市場調查報告書
商品編碼
1445889
行為生物辨識:全球市場佔有率 (2022)Global Market Share: Behavioral Biometrics, 2022 |
預計到 2027 年,全球行為生物辨識市場將以 11.9% 的複合年增長率成長。
行為生物辨識技術是一種不斷發展的網路安全技術。 隨著網路和物聯網設備使用的增加,組織需要防範各種類型的詐欺和網路犯罪活動。 因此,由人工智慧和機器學習技術支援的先進行為生物辨識解決方案正在興起。 行為生物辨識技術會影響各種數字和認知行為,例如鍵盤移動、打字節奏、觸控螢幕移動以及用於使用者重新驗證的裝置移動。 與密碼認證、多重認證、令牌認證、證書認證、生物認證等傳統認證方式不同,行為生物識別技術不需要記住密碼,可以保護用戶免受網路攻擊,因此成為比較受歡迎的選擇。一種簡單的身份驗證方法。
組織繼續投資行為生物辨識解決方案,為身分驗證添加額外的防禦層,以偵測高風險情境並增強詐欺預防能力。 靜默身份驗證功能是推動行為生物辨識解決方案採用的關鍵因素。 金融機構正在專注於建立各種安全措施和策略,加強用戶身份驗證功能,以提高網路安全性,並防止客戶詐欺攻擊的蔓延。 行為生物辨識解決方案提供強大且可擴展的身份驗證功能,幫助金融機構應對日益增長的風險。 該解決方案專注於分析各種線上管道的用戶行為,並減少用戶資料庫維護。
在本報告中,我們將行為生物辨識技術定義為 "被動和連續、擊鍵動力學、設備互動、觸控螢幕互動、滑鼠移動、導航模式、表單上下文和流暢性以及客戶生活。" 監控、分析和驗證的技術基於行為、認知和反應屬性的用戶,包括整個週期的數據熟悉度。” 行為生物辨識技術利用先進的分析和機器學習模組不斷產生行為風險評分,減少誤報,最大限度地縮短識別和補救風險所需的時間,並創造無摩擦的客戶體驗。
雖然金融機構越來越多地採用生物辨識解決方案,但他們在將該技術與現有安全系統整合方面仍然面臨挑戰。 這些挑戰包括需要擁有大型生物辨識資料湖來做出準確的決策,以及需要提供安全的環境來儲存資料。 組織將繼續推動人工智慧和機器學習能力,在多模式生物辨識技術中採用行為分析將有助於提高行為生物辨識技術的自適應和預測能力。 這些改進包括預測和糾正用戶錯誤以及根據用戶互動的歷史模式正確分配資源。
本報告分析了全球行為生物識別市場的份額結構,包括解決方案概述、市場基本結構、按實施方法、地區、行業和公司類型以及行業參與者劃分的份額結構。我們將編譯並提供信息,例如為客戶提供的建議。
Quadrant Knowledge Solutions Reveals that Behavioral Biometrics Market is Projected to Register a CAGR of 11.9% by 2027.
Behavioral biometrics is an evolving cybersecurity technology. With the increasing use of internet and IOT devices, organizations are facing the growing need to combat various fraudulent and cybercrime activities. This has given rise to advanced behavioral biometric solutions which are backed by AI and ML technologies. Behavioral Biometrics factors in various digital and cognitive behaviors include keyboard dynamics, typing cadence, touchscreen movement, and device movement for user reauthentication. Unlike traditional authentication methods, including password-based authentication, multi-factor authentication, token-based, certificate-based and biometrics authentication, behavioral biometrics is comparatively simple authentication method as it does not require remembering passwords and prevents users from cyberattacks.
Organizations are continuing to invest in Behavioral Biometrics solutions to add an extra layer of defense to identity authentication to detect high-risk scenarios and enhancing fraud prevention capabilities. The silent authentication characteristic is the key factor in driving the adoption of behavioral biometrics solutions. FIs are focusing on building various security measures and strategies and strengthening the user authentication capabilities to improve online security to prevent their customers from growing fraud attacks. Behavioral Biometrics solutions provide robust and scalable authentication capabilities that aid FIs in fighting growing risks. The solutions focus on analyzing user behavior across online channels to reduce maintaining user database.
Quadrant Knowledge Solutions defines Behavioral Biometrics as "A technology that passively and continuously monitors, analyzes, and authenticates users based on their behavioral, cognitive, and response attributes such as keystroke dynamics, device handling, touchscreen interaction, mouse movements, navigation pattern, form context and fluency, and data familiarity across the entire customer lifecycle. Behavioral Biometrics leverages advanced analytics and machine learning modules to continuously generate behavioral risk scores that helps reduce false positives, minimize risk identification and remediation time and drives frictionless customer experience."
While FIs are increasingly adopting biometrics solutions, they continue to face a challenge in integrating this technology with their existing security systems. These challenges include the need to have a huge biometric data lake for accurate decisioning and the need to provide a secured environment for storing data. Organizations will continue to make advancements in AI and machine learning capabilities and adoption of behavioral profiling in multimodal biometrics will drive improvements in adaptive and predictive capabilities for behavioral biometrics technologies. These improvements would include predicting and rectifying user's mistake and correct allocation of resources based on historical patterns of user's interaction.