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The 3 most important factors to consider in the rise of intelligent automation in personal lines insurance
Personal lines insurance is benefiting from the development of AI methodologies to overcome practical business challenges. Changing customer expectations and the emergence of insurtechs providing cost-effective solutions have been the driver for investment in emerging technologies.
However, getting the balance right between introducing technologies which enhance the customer experience and streamline business practices against reducing expenses and getting the right ROI can be problematic.
Here we have listed 5 technologies that can drive value for an insurance business, either from extra revenue or cost savings, including:
1. The cloud
Insurance is data intensive and thanks to cloud-based storage, vast volumes of data can be stored and managed at an affordable cost. By accessing the cloud through the internet, insurers can avoid the costs and burden of setting up and maintaining their own IT infrastructure. Instead, they ‘pay as you go' for the amount of storage they use. They can also rent other services such as applications and processing power.
2. Robotic Process Automation (RPA)
RPA is the automation of mundane, repetitive and labour-intensive tasks carried out by employees. It increases productivity and frees up staff to deal with more complex and challenging issues which require the application of emotional intelligence and strategic thinking.
3. AI and machine learning
Machine learning is a branch of AI where systems learn and improve from previous experience rather than being directly programmed. Machines learn patterns in sets of data and use it to predict patterns in new sets of data. This makes analytical models in areas such as risk and fraud detection more accurate and helps insurers make better business decisions.
4. Neural networks
Modelled on the human brain, neural networks, also known as deep learning, are a set of algorithms designed to recognise patterns which help analyse and classify data. The analysis is then used to improve insurance services and enhance customer experience.
Chatbots are virtual assistants who simulate conversations with people, responding to or resolving their queries. If someone needs further help, the chatbot can direct the person to resources or humans who can help them. Examples of how you can interact with them are on websites, via message apps and by phone. They improve support for customers and time for insurance customer service agents who don't have to deal with simple repetitive queries.
The application of intelligent automation in personal lines
Intelligent automation can be used throughout the insurance lifecycle. It delivers business intelligence through high-speed evaluation and analysis of data, a crucial task in a data focussed industry.
1. Automation of business processes - RPA is helping with process optimisation.
For example, rather than manual data entry, documents are digitally scanned and information stored for access at a later date. It also provides an electronic audit trail for tasks performed, which helps with regulatory compliance and identifying areas to make further improvements.
2. Product design - AI and machine learning are being used to create products for different customer segments. Using behavioural data analysis, insurers can explore the relationships between customer segments and policy types, then design products tailored to their needs.
3. New Business - AI is also being used to optimise new business agents' performance and online transactions.
By tracking the sales cycle and analysing interactions, AI can provide recommendations to improve sales opportunities.
For example, recommending the optimal point to interact with a customer such as when to tell them about the benefits of a particular policy and what products to cross-sell.
4. Customer experience - Customer experience is an area where leveraging digital advancement can have a significant impact on a business. A seamless journey encourages loyalty in an industry where customers are actively encouraged to consider alternative providers.
For example, the options listed below are all helping to meet customers' growing expectations, such as:
82% of respondents surveyed for our Digital Claims Conference 2019 said they thought AI and machine learning would transform the claims experience.
AI and machine learning tools are already enabling insurers to identify and flag abnormal patterns which may be fraudulent claims, saving money on claim payouts and keeping premiums lower.
Moreover, online claim notification and the electronic submission of evidence and claims related documentation is reducing claims processing times significantly and improving customer satisfaction.
RPA is helping insurers gather detailed information about prospects and customers to make accurate risk predictions. It can speed up data collection and processing, update systems and make premium recommendations based on customer behaviours (behavioural premium pricing).
Devices such as telematics in car insurance or a fitness tracker in health insurance collect data and feed it back to the insurer. The information helps insurers to build a picture of customer behaviour and shows their risk level.
In Part two, we’ll consider the key challenges to implementing new technology, ROI and business outcomes.