AI-enabled Regulatory Change Analysis of Legal Requirements

Statutory law is subject to change as legislation
develops over time – new regulation can be introduced, while
existing regulation can be amended, or repealed. From a requirements
engineering (RE) perspective, such change must be dealt
with to ensure the compliance of software systems at all times.
Understanding the implications of regulatory change on compliance
of software requirements requires navigating hundreds
of legal provisions. Analyzing instances of regulatory change
entirely manually is not only time-consuming, but also risky, since
missing a change may result in non-compliant software which can
in turn lead to hefty fines. In this paper, we propose MURCIA,
an automated approach that leverages recent language models to
assist human analysts in analyzing regulatory changes. To build
MURCIA, we define a taxonomy that characterizes the regulatory
changes at the textual level as well as the changes in the text’s
meaning and legal interpretation. We evaluate MURCIA on four
regulations from the financial domain. Over our evaluation set,
MURCIA can identify textual changes with F1 score of 90.5%,
and it can provide, according to our taxonomy, the text meaning
and legal interpretation with an F1 score of 90.8% and 83.7%, respectively.