加密機構
金融機構数据泄露和网络攻击每年都在增加，给全球的公司带来了巨大的压力和成本。据Statista统计，单次数据泄露的平均成本约为435万美元。
根据平台的内部统计，超过70%的欺诈行为发生在入职阶段之后。因此，保护整个用户旅程至关重要，而这就是行为分析可以提供帮助的地方。
精彩要点
- 什么是行为分析？
- 行为分析与欺诈检测
- 什么是行为生物识别？
什么是行为分析？
行为分析是对行为模式的研究。在商业上，客户的交互方式形成了一种模式，最终构成了预期行为的剖面。这可能包括人们通常在什么时候登录应用，他们通常进行的交易类型，他们通常使用的设备，甚至他们使用键盘的方式。
行为分析通常涉及自动化和机器学习，追踪人类可能会错过的行为模式，并在个人级别和组织级别上发现异常。
行为分析与欺诈检测
行为分析广泛用于通过研究行为模式来识别和标记可能的欺诈事件、异常交易或其他犯罪活动。以下是具体方法：
确定基线行为。行为监控有助于为每个用户建立典型行为的基线。通过分析历史数据和模式，系统学习了什么被视为个体用户的通常行为。
实时监控。欺诈监控分析通常实时运行，使得可以立即检测到可疑活动。例如，如果一个用户突然展示出与他们的正常模式不一致的行为，比如在短时间内访问多个账户，展示出非典型的购买模式，或者进行异常大的交易，系统就可以触发一个可能的欺诈警报。
机器学习算法。行为监控系统通常会结合机器学习算法以不断提高他们的欺诈检测分析。这些算法能够适应新的欺诈模式，并基于大量数据识别出以前未知的可疑行为。通过利用机器学习，这些系统在检测欺诈方面会随着时间的推移变得更精确。
用户分析和风险评分。行为监控系统基于行为模式创建用户分析。每个用户都可以被分配一个风险评分，这表明他们可能涉及欺诈活动。风险评分通常是基于历史数据、检测到的异常和机器学习算法来计算的。高风险分析可能会受到额外的审查或验证措施。
欺诈的早期检测和预防。通过实时监控用户，行为监控工具可以在早期阶段检测到可能的欺诈，防止金融损失发生。早期检测能够快速采取行动防止欺诈交易，如阻止可疑活动、通知用户，或升级案件至执法部门。
适应性安全措施。行为监控可以帮助实施适应性安全措施。例如，如果一个用户展示出高风险行为，可能需要额外的验证步骤，如进行活体检测。这增加了一层额外的安全保护，以防止犯罪。
什么是行为生物识别？
行为分析和行为生物识别是相关的概念，但有明显的不同。
不同于行为分析，行为生物识别是指用个体的独特行为特征或性状进行识别或身份验证。它涉及到捕获和分析人类行为的特定模式，例如键盘敲击动态，鼠标移动，触屏手势，语音模式，甚至是个体的步行方式。行为生物识别特征被用于创建一个独特的用户剖面，并且任何未来的互动或交易都可以与这个剖面进行对比以进行验证。
虽然行为分析专注于通过分析各种行为指标并识别异常来检测欺诈，但行为生物识别则使用特定的行为性状作为生物识别标识符，用于用户识别或身份验证。
根据SNS Insider的数据，仅行为生物识别市场在2022年就估值达到20.1亿美元，并预计将在2030年达到116.4亿美元。
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