Fintech

Fintech is financial technology: finance + technology. It is to describe new tech that seeks to improve and automate the delivery and use of financial services.

Drivers of fintech include extremely rapid growth in data (including quantity, types, sources and quality) and technological advances enabling the capture and extraction of information from it. In the investment industry, fintech development areas include analysis of large datasets, analytical tools, automated trading, automated advice, and financial record keeping.

Big Data

The three Vs are fundamental to Big Data:

• Volume: huge amount of data.
• Velocity: the speed at which data must be stored and analyzed.
• Variety: data are gathered from various sources in a variety of formats (e.g. structured data, unstructured data).

The non-traditional sources include data generated by individuals, business processes and sensors, while the traditional sources are financial markets, businesses (e.g. financials, credit card purchases) and governments (e.g. employment and payroll data).

Artificial Intelligence and Machine Learning

As data sets get larger and more complex, investors need to use sophisticated data analysis techniques. The tools used for these tasks include:

• Artificial intelligence: Artificial intelligence computer systems are capable of performing tasks that traditionally required human intelligence at levels comparable to those of human beings.
• Machine learning: ML computer systems can "learn" how to complete tasks and improve their performance over time. It involves training itself, validating dataset and predicting outcomes.

Main types of ML include:

• Supervised learning: Using historical data points as training samples to infer a rule or equation capable of predicting future outcomes.
• Unsupervised learning: Aims to identify the common drivers behind the data points by identifying relationships between input variables.
• Deep learning: Analyzes data via multiple iterations, or "layers of learning" - starting by learning simple concepts, and then combines these to formulate more complex concepts. This can be accomplished by passing the data through multiple layers of non-linear processing units in a manner similar to neutrons within the human brain.