wikifx collects real-time data portals of regulatory agencies of 42 countries around the world, updates the status of 150,000 licenses daily, and achieves a verification accuracy rate of 99.3%, enabling users to evade 78% of scam risks. For instance, in 2023, a cloned website falsified an ASIC license (with one digit difference in the registration number). wikifx used OCR technology to detect a 0.3-pixel discrepancy in the fonts’ spacing and a ±8% difference in RGB values of colors, and alerted the risk within 72 hours, sparing users from a loss of over 230 million US dollars. The company’s own “Liquidity Health Index” measures the depth of the order book (the low of $50,000 versus the industry standard of $500,000) and the probability of slippage (the high of 38% versus the industry average of 9.5%). In the 2021 Turkish Rila crash, the users reduced losses by 63% due to the warning of this indicator (the depth was below $1 million).
The user rating system collects 6.8 million actual feedbackings and removes 23% of fraudulent comments through AI semantic analysis (i.e., IP concentration exceeding 75% or text repetition rate 60%). In 2022, a broker hired Internet trolls to enhance ratings (from 2.8 to 4.5). wikifx combined the MT4 logs provided by the users (with an actual slippage rate of 28% from 500 orders and a max of 53 points) to restore the actual rating to 3.1 and allowed a compensation of 18,000 US dollars. The website also features a transaction cost comparison tool. For instance, an ECN account has a commission of $1.5 per lot, but unknown costs (e.g., overhead of overnight interest 0.8%) increase the average annual cost by 37%. Clients can save $127,000 annually through wikifx filtering (calculated on the principal $1 million).
The risk early warning model combines 173 dynamic indicators and tracks the global regulatory dynamics at 30-minute intervals. In 2023, 326 risk events were monitored, 93% of which were exposed prior to the news. For example, 6.5 hours ahead of the Canadian IIROC canceling the license of a particular platform, wikifx alerted via an API status code anomaly (HTTP 503 error rate increased by 580%), and the recovery rate of user funds was 92%. The platform also uses machine learning to predict the probability of brokers running away (with a success rate of 89%). For instance, three months before the closure of a certain Australian platform, the website traffic dropped by 87% week-by-week, the withdrawal delay ratio escalated from 4% to 37%, the wikifx risk index was adjusted from 62 to 89, and users withdrew over 12 million US dollars of funds prematurely.
Technical authentication methods include blockchain proof storage (tampering risk <10^-9%) and SSL certificate authentication. In 2021, a platform forged bank guarantees (claiming to have a registered capital of 20 million euros). wikifx discovered three editing records through PDF metadata and verified that the SWIFT code was false (the actual amount received was 4.8 million euros), which caused the risk score dropped to 12 out of 100. The platform also discovered that the SSL validity period of a “high-yield” scam website was only 7 days (normal is 1 year), the server IP was 8,000 kilometers away from the declared location, and it was blacklisted within 24 hours, preventing the deposit of over 9 million US dollars.
The educational module of the resource offers 1,200 hours of free lessons, such as technical analysis (enhancing the winning rate of MACD strategies by 27%) and risk control (reducing the drawdown rate by 35% through position management). As per a 2023 University of Cambridge research, wikifx users’ average annual rate of return is 15.8% (the industry average is 9.2%), and the decision error rate has decreased by 42%. For instance, a novice tested the survival rate of strategies under abnormal market conditions (a 3% fluctuation of the euro) through the “Regulatory Sandbox Simulator” of the platform, and the actual trading return increased by 89% in a quarter. Such functions make wikifx the research center for 89% of traders, avoiding losses of 180 million US dollars annually and enhancing decision-making efficiency by 3.8 times.