Identification of Spatial Autocorrelation in Cases of Complaints About Illegal Online Loans in Indonesia

Abstract

The development of digital financial services has made it easier for people to access technology-based loans, but this has also been accompanied by an increase in illegal online lending practices, which have generated numerous complaints from consumers in various regions. This situation indicates the potential for uneven distribution of cases across regions. This study aims to identify interregional spatial relationships in data on the number of complaints about illegal online loans across provinces in Indonesia. This study uses data on the number of complaints about illegal online loans from January to June 2025. The analytical methods used include descriptive statistics, the Moran Index to measure global spatial autocorrelation, and the Local Indicator of Spatial Autocorrelation (LISA) to detect local clusters. The results show positive spatial autocorrelation, where provinces with a high number of complaints tend to be close to each other, particularly areas with high levels of urbanization and digital activity such as DKI Jakarta, West Java, Central Java, and Banten. Thus, the distribution of complaints about illegal online loans is not random but is influenced by geographic proximity and regional socioeconomic characteristics.