Common use of Detection of Behavior Anomalies Clause in Contracts

Detection of Behavior Anomalies. Behavior anomaly detection monitors the user’s behavior to detect whether a transaction is possibly made by a fraudulent party. The identity of the user can only be ascertained after the user’s behavioral patterns have been registered and compared to a past baseline. Since the user is required to do something before there is some certainty of the user’s identity, anomaly detection based on behavior is unsuitable for user authentication (at the beginning of a session, before a user has performed any actions), but can be used to provide some certainty about the user’s identity and the validity of a user’s action afterwards. The origin of the data can come from user actions (such as at what time of the day a user performs an action and the user’s data entry speed) and from the environment in which the user’s device operates (such as the geographical location and local temperature). The used methods can be compared to those of data leakage detection systems used to spot anomalies in ACM Computing Surveys, Vol. 49, No. 4, Article 61, Publication date: December 2016. Method 2002 2013 2015 Password/PIN-only C(66.7%) C(23.8% + 13.8%) C(21.3% + 15%) OTP (paper/plastic) C(6.7%) C(16.3%) C(20%) OTP (offline electronic tokens)OTP (SMS) C C(13.8%) C(26.3%) C(33.8%) C(31.2%) Challenge-response (offline electronic tokens) C C(13.8%) C(12.5%) Certificate-based C(14.3%) C(3.8% + 2.5% + 5%) C(3.8% + 6.3% + 2.5%) data transactions (such as proposed by Xxxxxxxxx et al. [2014]). Several examples of implemented fraud detection techniques are given by Xxx et al. [2004], and Xxxx and Xxxxxxxxx [2007] provide an example of a proposal for a system that recognizes fraud. Since user behavior anomalies are registered by the back-end technical infrastruc- ture of banks, it cannot be said with full certainty how many of the banks in our 2013 and 2015 surveys apply this and to what extent. However, some banks state that they do use monitoring services for financial transactions [Ally Bank 2010; Bank of America 2013; Barclays 2014]*. It has also been claimed that some banks profile low-level user actions through mobile applications [Xxxxxxxx 2012]*.