losses and maintain the integrity of their systems while staying ahead of ever more sophisticated fraud schemes.

Threat:

Data security and privacy concerns

Concerns regarding security breaches and privacy violations are raised by the management of enormous amounts of sensitive patient data, which is a concern for healthcare companies as they use advanced analytics to combat fraud in increasing numbers. Because the healthcare industry is heavily regulated, there is a significant risk of unauthorized access, data leaks, or cyberattacks. Achieving a complicated problem requires strict compliance with privacy rules such as HIPAA (Health Insurance Portability and Accountability Act) while also collecting important insights from patient data in an equitable manner.

COVID-19 Impact:

Fraud analytics solutions are more important than ever because of the growing pressure on healthcare systems throughout the world to allocate resources efficiently and prevent fraud. On the other hand, the epidemic has also caused disruptions in the healthcare system, diverting resources and rapid attention to remedies. The quick adoption of new healthcare services and the surge in transactions associated with COVID-19 have made fraud detection systems more challenging. Furthermore, the pandemic's economic effects could promote further false claims.

The predictive analytics segment is expected to be the largest during the forecast period

Predictive analytics segment is expected to be the largest during the forecast period. Predictive analytics analyzes prior information, identifies trends, and projects future fraudulent activity using sophisticated algorithms and machine learning models. Healthcare businesses can prevent financial losses and safeguard the integrity of healthcare systems by adopting a proactive approach and staying ahead of emerging fraud schemes. Furthermore, predictive analytics improves the effectiveness of fraud detection by analyzing large datasets in real time and increasing the accuracy of spotting suspicious behavior while reducing false positives.

The pharmacy billing issue segment is expected to have the highest CAGR during the forecast period

Pharmacy billing issue segment is expected to have the highest CAGR. Pharmacy billing problems, like overbilling, unbundling, or charging for fraudulent prescriptions, have emerged as major avenues for fraud in the healthcare industry. The need for specialist analytics solutions designed to identify anomalies and discrepancies in pharmacy billing data has increased due to the rise in these fraudulent activities. Real-time fraud analytics tools such as predictive modeling and machine learning algorithms are being used to examine pharmacy billing transactions.

Region with largest share:

Due to the region's rapid modernization and digital transformation, many of its nations have adopted electronic health records (EHRs) and other digital health technologies, the Asia-Pacific area accounted for the largest percentage. Healthcare payers and providers in Asia Pacific are investing in advanced analytics solutions as a result of rising healthcare costs and growing penalties associated with fraud. In addition, there is an apparent rise in regulatory actions in the Asia-Pacific area that are intended to improve accountability and transparency in healthcare systems.

Region with highest CAGR:

Because of the complex healthcare infrastructure and sophisticated reimbursement system, the North American region is better positioned to continue profitable expansion. Because of the growing financial damage that healthcare fraud causes, regulatory agencies have enacted extensive laws, such as the False Claims Act and the Health Insurance Portability and Accountability Act (HIPAA) in the United States, to prevent fraud in the healthcare industry. Moreover, the adoption of advanced analytics solutions is urged by these regulatory measures, which need more transparency, data protection, and fraud detection capabilities.

Key players in the market:

Some of the key players in Healthcare Fraud Analytics market include Conduent Inc, Cotiviti Inc, DXC Technology, EXL Service Holdings Inc, HCL Technologies Limited, IBM, Optum Inc., OSP Labs, SAS Institute Inc and Wipro Limited.

Key Developments:

In November 2023, IBM launches new sustainability initiatives for global climate action. IBM's operations span a broad spectrum of technological fields, from AI and cloud computing to cybersecurity and data analytics.

In July 2023, HCLTech, the third largest IT services company in India, has acquired a 100 per cent equity stake in German automotive engineering services provider ASAP Group for €251 million ($279.72 million).

Solution Types Covered:

• Predictive Analytics
• Prescriptive Analytics
• Descriptive Analytics
• Other Solution Types

Deployments Covered:

• Cloud-Based
• On-Premises

Applications Covered:

• Payment Integrity
• Pharmacy Billing Issue
• Insurance Claims Review
• Other Applications

End Users Covered:

• Third Party Service Providers
• Private Insurance Payers
• Public & Government Agencies
• Other End Users

Regions Covered:

• North America
  • US
  • Canada
  • Mexico
• Europe
  • Germany
  • UK
  • Italy
  • France
  • Spain
  • Rest of Europe
• Asia Pacific
  • Japan
  • China
  • India
  • Australia
  • New Zealand
  • South Korea
  • Rest of Asia Pacific
• South America
  • Argentina
  • Brazil
  • Chile
  • Rest of South America
• Middle East & Africa
  • Saudi Arabia
  • UAE
  • Qatar
  • South Africa
  • Rest of Middle East & Africa

What our report offers:

• Market share assessments for the regional and country-level segments
• Strategic recommendations for the new entrants
• Covers Market data for the years 2021, 2022, 2023, 2026, and 2030
• Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
• Strategic recommendations in key business segments based on the market estimations
• Competitive landscaping mapping the key common trends
• Company profiling with detailed strategies, financials, and recent developments
• Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

• Company Profiling
  • Comprehensive profiling of additional market players (up to 3)
  • SWOT Analysis of key players (up to 3)
• Regional Segmentation
  • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
• Competitive Benchmarking
  • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

2 Preface

• 2.1 Abstract
• 2.2 Stake Holders
• 2.3 Research Scope
• 2.4 Research Methodology
  • 2.4.1 Data Mining
  • 2.4.2 Data Analysis
  • 2.4.3 Data Validation
  • 2.4.4 Research Approach
• 2.5 Research Sources
  • 2.5.1 Primary Research Sources
  • 2.5.2 Secondary Research Sources
  • 2.5.3 Assumptions

3 Market Trend Analysis

• 3.1 Introduction
• 3.2 Drivers
• 3.3 Restraints
• 3.4 Opportunities
• 3.5 Threats
• 3.6 Application Analysis
• 3.7 End User Analysis
• 3.8 Emerging Markets
• 3.9 Impact of Covid-19

4 Porters Five Force Analysis

• 4.1 Bargaining power of suppliers
• 4.2 Bargaining power of buyers
• 4.3 Threat of substitutes
• 4.4 Threat of new entrants
• 4.5 Competitive rivalry

5 Global Healthcare Fraud Analytics Market, By Solution Type

• 5.1 Introduction
• 5.2 Predictive Analytics
• 5.3 Prescriptive Analytics
• 5.4 Descriptive Analytics
• 5.5 Other Solution Types

6 Global Healthcare Fraud Analytics Market, By Deployment

• 6.1 Introduction
• 6.2 Cloud-Based
• 6.3 On-Premises

7 Global Healthcare Fraud Analytics Market, By Application

• 7.1 Introduction
• 7.2 Payment Integrity
• 7.3 Pharmacy Billing Issue
• 7.4 Insurance Claims Review
  • 7.4.1 Prepayment Review
  • 7.4.2 Postpayment Review
• 7.5 Other Applications

8 Global Healthcare Fraud Analytics Market, By End User

• 8.1 Introduction
• 8.2 Third Party Service Providers
• 8.3 Private Insurance Payers
• 8.4 Public & Government Agencies
• 8.5 Other End Users

9 Global Healthcare Fraud Analytics Market, By Geography

• 9.1 Introduction
• 9.2 North America
  • 9.2.1 US
  • 9.2.2 Canada
  • 9.2.3 Mexico
• 9.3 Europe
  • 9.3.1 Germany
  • 9.3.2 UK
  • 9.3.3 Italy
  • 9.3.4 France
  • 9.3.5 Spain
  • 9.3.6 Rest of Europe
• 9.4 Asia Pacific
  • 9.4.1 Japan
  • 9.4.2 China
  • 9.4.3 India
  • 9.4.4 Australia
  • 9.4.5 New Zealand
  • 9.4.6 South Korea
  • 9.4.7 Rest of Asia Pacific
• 9.5 South America
  • 9.5.1 Argentina
  • 9.5.2 Brazil
  • 9.5.3 Chile
  • 9.5.4 Rest of South America
• 9.6 Middle East & Africa
  • 9.6.1 Saudi Arabia
  • 9.6.2 UAE
  • 9.6.3 Qatar
  • 9.6.4 South Africa
  • 9.6.5 Rest of Middle East & Africa

10 Key Developments

• 10.1 Agreements, Partnerships, Collaborations and Joint Ventures
• 10.2 Acquisitions & Mergers
• 10.3 New Product Launch
• 10.4 Expansions
• 10.5 Other Key Strategies

11 Company Profiling

• 11.1 Conduent Inc
• 11.2 Cotiviti Inc
• 11.3 DXC Technology
• 11.4 EXL Service Holdings Inc
• 11.5 HCL Technologies Limited
• 11.6 IBM
• 11.7 Optum Inc.
• 11.8 OSP Labs
• 11.9 SAS Institute Inc
• 11.10 Wipro Limited