AI Consulting Group
Artificial Intelligence, Machine Learning, Data Science, IoT, Data Platform Modernisation, Consulting
How AI is helping insurance companies build insights about claims.
Historically, strong insurance performance meant having good products and a good claims service. Recently however, technology, data & AI have become vital to competitive advantage. Here are some real examples of insurance programs generating genuine ROI:
Improved Claims Management through Chatbots, Analytics and AI to empower quick decision making for agents.
- Automated customer responses to minimise agent time.
- Automated customer identity management.
- Automated photo and document collection and assessment.
- Automated report assessment and more.
Improved Claims Experience for members through intelligent recommendations and claims applications.
- Digital approval processes.
- Data-based insights to proactively reduce premiums for low risk behaviors.
- IoT and device tracking to send information to the insurer.
- Maintain an effective digital customer experience management program.
Generate insights from claims to proactively assist with policy pricing.
- Understand the reasons for claims.
- Predict incidents before they occur to warn the customer and minimise the need for future claims (claim prevention).
Some anecdotes from insurance organisations.
- These are some quotes and stories from the industry that help explain the real benefit that AI is delivering today and into the future:
Understand how the Customer want to Engage
“There’s no good news story when it comes to a life insurance claim. You are dealing with someone worried about death and their financial future, Our hypothesis was a claimant would always want to speak to human beings, who are trained on tone of voice and engagement. Yet this wasn’t true – our claimants want their data served up to them on their mobile.” – David Neate (Zurich)
Minimise Claims Processing Costs using Artificial Intelligence
Insurers are using Artificial Intelligence (AI) to automatically process simple, common claims which reduces time spent by agents, maximises consistency and reduced times for claims payouts to maximise customer satisfaction.
Predict and Prevent a Drowning
After combining temperature data with claims data, an insurer found that deaths by drowning in the male 55-65 year group doubled in months when temperatures reach a certain point, leading to the hypothesis that older gentleman were over-exerting at the beach – effectively predicting an increased claim likelihood for those members. Most importantly that information could be used to warn and prevent the claim to begin with – a win-win for the customer and the insurer.
Predict and Prevent a Suicide
An insurer discovered that suicides are often preceded by attempts to withdraw their superannuation (due to financial hardship). Again, this information could be used to introduce prevention mechanisms.
Identify Fraudulent Claims
Insurers easily identify fraudulent claims based on other previously identified fraudulent claims by using machine learning algorithms and automated fraud risk notifications.
Some Insights from McKinsey
AI to Innovate Claims
“Integrating real-time customer interactions and insights from AI modules into customer journeys poses vastly different requirements for the IT architecture. While in the past, online interactions with the customer were only one way (for example, saving the details of an online FNOL into the claims database), interactive digital customer journeys require real-time, bidirectional interactions. A new IT architecture concept—generally referred to as two-speed architecture—is required to complement the stability of the core claims database with responsive features on the front end. A middle layer connects the traditional, slow claims database with customer-facing interfaces and runs AI modules. This functionality connects the information a user submits with insights from AI in real time to populate online forms and offer direct feedback to the customer” – McKinsey
Examples
Prediction of Claims Characteristics:
AI will assess characteristics of a claim, such as the likelihood of fraud, total loss, or litigation, to speed up its downstream handling. According to McKinsey, a European insurance carrier, “significantly improved its fraud detection accuracy implementing an AI-based fraud detection system resulting in an 18 percent increase in fraud prevention as well as productivity gains in fraud investigation”.
Claims Segmentation and Handling:
AI can segment claims based on complexity to minimise handling times through optimised internal routing to ensure that the most appropriately experienced claims handler is assigned specific cases improves claims handling accuracy and throughput.
Other non-Claims Related Digital Transformation Aspects for Insurers to Consider (from EY)...
