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“Human ingenuity will outsmart the rule engine, each and every time.” This is how Dr. Tan GeokLeng, CEO of AIDA Technologies began his presentation at the Singapore MAS FinTech Hackcelerator 2016. What followed was an insightful explanation on how Artificial Intelligence Driven Analytics (AiDA) can be leveraged to address the issues facing Traders’ Risk Management. Dr. Tan’s pitch further covered how AIDA technology can analyze traders’ communication patterns to spot the usage of unusual words that might signal suspicious activity. The innovative idea not only won AIDA the prestigious MAS Global FinTech Hackcelerator Award but also marked the beginning of AIDA’s exciting start-up journey in Nov 2016.
AIDA was founded by a team of PhD-level scientists, to assist businesses around the world that struggle to effectively utilize massive volumes of data from heterogeneous data sources, largely focusing on banks and insurance industries in the region. AIDA offers AI-driven analytics to address such challenges through the automation of knowledge work by augmenting human-expert abilities to make decisions from large amounts of heterogeneous information. The company brings proprietary technology innovations, which help extract-valuable information from both structured and unstructured data and develop both supervised/predictive and unsupervised machine learning solutions. The technology blends a multi-layer machine learning approach to achieve superior levels of predictive accuracy. AIDA also has sophisticated unsupervised machine learning technologies to reduce false positives when detecting outliers, a specialized ability that detects conceptual drift or gradual change in data, a deep learning engine for text analysis, and an expert ability to extract information from documents including tables and graphs.
Beyond precision or accuracy, businesses also need another functionality of using machine learning solutions effectively for interpretability and explainability of the derived results.
At AIDA, we have developed mechanisms to provide interpretability and explainability for the sophisticated machine learning approaches
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