In this paper, we propose a methodology for the analysis of questionnaire data along with its application on discovering insights from investor data motivated by a day trading competition. The questionnaire includes categorical questions, which are reduced to binary questions, 'yes' or 'no'. The methodology reduces dimensionality by grouping questions and participants with similar responses using clustering analysis. Rule discovery was performed by using a conversion rate metric. Innovative visual representations were proposed to validate the cluster analysis and the relation discovery between questions. When crossing with financial data, additional insights were revealed related to the recognized clusters.

Artificial Intelligence has been consistently on the rise over the past two decades. Nowhere else has its impact been felt the most than in the realms of cryptocurrencies and digital assets. Just as Charles Darwin's Theory of Evolution states about the path to the evolution of life, AI has also evolved similarly since its inception, though at an exponential pace. The natural progression of AI has now entered into algorithms that are written with the ability to farm for successful cryptocurrencies such as Binance Coin (also known as BNB) with an average trading price of $600. For example, BNBXMAS, a Dapp launching this Christmas, is written on the Binance Smart Chain.

A high-level goal of many AI projects is to address the ethical implications of algorithms along the lines of fairness and discrimination. It is a known fact that algorithms can facilitate illegal discrimination. For example, it may not surprise that each investor wants to put more capital in loans with a high return of investment and low risk. A modern idea is to use a machine learning model to decide, based on the sliver of known information about the outcome of past loans, which future loan requests give the largest chance of the borrower fully paying it back while achieving the best trade-off with high returns (high-interest rate). There's one problem: the model is trained on historical data, and poor uneducated people, often racial minorities or people with less working experience have a historical trend of being more likely to succumb to loan charge-off than the general population.

Digitalisation, alternative data sets and enterprise data analytics will be critical in decision making for asset managers seeking high returns in the coming year, according to an outlook for the alternative asset management sector by Indus Valley Partners. Indus Valley, a provider of technology solutions for alternative asset managers, said in a statement that traditional and alternative asset managers will face a number of hurdles in 2018. With pressure growing from investors and regulators, many asset managers will seek new ways to earn high returns through quantitative strategies, alternative data sets and machine learning, it said. Artificial Intelligence and machine learning will become critical in the search for high returns. Blockchain technology will be used in pilot programs to achieve post-trade operational efficiency and for better regulatory compliance, according to the outlook.