Analytics/Data Mining
Is your Claims Vendor Ossified or Agile?
Today companies must use business analytics to achieve actionable insight. Does your current claims support vendor understand the importance of cutting edge data mining? Data mining analytics identify and track emerging exposures, interface with underwriting, helps actuaries price for known and IBNR risks and enlightens loss control to protect a company's financial bottom line projection.
What is Data Mining?
The term "data-mining" refers to analytics designed to determine useful relationships in volumes of complex sets of data. Whether mining is utilized in the field for background prepping on a Plaintiff or reviewing loss runs for potential coding fraud, there is a new interest in methodologies. This process originally began in nonmanufacturing industries through: data storage, where company’s management systems collected, stored and managed information. Currently, insured’s, carriers, reinsurers, brokers, SIR entities, and other companies have new opportunities to build predictive models based on the use of available historical data.
Claim professionals, today are able to draw upon external data bases for field information, tables, loss runs and information in IT systems. This presents valuable claim loss information for Insurance and Reinsurance professionals. From emerging exposures to reserve projections, auditors, actuaries, loss control and underwriters have needed data for renewals, pricing, and binding of profitable business. This process enables decision makers to co-exist and share information as a team. It assists in identifying suitable and profitable transactions from initial reserve setting, emerging exposure impact, identifying the need to purchase reinsurance and developing pricing models.
Predictive analytics empowers professionals with the ability to provide business intelligence which will positively protect your company’s bottom line. Uses in the industry involve, actuarial science, financial services, insurance, reinsurance, various insured’s businesses from telecommunications, retail, travel, healthcare, pharmaceuticals, marketing, security, genetics, economics etc. Statistical capabilities help in pulling out information to predict future trends, probabilities and behavioral patterns. The key is to have vendor extrapolation, adaptability and knowledge in capturing informational trends.
How the Process Helps with the Competive Edge?
Data mining is an analytic process. It explores available data looking for consistent patterns or systematic relationships between variables. Once seen, the process then leads to validating findings and applying existing patterns to new data subsets. The goal of data mining is prediction and in the field to test the pattern. Accurate prediction in the insurance and reinsurance field is invaluable for identifying emerging exposures, predicting future losses, taking measures at controlling risks, reserving and premium pricing. Predictive data-mining methods make use of historical data. Analytics are utilized to derive a ROI when studied. Your IT information is defined and loss runs produced to derive this invaluable data. Established standards may help assist in quality information, a competitive advantage, development of quicker cycles, and a higher ROI. Data input should have functionalities with little or no data manipulation required by the end user including:
Integration – All types of data should be integrated preferably into one system for easy access.
Collection – Data should be housed in one snapshot view available for the entire organization at various levels-password protected, for as long as the business requires.
Preparation – Filtering capabilities for spreadsheet sorting and reporting to management.
Great companies have employees (including top executives) that get out and swim in the deep sea of technology.
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Flexible firms make it easy to communicate with them.
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Successful companies can scale fast. New working opportunities are tested, refined, and the use of immediate energy seizes the available work for efficient cost effective results.
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Progressive vendors utilize analytics to optimize: underwriting, sales, claims operations, pricing, facility utilization, litigation management, staffing, etc.
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Partnerships rely on expertise, efficiencies, and functionalities suited for solid business growth with empirical analysis and little data manipulation required by the end user/client.
Extant strives to understand your Company’s individual data mining and analytic needs in order to customize parameters suited to leverage your business to new heights.