AI automates repetitive human tasks up till now possible to carry out only by humans. Examples include analysing documents, pictures, and complex information.
Example areas where insurers are successfully applying AI for automation
Insurer’s benefits from AI
Lower operating costs
Lower claims paid
Humans make mistakes, which results in claim leakage, usually estimated by internal audits. Once trained, AI is consistent and independent of tiredness.
Lower substitute costs in claims
AI automation can shorten the claims handling process, which may result in shorter rent of a replacement car or lower payments of business interruption claims.
Better customer experience
AI enables new ways of interacting with customers. An immediate, empathetic and correct response from the insurer can have a positive impact on the customer's perception.
Better analytics of frauds, tariffs, efficiency
Automatic AI recognition unveils data hidden in unstructured form in documents and photos that previously had to be manually processed by employees. Structured data is the fuel for predictive analytics.
Improved contract visibility, compliance & risk assessment
AI allows for automatic search and analysis of different types of contracts. It enables insurers to respond quickly to regulatory inquiries and increases the visibility of contractual risk.
Better employee experience
AI automation will reduce manual tasks and foster efficiency, allowing employees to focus on the higher-value creative aspects of work. Additionally, it will create exciting new jobs related to configuring and managing automated processes.
Higher quality of human work
AI can augment the work of all employees, not just experts, leveraging features like analytical skills and scalability to unlock insight and efficiency. This enables faster decision-making, more precise problem-solving, and enhanced innovation across the organization.
Lack of AI automation knowledge
The lack of knowledge related to AI automation stops you from making the right decisions regarding automation planning
- What automations should I consider?
- There are many AI tools – which one can I choose to solve my problem at a reasonable cost?
- How to manage the increasing complexity of new business logic and redefined processes?
- As an organization, how do we gain and manage knowledge related to AI automation?
Managing complexity of corporate architecture
AI tools are only a small part of the overall landscape
- Typically, AI automation introduces new data and business processes into current systems, which means the entire company architecture must be rethought to support it.
- Otherwise, excessive implementation costs and architecture complexity can halt the progress of AI automation initiatives.
- As an organisation, do we have experience in managing complex architecture change connected with business transformations?
Advisory about AI solutions
Sollers can support your team in learning AI tools to make the right decisions
- Educational/inception workshops – your team will become inspired by what is possible with AI
- Analyse as-is process to identify opportunities and realize the possible benefits of AI automation
- PoC – verify AI tool against your actual process but out of production environment – no risk, little cost
Design an AI transformation
Sollers can design and plan transformation of AI automation
- Design process changes and assess their impact on your organization
- Understand the scope of required AI tools
- Plan the target architecture roadmap to optimize efforts
- Define approach to governance to ensure ownership of new areas
- Understand the costs and benefits of the transformation to make better transformational decisions
Implement the AI transformation
Sollers can help you to
- Manage the transformation roadmap
- Configure AI tools and business logic
- Redefine business processes
- Redesign UI for business users or clients
- Implement changes to insurer IT core and front-end systems
Cloud platforms with AI tools
The major cloud providers offer a set of various best-of-breed AI components for addressing different problems of automation. You can think of it as building blocks, which democratises the usage of AI.
Examples: AWS, Azure, GCP.
omni:us Digital Claim Adjuster
The solution enables end-to-end automation of the insurance claim-handling process. It comes with AI components, reference processes and preconfigured business logic.
SEND Smart Submission & Underwriting Workbench
Underwriting workbenches support the underwriter workflow in managing new business, renewals and endorsements. They have various automation features which enhance submission, risk selection, pricing, quoting and underwriting. Some of the features are AI-driven.
Intelligent Document Processing platforms
IDP platforms provide a complete set of functionalities, including AI and NLP, to streamline the entire workflow of extracting information from various types of documents.
Example solutions: ABBYY, Appian, Hyperscience, Indico Data, Tungsten.
Generative AI/LLMs
Generative AI/Large Language Models (LLM), like ChatGPT, can augment experts, perform complex tasks, and improve business processes. In Sollers Consulting, we leverage the capabilities of LLMs to fit the needs of insurers.
Examples: Amazon Bedrock, Google Gemini, OpenAI.
Predictive Analytics
Predictive Analytics employs statistical algorithms and machine learning techniques to analyse historical data, unveil patterns, and predict future events or trends, allowing organizations to gain insights, anticipate outcomes, and make informed decisions. These solutions are integrated into various AI cloud platforms and enterprise decision engine systems, among others.
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Piotr Kondratowicz
Business Architect
