市场调查报告书
商品编码
1445889
行为生物辨识:全球市场占有率 (2022)Global Market Share: Behavioral Biometrics, 2022 |
预计到 2027 年,全球行为生物辨识市场将以 11.9% 的复合年增长率成长。
行为生物辨识技术是一种不断发展的网路安全技术。 随着网路和物联网设备使用的增加,组织需要防范各种类型的诈欺和网路犯罪活动。 因此,由人工智慧和机器学习技术支援的先进行为生物辨识解决方案正在兴起。 行为生物辨识技术会影响各种数字和认知行为,例如键盘移动、打字节奏、触控萤幕移动以及用于使用者重新验证的装置移动。 与密码认证、多重认证、令牌认证、证书认证、生物认证等传统认证方式不同,行为生物识别技术不需要记住密码,可以保护用户免受网路攻击,因此成为比较受欢迎的选择。一种简单的身份验证方法。
组织继续投资行为生物辨识解决方案,为身分验证添加额外的防御层,以侦测高风险情境并增强诈欺预防能力。 静默身份验证功能是推动行为生物辨识解决方案采用的关键因素。 金融机构正在专注于建立各种安全措施和策略,加强用户身份验证功能,以提高网路安全性,并防止客户诈欺攻击的蔓延。 行为生物辨识解决方案提供强大且可扩展的身份验证功能,帮助金融机构应对日益增长的风险。 该解决方案专注于分析各种线上管道的用户行为,并减少用户资料库维护。
在本报告中,我们将行为生物辨识技术定义为 "被动和连续、击键动力学、设备互动、触控萤幕互动、滑鼠移动、导航模式、表单上下文和流畅性以及客户生活。" 监控、分析和验证的技术基于行为、认知和反应属性的用户,包括整个週期的数据熟悉度。” 行为生物辨识技术利用先进的分析和机器学习模组不断产生行为风险评分,减少误报,最大限度地缩短识别和补救风险所需的时间,并创造无摩擦的客户体验。
虽然金融机构越来越多地采用生物辨识解决方案,但他们在将该技术与现有安全系统整合方面仍然面临挑战。 这些挑战包括需要拥有大型生物辨识资料湖来做出准确的决策,以及需要提供安全的环境来储存资料。 组织将继续推动人工智慧和机器学习能力,在多模式生物辨识技术中采用行为分析将有助于提高行为生物辨识技术的自适应和预测能力。 这些改进包括预测和纠正用户错误以及根据用户互动的历史模式正确分配资源。
本报告分析了全球行为生物识别市场的份额结构,包括解决方案概述、市场基本结构、按实施方法、地区、行业和公司类型以及行业参与者划分的份额结构。我们将编译并提供信息,例如为客户提供的建议。
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.