Machine Learning

TCB Analytics incorporates a proven methodology in implementations of machine learning and artificial intelligence solutions. This process includes a progression of analysis activities for initial data discovery, exploration and profiling. Afterwards, supervised and unsupervised machine learning approaches are leveraged to uncover data anomalies and trends, develop models and tune models for increased accuracy and performance.

MODEL DEVELOPMENT METHODOLOGY

Our machine learning algorithm development methodology is based on industry leading best practices, incorporating lessons learned from our team of statisticians and data scientists. After the initial analysis and investigation of a use case, a resolution hypothesis is developed for testing. This hypothesis is transformed into an initial algorithm, which is tested and validated with customer data. Insights from the machine learning algorithms are then embedded into dashboards to provide a feedback loop for model performance tuning and improvements.

REPEATABLE MODELING FRAMEWORKS

Based on common use cases for machine learning algorithms, our team offers a collection of reusable frameworks to help speed up implementation time. Examples of these frameworks include anomaly detection, transcription error identification and duplicate record detection. Our team customizes the frameworks based on your unique data and environment.

ARTIFICIAL INTELLIGENCE

Once machine learning models are developed, our team incorporates those findings into artificial intelligence solutions. By applying the insights extracted from the data to an AI application, the process of enabling automation or guided workflow can begin. These solutions also include a process to continue learning as additional live data is analyzed, so the underlying models continue to improve.