Big Data-based Risk Analytics will give you concrete data-driven answers to your most important questions about managing risk.
CREDIT RISK ANALYSIS
How can I increase my loan approval rates and minimize the number of defaults at the same time?
Traditional credit scoring mechanisms take into account a limited number of factors and a limited number of data points. On the other hand, a big data approach uses machine learning to assess the creditworthiness of a loan applicant. It takes into account your offline data as well as data from various online sources such as social media and websites. It takes into account structured as well as unstructured data sources like emails and social media activity. Big data analytics makes a far more accurate estimation of the probability of default – allowing you to increase your loan approval rates, as well as lower your default rates.
INSURANCE RISK ANALYSIS
How can I determine premiums more accurately for an unique customer?
No two customers are equally risky. So why should premiums be the same?
In order to accurately assess the risk of an insurance application and determine the appropriate premium, underwriters need to make the most of all the relevant data available. However, the huge volumes of complex data make it challenging for underwriters to compile all the data and and take profitable decisions. Big Data Analytics makes it possible to analyse huge volumes of data and make far more accurate estimations of the level of risk – and determine the necessary premiums.
Risk Analytics Techniques
Identify patterns of fraudulent activity by monitoring several qualitative and quantitative metrics
Arrive at far more comprehensive credit scores by analyzing several sources of qualitative and quantitative data