Projects
Data Warehousing and Analytics for Public Health Insurance
The project is about providing a next generation and state-of-the-art Analytical Ecosystem as required at Dr.NTRVST for addressing the two fold objective of providing descriptive and predictive analytics to the stakeholders at Dr.NTRVST. The proposed Analytical system should help users move from the existing operational reports available in the Dr.NTRVST Transactional system that are static in nature and create a Self-Service Analytical Environment where users can slice and dice the Dr.NTRVST Data and gain insights into the data by applying various Statistical and Data Mining techniques. The Analytical system should seek to automate various activities that are currently done manually while trying to perform analysis on the Dr.NTRVST data and also bring-in the best practices to the Analytical System. The end-result of identifying exceptions and hidden trends in the Dr.NTRVST data should be achieved with minimal manual interventions and the overall decision making and planning process needs to be made more efficient.
Modeling Loan Default Risk from Subjective Expert Experience
Our client, a microfinance company that disburses home loans to low-income individuals working in the unorganized sector, required a system to rate the risk of such loans. The risk of default is high because the applicants’ creditworthiness is not documentarily established, and the loan duration is for a considerable period of time. Sales personnel gather a multitude of information, either directly from the applicant or from different sources. This information becomes the primary data source to analyze and establish whether the loan will be approved, based on an estimation of the probability of successful repayment by the applicant. The principles of Aashiyan have decades of experience in underwriting microfinance loans and are able to make considered decisions based on the available data. However such expertise is mostly informal and subjective and cannot be easily translated into policy or automated risk-scoring. Each evaluation is time-consuming and thus makes it expensive for the organization to scale. Additionally, in the absence of a formal mathematical model, new information is harder to incorporate, and the evaluations suffer from a lack of flexibility. Our system formalizes the subjective expertise of the principals and builds a mathematical structure for risk scoring. The model then was integrated into the client’s data collection system and was able to instantly provide risk-scores as the information was entered. Finally, the model is made adaptable to information about loan outcomes and is able to learn from experience.
Products
Scalable Platform to Provide Actionable Information for Health Insurance Decisions
We built a product that effectively integrates clinical, claims, psychographic, and physiologic data from a variety of sources into one platform and presents a holistic patient view through an intuitive dashboard that supports physician decision-making and care team action, improving financial, quality, and health outcomes. The product included a highly effective clinical data model and predictive algorithms that exceed the industry norms for predictive accuracy. Traditionally, health plans have used actuarial models that determine risk and future costs at the population level. Our platform used leading-edge machine learning techniques to create a dynamic risk score for each member, which improves its precision and personalization as well as enables health plans to act on the insights to prevent health deterioration and future costs.