Increasingly, the health care industry is handling massive amounts of data coming from many different sources. This data typically relates to business, marketing, and clinical decisions. Our deep expertise in data science enables us to address several of these problems.
Data-driven patient clustering can identify patients who are likely to re-admit, respond to treatment, make fraudulent claims, or request repeat treatment. Our solutions provide these insights to help identify gaps in care, minimize unnecessary treatment, optimize resource allocation, and improve patient outcomes.
Re-admission Risk Forecasting
The ability to identifying patients who are likely to get readmitted to a hospital because of a medical condition has a significant effect on various stake holders -- the patients, the healthcare providers, and the insurance providers. This is more so true with the Affordable Care Act trying to reduce re-admissions. Our predictive models use a patient's available medial history to predict likelihood of readmission, helping healthcare providers take precautionary measures, thus cutting down readmission rates.
Today’s volatile markets demand that financial institutions have a better understanding of their risk-return while adhering to complex rules and regulations and gaining insight into consumer behavior. We provide the necessary insight to address these issues among others.
Monitoring and detecting behaviors and patterns that evolve over time can greatly increase the chances of highlighting sophisticated fraud activity. Self-learning algorithms providing enterprise-wide fraud coverage can increase customers’ trust and reduce reputational risk.
Probability of Default and Loss Given Default
In order to comply with the massive amount of industry rules (IAS/IFRS) and regulations (such as Basel III & Dodd-Frank), financial institutions are required to effectively forecast credit losses as part of the capital management process. Our extensive predictive capabilities enable institutions to effectively forecast credit losses based on multiple baseline and adverse scenarios.
Telecom companies today have evolved significantly beyond the the traditional voice services, providing comprehensive fixed and mobile, voice, data, and TV services. Navigating this integrated landscape and making the best use of the data collected requires a customized framework of analytic solutions. With our expertise in data science we are able to provide you with the best-in-class approaches to enable scientific decision making.
Churn models are used to identify customers with a high risk of dropping out (or attriting). This information can be used to pre-emptively adopt individual-specific retention measures. There is no ‘one-model fits all’ solution to this problem, and the approach has to be tailored to the data at hand. Modeling frameworks such as Neural Networks, Logistic Regressions, Bayesian Classifiers, Survival Models etc. can be used to compute churn propensity.
Cross-Sell / Up-Sell Models
Cross-sell models are used to identify customers who have a need for other services/products offered by the company (in addition to their current engagement). Similarly, up-sell models are used to identify opportunities to upgrade a customer from their existing service plan to a higher service plan. This information can be leveraged by Sales teams to better identify target customers, and marketing campaigns to improve their ROI.