Now, let's turn to the insurance industry. Now, the insurance industry includes many different types of insurance businesses: life insurance, health and medical, property, specialty insurance for boats and motorcycles, and so forth, reinsurance, mortgage insurance, and title insurance. And the types of analytic solutions are quite varied. From actuarial analysis or underwriting to property risks such as fire, to property value claims processing, fraud detection, loss ratio improvement, premium pricing, and investments. Santam insurance sought a personalized claim management platform that could also optimize resources and time. They created an advanced predictive analytic solution platform that categorized claims under different risk categories. This facilitated investigation and mediation of each claim, and at the same time, optimized resources and efforts. As a result, 50% of claims are accelerated through improved categorization. And 15% or 54,000 claims can now be processed in less than an hour, representing a 95% reduction in claims processing time. One of Peru's largest insurance companies Rimac insurance needed to process claims faster. So, they turned to IBM and IBM Watson's Content Analytics module and deployed this against 50 million custom policies consisting of 28,000 combinations of policy terms. You see, Rimac insurance customizes almost every policy, making them very difficult to process claims against. This enabled them to process 15 million claims in 30 days to codify all policy business rules. Claims are now processed 25 times faster, and Rimac can now process 100% of their claims, up from 1% previously. We often talk about the hidden benefits of dark data; this is a great example of that. A small insure in the southeast United States called Infinity Insurance needed to save and make money by reducing fraudulent auto insurance claims. They realized they were sitting on a gold mine of historical claims data. So, they use predictive analytic against this data, and text mined the adjuster report for hidden clues like missing facts, inconsistencies, and change stories. This improved success rate in pursuing fraudulent claims from 50% up to 88% and claim investigation time was reduced by 95%. They also use these models and bake them to marketing systems to prevent marketing to individuals with a high propensity for claim fraud. A Russian insurer needed a solution to better segment its target customers and improve upon transaction rates. Google Analytics was deployed to create insights into user behavior. It also optimized its website funnels and adjusted policy pricing for various segments. Features like tracking, custom dimensions, and e-commerce helped to identify which users were attracted by ads, the policy options they wanted, and the policy prices. As a result, they were able to double transaction rates with revenue trends on the rise, and average order size remaining untouched. This solution enabled them to segment their customers into thousands of precise groups. And reach each group with the right message and a data-driven policy price.