CXposѐ.tech: Can Twilio share a comment on the relevance of AI, analytics, and automation in shaping convenient customer experiences (CX) that are compliant for a regulated industry like insurance and insurtech?

Christopher: With new technologies like AI-powered chatbots and conversational AI, insurers can eliminate friction in interactions by leveraging contextual data to reference previous claims and enquiries, anticipate customer needs, resolve issues swiftly across multiple channels, and deliver personalised recommendations on insurance policies. These tools also empower insurers to guide customers through complex decisions more naturally and effectively. AI agents can further enhance the customer experience by providing 24/7 support and handling high-volume enquiries efficiently, while still making decisions within set constraints to ensure compliance.

Christopher Connolly

As LLMs evolve to become more reliable and trustworthy, they can support more accurate, data-driven decisions. There is even greater potential for the agentic workforce to augment human decision-making in areas like claims evaluation and risk assessment. Furthermore, investing in AI-powered fraud detection, prevention, and protection services ensures that customers are who they claim to be, rather than fraudulent bots. These services can help prevent account compromise, combat fake accounts, safeguard assets, and maintain consumer trust, ultimately strengthening the industry’s position in the evolving digital landscape.

Gavin: Most insurance companies are constantly trying to improve and streamline the Customer Experience, which can often fall short of expectations. With agentic AI, the latest wave of innovation in AI, insurers have the opportunity to re-imagine customer experience, drive higher productivity and utilise an unlimited workforce, available 24X7.

Gavin Barfield

Unlike chatbots, AI agents are fully autonomous. They can take action within prescribed limits, speeding up Service case resolutions and freeing human agents for more complex cases.

The value of AI agents for the insurance industry also extends to other front-office functions like sales. AI agents can enable less experienced salespeople to leverage institutional knowledge that normally only resides in tenured employees.

Insurers manage huge volumes of unstructured data such as policies and process documents in PDFs, presentations and other formats. Agentforce, the first digital labour platform, is powered by Salesforce Data Cloud and uses advanced Retrieval Augmented Generation (RAG) technology to bring in unstructured data which can then be brought together with structured data.

Salesforce Data Cloud can utilise zero copy technology to free data locked-in Data Management solutions without having to physically copy data. Bringing together structure and unstructured data creates a single source of truth which is used to effectively ground Agentforce.

In order to maintain compliance and trust in AI agents, it is essential that proper guardrails are in place to protect data and to enable seamless handover to humans when the agent reaches the end of the defined scope. Agentforce is built on the Einstein Trust Layer, which helps protect Personal Identifiable Information and flag toxicity.

CXposѐ.tech: What are the top AI use cases you have seen for insurtech/insurance companies?

AI enabled applications are significantly impacting the insurance industry as well as insurers move beyond traditional claims management and embrace digital workflows that employ a fully analytics-driven approach. This includes using AI to automate claims processing, to identify fraudulent claims, to more accurately manage risk and to create new digital services to increase customer satisfaction.

Kevin Levitt

For instance, Cape Analytics (to be acquired by Moody’s) is a computer vision startup that transforms geospatial data into actionable insights for insurers to write better policies and provide suggestions for homeowners to protect their property against wildfire damage. The startup uses AI to produce detailed data on the vegetation density, roof material and proximity to surrounding structures along with a calculated risk that homeowners can use to take preventative action. Cape Analytics trains its models on servers and uses them for live inferencing, with geospatial data converted into actionable structured data in seconds.

CXposѐ.tech: How have recent AI developments like Large Language Models (LLM) influenced the way data is collected and used in insurance and/or FSI? In turn how does this influence the way that NVIDIA would build AI solutions for insurance and FSI?

AI advancements have significantly influenced methods of data collection and utilization in the insurance and financial services industries. LLMs have transformed the way insurance and FSI organizations handle vast amounts of structured and unstructured data, enabling more efficient, accurate, and personalized services.

For example, AI models can automate claims handling by extracting insights from documents (e.g., accident reports or medical records) and classify claims for faster resolution. This reduces manual intervention, accelerates payouts, and minimizes errors. AI can also analyze diverse data sources, such as PDFs, satellite imagery, accident photos, claims history and more to offer hyper personalized services and products.

To support the broad set of AI enabled applications insurers will build to support all lines of business and functions, leading insureres are investing in AI Factories powered by NVIDIA’s accelerated computing and AI platforms. These platforms are delivering meaningful ROI by speeding AI enabled applications to market that reduce operational inefficiencies, reduce costs, and increase customer loyalty.

These AI Factories represent the next industrial revolution as they generate intelligence that will power insurers’ products and services in Generative AI, Agentic AI, robotics, as well as digital twins for weather forecasting, extreme event simulation and more.. NVIDIA full stack platform includes software libraries and APIs that include pre-trained models with domain-specific AI models that accelerate data scientists’ ability to build AI-enabled industry applications in FSI.

CXposѐ.tech: GPU-powered image analysis and AI to identify damages, automate claims, estimate costs and identify fraudulent claims – this is a use case that Ping An has shared. Can you comment about the readiness of industries to implement use cases like this. What are the challenges and opportunities for insurtech/insurance in APAC?

The insurance industry is already seeing positive results from the use of computer vision and natural language processing for the application of claims management.

For example, an AI-powered solution by company ControlExpert automatically classifies the content of images, such as vehicle photos or documentation (e.g., license or registration). ControlExpert uses vision AI to identify vehicle information like make, model, color, and license plate. Based on images of vehicle damage, ControlExpert also developed AI models to segment visible vehicle parts and precisely detect the severity of the damage, generating a detailed description of the damage as well as cost estimation for repairs. For the solution to accurately assess the damage, ControlExpert developed AI-based fraud prevention that analyzes and detects manipulated images.