Self-learning algorithms that results in pattern recognition and predictive modeling
The core of First Union Capital's machine learning consists of self-learning algorithms that evolve by continuously improving at their assigned task. We have structured and fed proper data to allow these algorithms to produce results in the contexts of pattern recognition and predictive modeling.
Machine-learning algorithms become more effective as the size of training datasets grow. Therefore, we benefit twice when combining big data with machine learning: the algorithms help us keep up with the continuous influx of data and hidden patterns while the volume and variety of the same data feeds the algorithms and helps them grow.