# Suspicious Financial Activity in the Context of In-Game Asset Exchange Marketplace
## 3 Methodology  
A literature review was performed, collecting, analyzing, and synthesizing research on suspicious activities in the in-game asset exchange website. The objective of conducting the review was to come up with a conceptual understanding of the current state of knowledge, the absence of the literature, and directions for further investigation. Critically reviewing the literature and identifying the gap in the area helped identify what needed to be conducted. Therefore, the main objective of this work was to gather and analyze a dataset from an online in-game asset exchange website. After defining what counts as suspicious activity, the next phase was to identify the right dataset for analysis. Using this foundation, we sought a comprehensive dataset containing detailed transaction records, including buyer and seller names and IDs, along with transaction dates. There are several in-game asset exchange websites, which have been ranked according to the number of visitors. Leveraging Semrush's 2024 rankings for website traffic, we selected a high-traffic in-game asset exchange platform for data collection [25]. This website allows sellers to sell in-game assets, such as skins, in-game gold, or money, for different games. To obtain the relevant dataset, the in-game asset exchange website needed to be scraped. Initially, the information on the website was more about sellers than buyers. However, the website had an application programming interface (API) that provided all of the relevant information about both sellers and buyers, including their IDs and transaction dates. The scrape began by focusing on popular games that offered up these assets. Top-ranked users who did not have verified accounts were selected from the seller list since they offered multiple transactions that could be used to grow the dataset. After scraping the seller's dataset, the focus was then shifted to buyers. This led to an interesting finding that some of the buyers' accounts were selling assets in their accounts as well. This meant that it was necessary to look at their seller accounts, buying patterns, and interactions with other sellers. The buyers that appeared as sellers as well were scraped in each round. After scraping each seller's API, an Excel dataset was created using 38 unique sellers. This dataset consisted of 18,157 rows and the following columns (Table 1):  
After gathering the data, the next step was the data-cleaning process. One of the main concerns that we faced during this stage was the presence of NaN (not a number) values in the buyer_id column. Once the dataset was successfully updated, the next step was to anonymize the data via the use of Python code. The code numbered the sellers and buyers according to their IDs. The final data that were used for analysis included numbered sellers and buyers. After anonymization, the next step was to pursue a detailed analysis so that suspicious activities could be identified. The key columns for the analysis were 'id', 'created_date','seller_id', 'buyer_rating', and 'buyer_id'. The 'buyer_rating' column was used in the initial analysis to detect the buyers who were also sellers. According to the data, if a buyer had a rating, then that buyer was also a seller. By using the'seller_id' and 'buyer_id' columns, the users could be identified even if the user had used different display names in the past. The 'created_date' column provided the dates on which the transactions took place. This was important for analysis when we identified transaction volumes over a certain period of time. The dataset was analyzed to find any linkages between the sellers and the buyers. Various tests and methods were used to find any suspicious activities, one being the interquartile range (IQR) method. This method plays an important role in detecting any outliers that have been missed during an analysis [26]. Obtaining outliers identified with