5 use cases for AI in insurance
Artificial intelligence (AI) is touching every industry and vertical. Hannover Re’s Senior Data Scientist Julia Perl describes five ways in which AI is reshaping insurance and changing business models.
Insurance distribution is the first step in identifying and acquiring customers. It’s a term that embraces the sales and marketing strategies that connect insurers with potential customers and pave the way for targeted products. By applying big data and algorithms, AI can increase customer focus and engagement in several ways. Examples here include better customer segmentation, selection of the most effective distribution channels, and predictions about future demand based on data analysis from a wide range of sources. The insights derived from AI can improve the effectiveness of sales teams and strengthen lead generation. They can also identify opportunities to cross-sell and up-sell products and services.
Related solutions: #distribution
Faster and more accurate underwriting is possible when AI supports human risk analysis and traditional modelling. Rule-based models and risk engines are no longer enough for accurate estimates because risks are diversifying and becoming more complex due to cyber threats, climate change and many other emerging risk scenarios and dependencies. Insurers can evaluate risks and underwrite policies with greater speed and precision when they use artificial intelligence to interpret data and identify risks that might otherwise go unnoticed.
Underwriting systems backed by AI can sift through a huge number and variety of data points from public information sources, historical records, third parties, social media and other channels. This minimises manual processing and reduces errors. Application fraud is easier to spot and more transparent risk profiles will help insurers to shape policies and reject bad risks.
Related solutions: #acceleratedunderwriting
#3 Claims handling
Assessing and settling claims is a labour-intensive and demanding activity. Insurers must carefully check and validate every submission, a process that is often paper-based and may lead to errors and oversights. AI can reduce the administrative burden and streamline workflows while increasing the accuracy of every assessment. With the growth of the internet of things and the use of telematics, wearables and other connected devices, insurers can automatically gather a wide variety of claims-related data that can be analysed with AI tools. For example, visual AI can analyse images of damage to cars or property and provide instant assessments and cost estimates. With machine learning algorithms, insurers can review and interpret all incoming data. This can help with initial claims routing, triage, fraud prevention, and overall cycle times.
Related solutions: #claims
#4 Fraud prevention
Fraudulent applications and claims are a constant challenge for the insurance industry, not least because fraudsters are adept at finding new ways to exploit vulnerabilities and deceive underwriters and claims handlers. AI can reinforce and enhance existing fraud checks and methodologies, and eliminate human error. Machine learning and deep learning systems can examine large datasets and identify anomalies and fraud patterns that may not be apparent to human reviewers, and predictive analytics can identify the likelihood of fraud. Deceptions and suspicious activity can be revealed at all stages of the insurance cycle and at every customer touchpoint, leading to more accurate fraud scores.
Related solutions: #fraud
#5 Customer experience
Customer experience – which today overwhelmingly means digital experience – is a critical success factor for all businesses. AI can greatly improve how insurers engage with customers and add value to relationships during the lifetime of a policy. Speed of service is one of the most important factors for customers, as is the ability to connect through any channel and provide personalised advice. AI can guide customers through numerous stages, from applications to claims, and provide instant responses and a tailored service without human intervention. A good example is chatbots – AI-powered virtual assistants that are available 24/7 and growing in sophistication. With the introduction of AI tools and systems, insurers can follow the example of customer-centric companies and understand more about customer needs and expectations.
Related solutions: #engagement