B2Metric is an automated machine learning (Auto-ML) platform solution that provides Enterprise-AI and continuous business learning solutions for the Insurance, Finance, Retail, Telecom, and Automotive industries. Becoming a data-driven company is such an easy task for the marketing and analytics teams.
The product is an automated machine learning based smart healthcare reimbursement platform.
By extracting this predictable health policy model adaptively with up-to-date data and microsegmentation, the product will compare the segments formed at the time of T with the segments formed at the time of T-1, and formulate whether the overlaps of the same segments diverge over time, and we will catch the anomalies when the reimbursement support, price and frequency differ.
B2Metric will facilitate the follow-up of this problem by KPI’ing the risks of factors such as treatment equipment, treatment costs, pricing enriched with supply / logistics data, regional factors, expansion of health services, which form the content of reimbursement requests from health practitioners, in terms of price sustainability, with prescriptive and predictive methods. And by making a historical model of these risks, we will predict potential risk thresholds in the future, enabling insurance policies with low risk premiums and ensuring profit sustainability.
B2Metric micro segmentation solution, it will be assigned a normalised abnormal score between 0-1 to subsets that are non-linearly separated by gradient boosted methods, according to whether each transaction is close to neighbourhood transactions in the variable plane or not. Thus, when a new transaction comes from the same source, we will be able to determine whether there are similar pricing in its proximity space (to be found with the help of the cosine distance between the segment vector and the segment centre vector), and if there is, the similarity rate to the previous pricing. Segmentation as a method will be realised by combining the Isolation Forest approach with gradient-boosted and cluster centroids.
Successful adaptive micro segmentation will eliminate the factor of non-linearly, multi-regression and discriminant analysis-like methods, which are highly influenced by the general characteristics of the data and historical data, to model the current data, to eliminate the factor of not being able to add a learned weighted average, to improve the pricing and risk perspective with data localization and current adaptation, to time will be able to perform risk and outlier analysis in line with the region and trend.
Price approximation will be made on the features engineered in time-dependent procedure used in microsegmentation of extreme gradient boosting libraries such as LightGBM and XGBoost. In addition, confidence intervals obtained by probabilistic forecasting methods will be modelled over dependent variables and given as input to the mentioned models. DeepAR will be used as the probabilistic forecasting method. For the variable derivation, sktime, tsfresh will be used.
B2Metric AutoML solution set up an ML pipeline for the usage of marketing teams, data scientists, and data executives. B2Metric Machine Learning Studio brings end-to-end solutions and meets these main data science situations: data preparation, data wrangling, feature engineering, selection of algorithms, training and parameter tuning, then understandable insights with reporting at clean dashboards.
Becoming a data-driven company is such an easy task for the marketing and analytics teams. B2Metric AutoML solution set up an ML pipeline for usage of marketing teams, data scientists and data executives. B2Metric Machine Learning Studio brings end-to-end solutions and meets these main data science situations: data preparation, data wrangling, feature engineering, selection of algorithms, training and parameter tuning, then understandable insights with reporting at clean dashboards.
Usage of Standards for data interoperability:
SOC 2
GDPR
