Event log-based fraud rating using interval type-2 fuzzy sets in fuzzy AHP

Fraud on the event logs derived from business process is known as event log-based fraud. Fraud detection using event logs employs process mining technique, notably conformance checking analysis. This study proposes a method for rating event log-based fraud datasets using fuzzy analytic hierarchy process (AHP), a well-known multicriteria decision-making method. Earlier studies proposed that interval type-2 fuzzy set provides an alternative in handling uncertainty than type-1 fuzzy set. Therefore, we utilize interval type-2 fuzzy sets in fuzzy AHP in order to manage vagueness in many linguistic judgement. This study includes linguistic hedges implementation to modify the membership function of expert valuation. The experimental results showed comparable types of membership function shape and its obtained accuracy performance. Obtained accuracy from fuzzy type-1 AHP method achieved 94% for triangular membership function shape and 93.9% for interval type-2 fuzzy AHP. Although there was no escalation in accuracy after applying interval-valued fuzzy sets for all scenarios, the rank of fraud weight in each feature were altered.