Predictive Analytics in Insurance: Types, Tools, and the Future
- What Is Predictive Analytics?
- Predictive Analytics – Life Insurance Industry
- Predictive Analytics – Health Insurance Industry
- Predictive Analytics Tools for the Insurance Industry
- The Future of Predictive Analytics Use in the Insurance Industry
- Delivering the Benefits of Big Data to the Insurance Industry
What Is Predictive Analytics?
Predictive Analytics Definition for the Insurance Industry
How Insurance Companies Assess Risk, Set Rates
How Predictive Analytics Improves Risk Model Accuracy
- Collect relevant client information, including credit history, medical history, and driving record.
- Analyze the data to devise a risk score for the insured party.
- Apply predefined underwriting guidelines to accept or decline the application for the policy, and if accepted, calculate the client’s premium.
How Consumers Benefit from Insurers’ Use of Predictive Analytics
- The increased availability of potentially relevant data must be matched by modeling that separates high-quality data from low-quality data. By increasing the amount of quality data available, the model’s results will be more accurate.
- Predictive models score underwriting submissions to identify traits such as “broker sincerity” and “projected loss ratio for this class.” AI-based ranking systems help underwriters calculate the optimal course by connecting insight with action.
- By reducing the amount of guesswork entailed in decisions, AI-based models improve the accuracy and consistency of employees’ policy work. For example, adjusters with limited experience can be guided by the system to avoid overcharging a client for a policy or undercompensating for a claim.
- Much of the overhead insurers pay can be eliminated by automating fraud detection via AI-based pattern recognition. Insurers are able to pass much of the savings they realize to consumers by offering them lower premiums.
How Are Predictive Analytics Used in the Life Insurance Industry?
How Life Insurance Companies Benefit from Predictive Analytics
- Competitive pressures in product development and pricing (cited by 78% of respondents)
- Customer relationship management (67%)
- Earnings and profitability pressures (64%)
- Technology innovation (60%)
- Reduction in issue and underwriting expenses. 17% reported a strong positive impact, and 50% reported a somewhat positive impact.
- Significant increase in sales. 17% reported a strong positive impact, and 43% reported a somewhat positive impact.
- Increase in profitability. 13% reported a strong positive impact, and 47% reported a somewhat positive impact.
Application of Predictive Analytics by Life Insurance Underwriters
- Pricing and rate-setting use is forecast to increase from 31% to 56% in two years for group life, and from 18% to 55% for individual life.
- Underwriting use may increase from 52% to 92% in two years for individual life.
- Mortality and morbidity risk use may increase from 19% to 56% in two years for group life, and from 23% to 75% for individual life.
- Claim management use may increase from 37% to 87% in two years for group life, and from 10% to 40% for individual life.
Statistics on Use of Predictive Analytics in Life Insurance
- Current use of predictive analytics to calculate individual life policies (and expected percentage in two years):
- 70% of large carriers (increasing to 90% in two years)
- 50% of midsize carriers (75%)
- 54% of small carriers (89%)
- Current use of predictive analytics to determine group life policies (and expected percentage in two years):
- 71% of large carriers (increasing to 100% in two years)
- 67% of midsize carriers (100%)
- 23% of small carriers (46%)
- Current use of predictive analytics to calculate individual health policies (and expected percentage in two years):
- 40% of large carriers (increasing to 80% in two years)
- 20% of midsize carriers (40%)
- 15% of small carriers (38%)
- Internal customer data: 55% used as of September 2018 (82% planned to use in two years)
- Customer interactions and surveys: 55% (73%)
- Clickstream data: 18% (45%)
- Social media: 13% (35%)
- Web scraping: 11% (29%)
- Wearables 6% (38%)
The Challenges of Using Predictive Analytics in Life Insurance
Resources for Predictive Analytics in Life Insurance
- Deloitte, 5 Ways to Sell More Life Insurance: This report describes how predictive analytics can stem the sharp decline in life insurance policies over the past 20 years.
- Canadian Institute of Actuaries, The Use of Predictive Analytics in the Canadian Life Insurance Industry: This study identifies several trends that impact the application of the technology.
- TechHQ, How Predictive Analytics Is Closing the Life Insurance Gap: This article examines the effect InsurTech firms have had on the industry.
How Are Predictive Analytics Used in the Health Insurance Industry?
- Data-driven claims decisions
- Reduced operating expenses
- Improved profitability and expansion in new and existing markets
How Predictive Analytics Can Help Identify High-Risk Patients
How Predictive Analytics Can Reduce Healthcare Expenses
- Identify gaps in care and at-risk patients most likely to experience a “crisis event.”
- Increase visibility into inappropriate use of high-cost health services.
- Address social determinants of health, such as food and housing security, patient health literacy, and transportation.
- Develop, test, and implement new workflows and care models.
- Enhance patient satisfaction and customer experiences.
Use of Predictive Analytics to Improve Claims Processing
- Allocation of resources/triage
- Reserving/settlement values
- Identification of potentially fraudulent claims
- Early warning of potentially high-value losses
- Expense management
- Trend analysis
Resources for Predictive Analytics in Health Insurance
- Innovation Enterprise, How to Utilize Predictive Analytics in the Health Insurance Industry: This article discusses the use of social media analytics to strengthen relationships between physicians and patients.
- Managed Healthcare Executive, Making the Most of Predictive Analytics: This piece explains techniques used by healthcare providers and insurers to apply predictive analytics to improve care and control costs.
- Towards Data Science, 3 Tools for Healthcare Claims Data for Predictive Analytics: This article presents three types of predictive analytics models used in healthcare.
Predictive Analytics Tools for the Insurance Industry
Pricing and Product Optimization
- The Alteryx data analytics engine helps healthcare insurers and payers combine clinical, operational, and business data models to streamline processes and monitor costs. The product is used in call center support based on resource use and customer preferences, among other applications.
- Guidewire Live Analytics includes data models predesigned for specific insurance uses, including the Guidewire Claim Canvas that allows claims managers and catastrophe responders to geo-visualize claims and policy locations in combination with internal and external data.
Claims Prediction and Timely Resolution
- Lemonade runs insurance claims through an AI-based engine when they’re filed to detect fraud automatically, collect information from insured parties via chatbots, and approve claims on the spot when they meet prespecified criteria.
- Tractable is an AI-based system designed to assess car damage in real time, help insurers respond to disasters more quickly, and analyze the content of visual images. The company’s machine training techniques are optimized to handle uncertainty in datasets.
Predicting New Customer Risk
- Slice is an InsurTech firm that offers property and liability insurance on demand. The company’s AI-based Slice Mind service for insurers predicts risk by classifying business activities based on a website, a series of keywords, or a single natural-language sentence. The company also offers real-time cyber risk modeling, geographic distribution of risk, and a claims fraud detection service.
Fraud Detection and Policy Manipulation
- V2verify uses voice recognition to authenticate insured parties to healthcare providers, call centers, and other industries. In addition to enhancing customer experiences and improving access controls, it helps insurers ensure compliance with regulations and accommodate audits.
- ForMotiv’s Digital Polygraph predictive behavioral analytics models automatically identify and measure thousands of unique behavioral signals to establish a “digital body language” that predicts the intent of the people filing insurance claims and other online forms.
Dynamic Engagement of Customers
- ScoreData offers insurance companies real-time modeling designed to gain a better understanding of their customers’ changing behaviors and needs. The company provides risk-based granular pricing and advanced customer analytics, including customer lifetime value, retention and lapse analysis, social media analytics, and lead generation and conversion.
- Genesis Engage is a customer experience application that uses AI techniques to automate dialogues in self-service situations. The product uses “predictive routing” to determine the best agent for a specific customer and analyzes interactions with clients in real time to take advantage of the context of contacts and improve the customer experience.
The Future of Predictive Analytics Use in the Insurance Industry
Extending Predictive Analytics Use to Other Types of Insurance
Factors to Consider When Planning Investments in Predictive Analytics
- Having a mature analytics process in place provides firms with a competitive advantage by identifying customers who are most likely to switch vendors and offering them incentives to remain.
- Companies are able to maximize the value of their internal data assets to enhance marketing operations and other business processes, especially in the area of customer engagement.
- The products identify areas at the corporate and department levels in which costs can be reduced, such as analyzing claims data to assist investigators working to prevent fraudulent claims.
- Predictive analytics provide companies with earlier notification of potential problems by analyzing customer and other data in near real time. For example, monitoring customer responses gives insurers insight into which innovations customers prefer.
- More classes of data can be included in the analyses that business decision-makers rely on, such as levels of price sensitivity among customers in specific demographics, and customer brand affinity.
- Predictive analytics enhance revenue growth over competitors that are slower to adopt predictive analytics. Research by Cisco Systems found that companies that have an enterprisewide analytics policy in place have average annual revenue growth greater than 7%.