{"id":1133,"date":"2019-02-15T10:31:59","date_gmt":"2019-02-15T07:31:59","guid":{"rendered":"http:\/\/kusuaks7\/?p=738"},"modified":"2021-05-11T14:05:44","modified_gmt":"2021-05-11T14:05:44","slug":"predictive-analytics-impact-on-the-insurance-industry","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/fintech\/predictive-analytics-impact-on-the-insurance-industry\/","title":{"rendered":"Predictive Analytics Impact on the Insurance Industry"},"content":{"rendered":"\n

Ready to learn Insurance Analytics?\u00a0Browse courses<\/a>\u00a0like\u00a0Insurance Analytics Training<\/a> developed by industry thought leaders and Experfy in Harvard Innovation Lab.<\/em><\/strong><\/p><\/blockquote>\n\n\n\n

Predictive analytics is playing an ever-increasing role in virtually all areas of the insurance industry. Throughout the value chain of marketing, sales, underwriting, pricing and claims, predictive analytics are assisting more and more companies in better risk assessment, maximizing the return on their investments, improving customer service and increasing overall efficiencies. Its impact in the insurance industry is seismic, although not necessarily new.<\/p>\n\n\n\n

A case could be made that predictive analytics got its start in insurance when Edmond Halley, of Halley\u2019s Comet fame, published the \u201cLife Table and its Uses\u201d<\/a> in the 17th century. Actuarial tables are still used, of course, but are a just a small part of predictive analytics now and in the future.<\/p>\n\n\n\n

Overcoming Challenges: Predictive Analytics in Insurance<\/h2>\n\n\n\n

Many early concerns about predictive analytics, mainly those associated with the Fair Credit Reporting Act and involving consumer privacy issues, have been overcome by the consumer\u2019s own willingness to share such personal information to receive greater value in the form of discounts or credits for better risk profiles. A paper on predictive analytics, published by\u00a0Deloitte Consulting LLP in 2010<\/a>, recognized these issues even back then and cautioned that predictive analytics must consider \u201cregulatory, ethical, and cultural concerns\u201d.<\/p>\n\n\n\n

Another challenge for the insurance sector is its conservative nature. Analytics are broadly used but are primarily descriptive or historical. Most insurers want high confidence in their predictive analytics. Today\u2019s models supported by new predictive tools and database technologies combined with Big Data offer far superior, secure and solid behavioral information than has been previously available.<\/p>\n\n\n\n

Trends and Benefits of Predictive Analytics in the Insurance Sector<\/h2>\n\n\n\n

So, what are the \u201cbrass tacks\u201d benefits of predictive analytics for the insurance industry?<\/p>\n\n\n\n

Predictive analytics is widely accepted in marketing for customer acquisition, retention and cross-sell for good reason. Customers with more than one policy typically have a higher retention rate. Also, a policy often does not become profitable for 2-3 years due to sales acquisition costs. Predictive analytics help target the right customers and to predict those who may leave or churn.<\/p>\n\n\n\n

In underwriting, predictive analytics enable better risk assessment and classification which leads to better pricing. Pricing analytics can be the \u201ctipping point\u2019 for consumers in choosing insurers and products. They also lead to better profitability for insurers as they target desirable customer segments and support real time, dynamic pricing.<\/p>\n\n\n\n

Predictive analytics is also an important defense against insurance claims fraud. According to the\u00a0Coalition Against Insurance Fraud<\/a>, insurance fraud is one of America\u2019s largest crimes, with at least $80 billion in fraudulent losses each year. These losses don\u2019t just affect insurers, but policyholders as well since ultimately premiums must reflect all losses. Predictive analytics can better detect and \u201cflag\u201d potential fraudulent and duplicate claims.<\/p>\n\n\n\n

From a finance perspective, by combining past losses with predictive analytics on future losses, the ability to manage loss reserves is also greatly enhanced. Funds can be more efficiently allocated, improving margins through improved cash management.<\/p>\n\n\n\n

SAP\u2019s Customer Retention and Leonardo<\/h2>\n\n\n\n

One of the greatest challenges in predictive analytics is scarce data science resources. SAP can help insurers jump start or refine their predictive analytics and increase the effectiveness of their existing analytics teams with SAP Leonardo.<\/p>\n\n\n\n

SAP offers a Big Data solution to help predict customer churn based on behavior in reaction to events and other indicators. Often insurers\u2019 current approaches have data limitations, aren\u2019t customer-centric, and offer little insight into customer churn. SAP Customer Retention offers features like the ability to mine data, predict customers at risk of churning, create customer \u201cfingerprints\u201d, and better visualize the customer journey. It can monitor early indications of churn and take steps to prevent it. It also integrates with other SAP Products for full digital transformation.<\/p>\n\n\n\n

SAP\u2019s Leonardo opens new opportunities in the insurance sector as well. Big Data can be leveraged in the insurance business to take full advantage of the digital economy, including the use of powerful and valuable predictive analytics. Like Halley\u2019s game-changing \u201cLife Tables\u201d of the 17th century, SAP is here today to help you take the next step into the promising analytics future of the insurance industry.<\/p>\n\n\n\n

For more information on predictive analytics, please visit\u00a0SAP Leonardo<\/a>: Enabling organizations to store, manage, analyze data, and gain valuable insights.<\/p>\n\n\n\n

<\/p>\n\n\n\n

Originally posted at SAP<\/a>\u00a0Blogs<\/p>\n","protected":false},"excerpt":{"rendered":"

Predictive analytics is widely accepted in marketing for customer acquisition, retention and cross-sell for good reason. Customers with more than one policy typically have a higher retention rate. Also, a policy often does not become profitable for 2-3 years due to sales acquisition costs. Predictive analytics help target the right customers and to predict those who may leave or churn.<\/p>\n","protected":false},"author":52,"featured_media":4294,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[192],"tags":[495,126,724,723,725,295],"ppma_author":[2522],"class_list":["post-1133","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-fintech","tag-benefits","tag-insurance","tag-insurance-analytics-training","tag-insurance-industry","tag-insurance-sector","tag-trends"],"authors":[{"term_id":2522,"user_id":52,"is_guest":0,"slug":"patricia-saporito","display_name":"Patricia Saporito","avatar_url":"https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2020\/04\/medium_79e5270a-41bb-4612-8494-44b538030090-150x150.png","user_url":"","last_name":"Saporito","first_name":"Patricia","job_title":"","description":"Pat Saporito, a Data and Analytics Thought Leader, is a senior director, in SAP Global Center of Excellence for Analytics, an Advisory Board Member, BI & Analytics Masters Program\u00a0at\u00a0Stevens Institute of Technology<\/a>, and Mentor\u00a0at\u00a0Global Insurance Accelerator<\/a>. She has over 25 yrs. experience in information management, data warehousing and analytics and is a frequent blogger, writer, and speaker on these topics. She is a recognized expert in the insurance industry and has worked with many other industries as well. She is author of the book Applied Insurance Analytics<\/a> and a regular contributor to Best\u2019s Review magazine. She has been a faculty member of the International Institute for Analytics (IIA), co-founded by Tom Davenport."}],"_links":{"self":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/1133","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/users\/52"}],"replies":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/comments?post=1133"}],"version-history":[{"count":2,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/1133\/revisions"}],"predecessor-version":[{"id":10210,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/1133\/revisions\/10210"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media\/4294"}],"wp:attachment":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media?parent=1133"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/categories?post=1133"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/tags?post=1133"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/ppma_author?post=1133"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}