Insurance

Data has been at the heart of the insurance industry since its beginnings. The explosion of data globally, along with exponential increases in computing power, is driving advances in AI. This creates unprecedented opportunities especially for insurers, where AI has already started to revolutionize the industry. Key use cases of AI technology lie within product development, sales and marketing, pricing and underwriting as well as claims settlement.

Product development

On-demand insurance: With the emergence of more specific needs and the increased use of online services, the traditional insurance model tends to an on-demand strategy. AI, using customers’ geographic and social data, can personalize pricing and automate custom coverage for specific items and events.

Microinsurance: Being able to deliver insurance solutions to the lower and emerging middle-classes of the economic spectrum is a recurring challenge. Robotic Process Automation, designed to automate unsophisticated and repetitive jobs, leaves employees more time to focus on tasks that actually require human input. Reducing time and costs can help to cover more people at lower price, hence closing the protection gap.

Usage-based insurance: Using data rather than human judgement to classify the risks allows, for instance, high-milage drivers to pay less for car insurance, and people who have already traveled extensively to pay less for travel insurance.

Pricing and underwriting

Modular/ behavioural pricing: A dynamic insurance plan, that adapts premiums and pricing based on individual needs, behavior and an expected willingness-to-pay. This is made possible by AI predictions and risks modelling techniques, taking into account past behaviour and price points for similar customers.

Granular risk models: Finding an accurate pricing plan for each individual depends on real-time data at a detailed granular level. Leveraging large databases using AI allows the computation of granular risk models based on individuals rather than groups.

Automated application scoring and processing: With the increased demand for insurance coverage, scaling the application-reviewers teams is a challenge. Document capture technologies enable insurance companies to automatically extract relevant data from application documents, score the risks and accelerate insurance application processes with fewer errors and improved customer satisfaction.

Risk segmentation: AI-driven segmentation quickly discovers changes in customer and risk segments. By incorporating geo-sensitive third-party data and economic indicators, the resultant model can accurately identify anomalies and emerging trends.

Sales and marketing

Personalized offerings: Adapting the offerings to the customer’s situation and needs can be automated thanks to geographic and social data, as well as past customer behaviour and risks modelling techniques.

Sales Force steering (e.g. “next best action”): Anomaly detection, predictive analytics, text analysis and other AI-powered tools can detect claims with high chance of fraud by comparing conditions of the insurancy policy with data captured from the applicant’s report.

Customer churn prediction and prevention: Predictive and prescriptive machine-learning algorithms can help organizations find patterns in the way customers respond to different touchpoints, hence determining which actions are more likely to lead to conversion. This set of techniques is referred as “Next Best Action” (NBA) and will increase the effectiveness of your salesforce

Claims management

Claims process automation: Using text analysis tools and document capture technologies automates claims processing and allows to dedicate more time to tasks that actually require human attention.

Fraudulent claim detection: Saving time and at the same time increasing the precision in fraud detection is a main concern for any insurance. Using anomaly detection, AI-powered predictive analytics and text analysis tools might detect fraudulent claims based on business rules with data captured from the claimant’s story.

Claims appeal processing: Document analysis and Robotic Process Automation significantly speed up appeal processing time and accuracy based on high degrees of automation.

Customer support automation (e.g. chat bots, voice bots): Using chatbots and voice bots trained on customer interaction data from previous text or voice exchanges can help in reducing the workload of human customer support for standard queries (e.g., “How do I file a claim?”). Additionally, as the bots are available 24/7, you can benefit from an increased customer service level and lower labor costs.