
Risk Management
Predictive Analytics in Car InsuranceThe changing nature of the insurance industry has brought on new risks due to disasters, unforeseen events, and regulatory compliance. As a result, risk management becomes more important to organizations. In particular, risk modelling predicts the maximum potential loss due to a catastrophic event. With big data analytics, insurers can factor in various types of data, such as historical data and policy conditions. Likewise, underwriters can price catastrophe policies based on granular factors rather than by city and state. A big data driven solution allows pricing models to be updated in real time rather than a few times a year.
Our goal to help financial organizations attain competitive advantages in terms of cost, customer relationships (customer satisfaction), and market leadership in a cost effective way.
Our current predictive analysis platform integrates data mining tools with the rest of a company’s IT infrastructure. Supporting features like data cleansing, data modelling, vectorization and predictive elements dedicated to the insurance risk management, simplify the integration and deployment of big data analytics into business processes of insurance companies.
PredMine provides predictive analysis tools based on data mining and machine learning techniques, dedicated to the financial and insurance sector. It enables a company’s employees with low expertise in data analytics to apply different machine learning algorithms on huge data sets. Thus, it lowers the bar for insurance companies seeking predictive analytics capabilities with Big Data.
In traditional approaches, prediction is based on factors selected by scientists according to their experience. Users actually define decision variables. The PredMine platform actively assists the process of highlighting the factors that cause the events (e.g. car accidents) and discovering patterns between certain characteristics and attributes of an entity (driver, car). Our machine learning approach can be very accurate in detecting the relationship between dependent (e.g. claims) and independent variables (driver’s age, type of the car, region, etc.).