While companies across various industries are rapidly adopting digital technologies, are insurers keeping up with the evolving demands of their customers?
Customer service is crucial in the insurance sector, impacting areas such as marketing, customer retention, and claims management. Nowadays, customer service teams experience higher turnover rates compared to other departments, and training these teams requires significant time and effort. Additionally, the frequent outsourcing of customer service to different providers poses challenges for maintaining service quality and consistency.
With a PhD in Computer Science, Andrea Rosales specialises in domain adaptation, transfer learning, continual learning, and generative AI. Andrea is passionate about developing innovative data science models that deliver impactful solutions. She has a proven track record of creating novel deep-learning models to address real-world problems in both industry and academia, and she is recognised as a Global UK Talent.
Andrea Rosales
Lead Data Scientist
AI has the potential to revolutionise customer service across industries by enhancing efficiency, personalising interactions, and predicting customer needs. AI enables faster and more accurate responses to customer inquiries. For instance, companies that reach higher maturity levels of AI integration in customer service can handle up to 95% of interactions through digital and automated channels. This leads to quicker resolution times and more personalised services.
AI can address these challenges by incorporating customer intent prediction into customer service operations. Customer intent prediction involves understanding and categorising the purpose behind a customer's interaction. Whether a customer calls, emails, or chats, AI models analyse the content of their messages to determine their needs.
In the next section, we will explore how AI-driven solutions can elevate customer service in the insurance sector by predicting customer intent, enabling insurers to not only meet but exceed customer expectations.
Accurately understanding customer intent is a game-changer for insurance companies. It enables them to provide more personalised, efficient, and effective service, ultimately leading to higher customer satisfaction and operational efficiency. Here are some key benefits of understanding customer intent:
Reduce churn: Customer intent analysis allows insurance companies to identify at-risk customers and address their concerns before they churn.
Targeted Marketing and Sales: Understanding customer intents provides valuable insights into their needs and preferences. This information can be used to create targeted marketing campaigns and personalised product recommendations, increasing the likelihood of sales conversions.
Personalised Policy Recommendations: By accurately predicting customer intent, insurers can offer personalised policy recommendations that better meet individual needs. For example, if a customer frequently asks about travel insurance, they might be interested in an annual travel policy.
Streamlined Claims Processing: Recognising intents such as "file a claim" or "check claim status" allows for the automation and streamlining of claims processing. This results in faster and more efficient handling of claims, reducing wait times for customers.
Customer Satisfaction: This is the big one. Customers who get high-quality service right away and don't spend an extra 20 minutes bouncing between agents and listening to hold music will be happier and therefore more likely to remain customers for longer.
Agent Performance: Traditional call centres operate based almost entirely on workload. If someone is free, they take the next call on the switchboard. But AI makes this process smarter. By distributing agents to claims based on skill set, companies can better manage their workforce, likely resulting in faster call resolution and a higher overall level of performance.
Interpreting the analysed customer intent data, identifying patterns and trends, and translating these findings into strategic actions complete the process. These insights can guide businesses in improving their products or services, enhancing their marketing strategies, and providing better customer service.
Conducting intent analysis involves several steps. To better understand it, we've broken it down into smaller steps:
Data Collection: When a customer interacts with the insurance company, their verbal or written input is collected. For voice interactions, this involves transcribing speech to text using an audio transcription service.
Text Processing: The collected text data is then processed to clean and normalise it, ensuring that the text is in a format suitable for analysis.
Intent Classification: NLP models analyse the processed text to classify the intent. The AI model can be trained to recognise various intents such as claims, policy inquiries, premium payments, etc.
Actionable Insights: Once the intent is identified, the system can route the customer to the appropriate department or provide automated responses, enhancing efficiency and customer satisfaction.
In the diagram below, we provide an example of how AI can be integrated at various touch points into a customer service workflow in an insurance company.
AI integration diagram
The process starts when the customers initiate contact through either phone calls or chat interfaces. When a call is received, the spoken interactions are transcribed into text by an AI-powered audio transcription service.
Using AI models, for example, Hugging Face's DistilBERT, the transcribed text is analysed to determine the intent behind the customer's query. For instance, phrases like "I had a car accident" are classified under "Car Insurance / Accident," while "Email me my life insurance policy" falls under "Life Insurance / Email Policy."
Based on the classified intent, the system can automatically route the customer to the relevant department or specialist. In the example, the customer who had a car accident is directed to an auto insurance specialist who can address the claims efficiently. The AI models ensure that the responses are tailored to the specific needs of the customer, providing a more personalised and satisfactory experience.
Several AI models are used for this purpose, particularly those specialising in natural language processing (NLP), for example:
BERT is a transformer-based model designed to understand the context of words in search queries. It reads text bidirectionally, meaning it considers both the left and right context in all layers. BERT's ability to understand context makes it highly effective in predicting customer intent. It can be fine-tuned on specific datasets to classify intents accurately, such as determining if a query relates to policy information, claims, or payments.
RoBERTa builds on BERT by optimising the training process, including training on more data and using larger batch sizes. RoBERTa's enhanced training makes it even more effective at understanding nuanced customer queries and accurately predicting intents, providing a robust solution for complex intent classification tasks.
GPT is one of the largest and most powerful language models, with 175 billion parameters. It can generate text, answer questions, and perform a wide range of language tasks with high accuracy. It can handle complex and varied queries, making it suitable for applications requiring high accuracy and nuanced understanding. It can predict customer intents within the flow of a conversation, providing accurate and contextually relevant responses.
Both Hugging Face and OpenAI offer powerful models for predicting customer intent. Hugging Face's BERT, DistilBERT, RoBERTa, and GPT models provide robust and scalable solutions for understanding and classifying customer queries. OpenAI models excel in handling complex, conversational interactions, making them ideal for advanced customer service applications. Insurance companies can significantly benefit from these models in understanding and responding to customer needs, leading to improved service, efficiency, and customer satisfaction.
The integration of AI, particularly advanced NLP models, is just the beginning. As AI technology continues to evolve, we can expect even more sophisticated applications in the insurance industry, from predictive analytics for risk assessment to advanced fraud detection mechanisms.
By adopting AI solutions, insurance companies can not only improve their operational efficiency but also provide superior service to their customers. The future of insurance is undoubtedly digital, and those who implement AI will lead the way.