Singu̇PREDICT takes historical time series data to predict business outcomes.
Singu̇PREDICT can help companies to optimize existing processes, better understand customer behavior, identify unexpected opportunities, and anticipate problems before they happen. It allows you to go beyond simple statistical or regression analysis to forecast business metrics such as sales, inventory, budgets, outages, risk factors, et al. Singu̇PREDICT uses neural networks within a deep learning approach to facilitate the modeling, prediction and execution of various business functions, including detecting fraud, predicting claims, recommending products, projecting sales, avoiding outages, trading securities with higher returns, et al. Singu̇PREDICT allows management to make informed business decisions by understanding how the past influences the future.
Insurance companies store vast amounts of historical data. Data is expanding exponentially as connected devices provides telematics data. With Singu̇PREDICT insurers can build models to derive value from this data to identify fraud, prevent customer churn, detect incorrect payouts for insurance claims
Healthcare will benefit dramatically from predictive analytics. As there is a vast amount of healthcare data, including patient records, lab results, claims data, published research, at al. With Singu̇PREDICT hospitals and healthcare insurance providers can build models to predict patterns in healthcare operations to generate risk assessments for patients, predict hospital readmissions or missed appointments.
Most plants have numerous sensors that collect data about pressures, temperatures, levels of vibration in machines, et al. With Singu̇PREDICT, engineers can use the historical data and real-time data from these sensors to find anomalies to enable preventative maintenance or build models to predict future commodity prices
Retailers are eager to leverage AI to improve customer engagement, optimize pricing, manage inventory and detect fraud. With Singu̇PREDICT retailers can build models to combine historical data about buying behaviors with customer feedback to identify customers most likely to buy specific products or identify issues from customer complaints to improve operations