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Artificial Intelligence in Insurance

Take the next step in process automation and improving customer experience

Artificial Intelligence in Insurance

Take the next step in process automation and improving customer experience

Adapt AI solutions with us

At Sollers Consulting, we aim to support our clients in complete transformations of AI automation. We learn and test various AI tools available on the market.

 

We are helping our clients make the best choices of AI tools to maximize value.

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Strategic areas for AI implementation in insurance

Due to the virtual nature of insurance, the insurance industry is ideal for digitalization.

Based on our experience we selected core areas for quick ROI of AI implementation.

  • Shorter claims handling time​
  • Increased productivity​
  • Fewer unjustified payouts​ (less human error)​
  • Easy claim submission for customers​
  • Shorter underwriting process​
  • Improved risk assessment and pricing​
  • Increased productivity ​
  • Easier offer personalization
  • Increased customer self-service​
  • Expanded customer base due to foreign language support​
  • Improved customer satisfaction leading to:​
    • Customers retention​
    • New policies sales​
    • Cross-sell
  • Decreased software development effort​
  • Better time-to-market​
  • Improved software quality​

AI tools are very effective

but the right tool needs to be used for a specific problem.

For example, a typical success rate of intelligent document recognition is 80–95%.

Insurer’s benefits from AI

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Lower operating costs

AI automates repetitive human tasks up till now possible to carry out only by humans. Examples include analysing documents, pictures, and complex information.

 

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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.

 

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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.

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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.

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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.

 

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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.

 

 

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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.

 

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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.

 

AI components are easy to configure

You do not program an AI component, you teach it almost like a human.

 

So why do companies not adopt the AI revolution relatively quickly?
What's the crux of the matter?

Reasons why insurers may need some time to take advantage of AI automation

Think of the strategic perspective - critical capabilities of your organization to enable company-wide AI automation

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?
lack of ai solutions
managing complexity of company architecture

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?
Based on our experience, we can help you face these challenges – see our service offer

Sollers Consulting insurance services connected with AI

We are helping insurers in the whole journey of AI-driven transformation

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
advisory for ai solutions
design of ai transformation

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
implemention of ai

AI solutions for the insurance industry

  • Inspektionen für die Schadenbearbeitung
  • Vorbereitung der Schadenentscheidung für den Sachbearbeiter
  • Automatisierte Bearbeitung von Dokumenten und E-Mails
  • Chatbots und Voicebots
  • KI-Echtzeitunterstützung für Call Center-Agenten
  • Automated processing of documents and emails​
  • Automatic insurance offer preparation​
  • Chatbots and Voicebots
  • AI real-time support for Call Center agents​
  • Inspections for claims handling​
  • Modernisation des systèmes hérités
  • Automatisation des tests système
  • Automatisation de la programmation
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.

Contact Us

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Piotr Pastuszka
Head of AI&Cloud
photo of Dominik Kamiński
Dominik Kamiński
Cloud&AI Lead

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