In the simplest terms risk analytics is the business of analyzing risk. Risk analytics can be described as a systematic approach for assessing and/or calculating risk. It involves the identification of uncertain undesired events, and the causes and consequences of these events. It includes risk assessment, risk characterization, risk management and development of risk related policies.

In industries where the inherent nature of the business involves risk - success and profitability depend on the ability to manage and minimize that risk. Banks, insurance companies, credit card companies and brokerage firms are examples where there exists an opportunity to apply risk analytics to improve profits and reduce costs. By employing models to analyze risk, firms can avoid huge losses through improved decision making enabling them to acquire and retain low risk customers.

The application of risk analytics may vary from industry to industry. For loan institutions and the credit card industry, risk analytics may involve models to predict default, bankruptcy, loss rates, and fraud detection. The insurance industry whether health, auto, life or home, can develop models that predict expected claim amount or probability for claims from past insurance data containing claims information.

These firms also tend to have an abundance of customer-level transaction data which can be utilized for assessing and predicting risk. Building predictive risk models using historical customer data to manage the overall risk of a portfolio or individual products across industry, product type, or region can help prevent losses that could arise if high risk customers are not detected early on.

Once such risk models have been developed, it becomes easier to price products for different customer segments based on their risk profiles. Risk analytics can help identify customers that are profitable, and evaluate the risk and associated losses of unprofitable customers.

At Inductis, we work with clients across various industries to perform risk analytics using their high volume of customer data to improve their bottom line. We leverage customer information combined with our expertise in data mining, analysis and predictive modeling to lead our clients to make better informed decisions and better manage their risk. The risk analytics models we build enable credit card clients to estimate the probability of default or bankruptcy, detect application or transaction fraud and estimate charge-off amounts. For finance companies, we deploy advanced models for estimating default and prepayment as well as charge-off amounts and collateral asset recovery values. We also develop statistical models for insurance claim incidence and amount. In addition we have developed optimal pricing models given customer risk characteristics.

Some of our Risk Management Services include:

• Credit Risk Analytics and Modeling and
• Fraud Risk Analytics and Modeling.