Machine learning has a proven track record of solving complex problems on large datasets including unstructured data. Increasing computing power and the explosion of available information permits the use of learning algorithms to explore new business ideas, as well as improve existing processes.
The usage of machine learning has expanded greatly from its specialised beginnings in artificial-intelligence research and digital-native tech companies.
The financial service industry is no exception, and is rapidly adopting these techniques in fields as diverse as trading strategies, risk assessment, fraud prevention or better understanding customer needs.
Generally speaking, we see four key ways in which machine learning can be applied:
to automate processing,
discover previously unknown relationships,
make informed predictions and
augment human access to information.
In many cases, however, identifying suitable data sources, reducing the number of fragmented data silos and achieving a better interconnection between different data pools might be sufficient as a first step. An intelligent combination of internal and external data often allows for major improvements.
How to get started? d-fine next helps you make the most out of your data by bringing together cutting-edge data science and deep domain knowledge.