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Activity1: Identify spelling mistakes, grammatical errors,…
Activity1: Identify spelling mistakes, grammatical errors, inconsistent use of US and UK spellings, incorrect or inconsistent punctuation, and inconsistent formatting in the text below. Highlight incorrect text with a reference number. On page 2, write the reference numbers and the correct text.
Big data and general insurance
Insurers are gradually becoming more technology savvy and big data is increasingly seen as essential for gaining important market and customer insights. The spectrum of take-up across the industry, however, are varied across lines of businesses. Within personal lines, notably private motor, big data and analytics are well embedded; by contrast, specialty insurance is at a nasent stage in the use of data and associated analytics.
Regardless of the line of business, more needs to done to demonstrate the true value of big data for insurers. Many currently see it as a solution to problmes, rather than as instrumental to the development of new insurance products that better align to customers’ ever changing needs.
For example, with the emergence of telematics, the common approach has been to use big-data to solve the motor insurance problem of improving profitability by risk selection and pricing. However, the real chalenge is for insurers to use the data across the whole business, including in the development-of-new-products, with a view to competing on product, price and service rather than just price
Steps for success in analytics
Data used correctly allows businesses to blend analytics and experience to help improve business processes and to develop new or existing bussiness opportunities. The critically point is to blend analytics and experience.
It is typical for organizations with a hierarchal structures to initiate a big data and analytics project by initially employing data experts to capture, assess and structure data into a pre-defined, usable format, working alongside IT experts to set up the technology platforms needed to host, manipulate and analyze the data. Following this, the statiscians can explore the data to find interesting trends and correlations within the data. However, to drive efficient results, it is critical to set the goal first.
Business-driven analytics
Different board members tend to have different objectives. These goals may be simple, clear and possibly measurable (although rarely directly measurable); however, they are not always directly applicable to an analytics project. For e.g., the drivers leading to improved profits could improved risk selection, optimization of pricing and product strategies, cost reductions or better claim management, all of which will have their own problems and a collection of different analytical paths to explore.
There are several key considerations when thinking thorough an analytical project:
1. Organisational goals,
2. Operationalization of analytical solutions,
3. Likelihood of success,
4. Complexity of analysis;
5. Availability of data.
All of these areas requires input from different areas of the buisness.
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Like most business problems, analytics should from part of an iterative journey, which will be a collaborative approach between the analysts and business owners. The business steakholders will needed to set the agenda and goals while the analysts should translate the business goal into the analytical approach needed to solve these problem.
