In Hashstacs, we develop various blockchain solutions for Capital Markets. Within our products, we see a big potential to include features that utilize Machine Learning (ML) and Artificial Intelligence (AI) to create additional value for our clients.
Having the STACS blockchain technology as the cornerstone of our applications, Hashstacs is uniquely positioned to harness the synergy of Blockchain and ML, which gives us certain advantages to adopting Machine Learning.
Firstly, the data collected are the one single source of truth. These results in the data to be trusted and more accurate, to be fed into ML models, which improves the quality of our models and gives us confidence in the data that we use for financial use-cases.
Secondly, the data are in the highest-quality form. Data preparation and cleansing take up around 80% of Data Scientists’ time according to Forbes research and is often a big hurdle in launching Machine Learning ideas into actual development efforts. We are able to move fast in the development of ML, focusing our time on more important aspects of data exploration, transformation, and model building and tuning.
In this article, we would like to share our experiences in setting up a real-time Machine Learning workflow with the STACS Blockchain and AWS SageMaker (a fully managed ML solution by AWS) where we implement classification to enable fraud detection.
For more details, please read the technical report here
STACS is a Singapore fintech development company focusing on the digital transformation of the financial industry. Today, our clients and partners such as stock exchanges, banks, asset management firms etc are using our proprietary Securities Trading Asset Clearing and Settlement (STACS) Blockchain for various use cases while working together with their ecosystem participants to enjoy efficiency savings in operations and to create new revenue streams.