  The Belief Rule Base (BRB) system that adopts a hybrid approach integrating
the precision of expert systems with the adaptability of data-driven models.
Characterized by its use of if-then rules to accommodate various types of
uncertainty through belief degrees, BRB adeptly handles fuzziness, randomness,
and ignorance. This semi-quantitative tool excels in processing both numerical
data and linguistic knowledge from diverse sources, making it as an
indispensable resource in modelling complex nonlinear systems. Notably, BRB's
transparent, white-box nature ensures accessibility and clarity for
decision-makers and stakeholders, further enhancing its applicability. With its
growing adoption in fields ranging from decision-making and reliability
evaluation in network security and fault diagnosis, this study aims to explore
the evolution and the multifaceted applications of BRB. By analysing its
development across different domains, we highlight BRB's potential to
revolutionize sectors traditionally resistant to technological disruption, in
particular insurance and law.
