Predictive Machine Learning Models rely on data. Data preparation tasks include domain-specific analysis and engineering. Our Models cleanse and transform data, select features and engineer them, and reduce the dimensionality of data. Also, our Models are trained and tuned with the resultant dataset. We build our Models keeping on mind that the application of data pre-processing within the relevant domain contexts is key to success of any PMLM.
DeepFusion automates the intake process even while improving the quality of data in the early stages thereby freeing up skilled knowledge workforce. It reduces cost to acquire new businesses and renew old ones. Underwriters can now analyse and prioritize submissions for effective utilization. This results in an increase in the number and quality of submissions. DeepFusion reduces turn-around times. Research suggests that our Vision API increases productivity by 60% while saving 70% of time.
