Before we start talking about our services you may want to know what predictive analytics or predictive modeling is in the first place.
In essence, predictive analytics is a goal-driven analysis of large data sets that develops mathematical models to improve efficiency with regard to specific business objectives. This analysis is a knowledge discovery process that offers greater insight into an organization’s data and transforms data into information and information into knowledge. Predictive analytics offers more effective problem conceptualization, a higher degree of accuracy and consistent results.
Predictive analytic models often build on data warehousing and business intelligence solutions that many modern organizations have already implemented. By applying sophisticated analytical techniques, they further enhance and aid the human decision process. As such, predictive analytics models can be leveraged for making decisions about allocating organizational resources in a particular functional area to generate significant financial benefit.
The business drivers of insurance market consolidation, growing competition and increased government regulation force insurance companies to get more efficient. These business drivers mean technology plays an increasingly important role, as companies with a broader portfolio of insurance products seek to leverage their data and market knowledge to create a competitive advantage by making informed decisions faster which leads to better pricing, new or enhanced distribution channels, differentiated products and services, operational cost reductions and better risk management.
- Risk Modeling and Risk Management. Proper risk assessment is essential to all insurers. Accurately predicting risk is a significant competitive advantage and profit driver. Predictive models are capable of measuring the level of propensity to claim via metrics including mean claim amount, claim frequency, and loss ratio.
- Reinsurance. Predictive models may guide and aid actuary’s decisions regarding the reinsurance level for the book of business based on the historical claim data in order to maximize the returns for the risk acceptable to the insurance company.
- Profitability Analysis. Based on historical profitability analysts can develop predictive models for estimating the marketability of new products.
- Premium Analysis. Predictive models may assist in tracking premium performance by a product or product line, geographic region, an agency or particular agent, and by a branch office.
- Loss Analysis. Predictive models may assist in finding irregularities in pricing and help to identify inaccurate initial risk estimates.
- Claims analysis. Efficient claim management is essential to maintaining customer satisfaction. Predictive models can optimize the workflow for claim processing. Additionally, they may detect subtle business trends in claims management, which can optimize reserve management, leading to lower risk and more available funds for investment.
- Fraud detection. Predictive models are typically applied to claims fraud detection. They are capable of spotting patterns of fraudulent behavior. In cases of health insurance fraud, predictive analysis can analyze behavior and detect practitioners who have been consistently prescribing expensive medicines and tests in cases where they are not required.
- Claims estimation. Predictive models can assist in forecasting expected claim amounts across geographies and customer segments.
- Customer Profitability. Since acquisition of new customers is costly, it is essential to focus on the existing ones and on increasing their profitability. Predictive models assist in identifying the most profitable customers. They tie the risk profile of the customers to their products, which often can explain why a good potential customer is not profitable and how to increase his/her profitability.
- Customer Lifetime Value. Frequently organizations are interested in estimating a customer’s lifetime value (LTV) which assists in answering questions about future product offerings.
- Customer Segmentation. Segmentation divides customers into groups that exhibit common characteristics. This can save a lot of marketing effort which otherwise may turn out to be very ineffective and result in a loss of customer satisfaction or wasted budget.
- Attrition Analysis. Acquisition of new customers is especially costly in the insurance business. It is important to understand attrition behavior and to be able to act accordingly.
- Affinity Analysis. Affinity is also known as market-basket analysis. Certain products show affinity towards each other and therefore are likely to be purchased together. Predictive models help identify these product combinations.
- Cross selling. Predictive models can identify the best target groups of customers and products for cross selling. Those products can be offered to customers at the moment of the next contact.
Predictive analytics may sound complex and overwhelming. It is our foremost goal to make it simpler and more accessible to businesses. Simpler to understand, evaluate and use. Many organizations fail to understand that predictive analytics projects are not technology projects even though they extensively use technology. They are business projects. As such when evaluated, they should adhere to the most important business measurement: return-on-investment (ROI). We do our absolute best to work with our clients to translate the business objectives into analytical model objectives to maximize business goals.
Apart from the above, we have broad expertise in scientific application of predictive analytics including data mining, machine learning, knowledge discovery, expertise in data warehousing and business intelligence and a successful track record in software engineering and application development in business settings.