<![CDATA[Indonesia has faced challenges regarding the lack of compliance to the requirement of disclosing the financial assets of state administrators. This issue has contributed to the development of unfavorable public sentiment towards the compliance of state administrators, particularly in relation to corruption. Indonesia has implemented a corruption prevention mechanism known as the State Officials Wealth Report (LHKPN). The LHKPN is a compulsory declaration pertaining to the assets possessed by individuals from the time they assume a position in the government until their retirement. Despite the level of compliance achieved in LHKPN reporting, the report content is susceptible to some shortcomings in terms of its validity. The issue of validity is further compounded by the constraint of a limited resource of human resources. This phenomenon gives rise to the possibility of fraud occurring through the misrepresentation or concealment of assets. In light of the aforementioned, it is imperative to establish a comprehensive big data system integrated with risk management protocols to facilitate the LHKPN inspection procedure. This study aims to provide new ideas in the identification of potential fraud using early warning. In this study, the preparation of the system uses Software Development Life Cycle (SDLC). The Fraud Early Warning System integrates LHKPN data and comparative technical data using specific indicators and variables to assess the amount of risk within the identification framework. This system is able to provide red flag for assets that have not been disclosed or are deemed inappropriate, hence enabling the initiation of further follow-up actions. The application of this system can be a solution for strengthening fraud early detection related to the assets of state administrators as well as the potential to increase compliance in LHKPN reporting.]]>
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