Most of the revolutionary successes of Artificial Intelligence are made possible through supervised learning. It is called supervised learning because the process of an algorithm learning from the training dataset can be thought of as a teacher supervising the learning process. The correct answers are known, the algorithm iteratively makes predictions on the training data, and is corrected by the teacher. Learning stops when the algorithm achieves an acceptable level of performance. The input data for supervised learning, also referred to as training data generally needs to be created by manual effort. Even if a categorization or output is available, usually, manual effort is still necessary.
We have developed several strategies to accelerate the data annotation task while still respecting the unique constraints of the financial industry.
In contrast to traditional models, machine learning models improve significantly with more data and do not plateau. Consequently, adding more labeled data is in most cases even more important than improving the actual prediction model.
Unfortunately, training data generally needs to be created by manual effort. Even if a categorization or output is available, usually, manual effort is still necessary. The data annotation process needs to be planned carefully keeping several factors in mind. Most importantly the factors data confidentiality and domain knowledge requirements have to be taken into account.
At cognaize, we have developed and implemented several strategies to maximize high quality annotated data with minimal resources.
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