The insurance industry is facing a range of challenges that are significantly impacting its operations. Inflation, rising claim costs and severe natural disasters are driving up premiums and putting pressure on businesses. At the same time, customer loyalty is declining as consumers shop for lower rates, and a looming talent shortage threatens to leave key roles unfilled.
In this climate, technology such as artificial intelligence (AI) can help streamline operations, reduce costs and improve customer experience. To navigate these hurdles, insurers will need to find a balance of strategic pricing, innovation and efficiency.
Data challenges are holding back insurance innovation
A key challenge for the insurance industry is poor data quality and inconsistency. Many organizations struggle with unstructured data and siloed systems that make data difficult to process and analyze. This can limit analytics, automation and AI efforts.
Data complexity adds to the problem, as information across different lines of business, such as auto, homeowners and life insurance, varies significantly. Legacy systems and manual workarounds, such as entering data from forms or working in Excel, slow down operations even further.
While these methods may work in the short term, they’re not scalable or efficient. To harness the full potential of their data, insurers must modernize their systems, break down silos and streamline data management.
Data challenges across the insurance life cycle
Data challenges impact the entire insurance life cycle, affecting multiple departments and creating inefficiencies. Key data related obstacles often include:
Underwriting: Complex products and varying data sources lead to inconsistencies. Manual and partially automated data entry further complicates the process.
Claims handling: Reliance on manual decision-making and legacy systems slow down claims processing.
Policy and claims administration: Many organizations continue to rely on outdated platforms and legacy systems, which hinder their efforts to modernize.
Financial reporting: While automation is improving in some areas, many departments still rely on manual processes, limiting real-time insights.
Legal and compliance: Unstructured data in contracts and documents makes it difficult to extract insights and ensure compliance.
Reinsurance and actuarial: Despite being data-driven, actuarial work is still often done manually in Excel, slowing down decision-making.
Data challenges cut across the entire insurance value chain, from underwriting to claims to compliance. To unlock the full potential of data, insurers need a unified approach to modernization, automation and data management.
Harnessing AI to unlock unstructured data in insurance
Unstructured data offers significant untapped value for insurers, yet it remains largely underutilized. Unlike structured data such as customer records or transaction volumes, which can be easily organized and searched, unstructured data exists in formats that are more difficult to manage and analyze. Examples include handwritten notes, claims notes, sensor data and even social media posts.
By leveraging AI and advanced technologies, insurers can unlock insights from unstructured data sources, turning them into valuable assets. Key examples of the AI capabilities that can transform unstructured data include:
Generative AI: Creates content based on user input, enhancing productivity. It can be used to draft emails and generate PowerPoint slides using tools like ChatGPT.
Predictive AI: Analyzes data to predict future outcomes and trends. It can generate product recommendations or assist with predictive maintenance.
Computer Vision AI: Examines images to identify patterns or anomalies. It's used for damage detection, quality control and medical imaging.
Decision Automation AI: Automates decision-making by combining generative, predictive and vision AI. It handles exceptions and enables autonomous process decisions to enhance human judgment.
Key AI use cases for insurance organizations
Leveraging an AI solution in the following areas can drive significant value for insurers:
Data integration and standardization: Automates the consolidation of data from different systems, simplifying information sharing and comparison across business units, to help standardize and enrich data to break down data silos.
Knowledge management: Streamlines access to domain-specific information, improving consistency and supporting efficient onboarding for new employees and resolutions to customer requests.
Document and image comprehension: Extracts insights from text, images and complex Excel files, ensuring data consistency and simplifying analysis.
Automating decision-making: Enhances workflows by guiding routine decisions, improving efficiency and freeing up resources for higher-value tasks.
By applying AI to these key areas, insurers can improve operations, enhance decision-making and unlock new growth opportunities.
Transformative possibilities with AI
When considering AI implementation in insurance, it’s important to focus on areas where the technology can deliver quick wins and measurable improvements. Here are some key opportunities across different departments that can help unlock the potential of AI:
Underwriting: AI enhances risk evaluation by analyzing unstructured data, offering a deeper understanding of customers for more accurate underwriting decisions.
Fraud detection: AI helps identify fraudulent claims earlier by analyzing patterns and detecting anomalies, reducing risk and improving claims processing efficiency.
Claims processing: AI streamlines claims by quickly analyzing submissions and automating routine tasks, improving efficiency and accuracy.
Legal and compliance monitoring: AI improves compliance checks by extracting information faster and allowing for more frequent reviews. For example, monthly checks instead of annual checks help ensure ongoing compliance.
Our approach to AI implementation
At Baker Tilly, we take a structured, iterative approach to AI implementation, ensuring that solutions are tailored to your organization's unique needs and deliver measurable business value.
Here's an overview of our process:
Discovery: We begin by aligning AI initiatives to your business goals, identifying key metrics and mapping out business decisions that make up the workflow. This helps us pinpoint areas where AI can have the most impact.
Pilot: We test selected use cases on a small scale, validating assumptions about data, AI approach and business value. If needed, we pivot to other use cases to find the most effective solution.
Scaling: Once the pilot proves successful, we expand the solution, address training, adoption and change management areas as well as build the infrastructure needed for full-scale deployment, with a focus on data security and governance.
Optimization: As the solution matures, we work on refining the AI solution, reducing costs and improving performance. We also optimize energy consumption and operational efficiency to ensure long term sustainability.
Throughout each stage, we emphasize the iterative nature of AI projects. As new insights emerge, we return to the discovery phase, adjusting our approach based on findings from the pilot or scaling stages. This allows for continuous improvement and ensures that AI solutions evolve in alignment with your business needs.
Key technologies in AI implementation
We use a variety of technologies to drive AI success for our clients. These technologies form the backbone of our AI solutions, ensuring they are efficient, scalable and cost-effective:
Document storage: AI tools help with organizing and searching through documents, making data easily accessible across sources.
Foundational models: We use cloud providers offering various foundational models, selecting the most effective and cost-efficient options for each use case.
Human-in-the-Loop (HITL): While AI automates tasks, human oversight ensures accuracy for edge cases and subjective decisions.
Reporting and monitoring: Continuous reporting on compliance, accuracy, data drift, bias and fairness to ensure AI solutions stay aligned with business goals and in compliance with AI laws.
High-impact areas for AI deployment
If you’re considering where AI can provide the most value within your organization, these are the high-impact areas we recommend:
Data extraction: AI automates the extraction of key data from unstructured sources, making it usable for downstream processes like onboarding.
Decision automation: AI-driven decision automation improves efficiency and accuracy by integrating data from multiple sources, with the potential to create configurable, compound AI systems.
Customer service workflow: AI enhances customer service by making domain-specific information easily searchable and integrating it across systems, improving response times and accuracy.
How we can help
Baker Tilly’s team of digital and insurance industry specialists understand the unique challenges the insurance industry faces and how AI can be a game changer in driving operational efficiency, reducing costs and improving customer experiences. To unlock the full potential of AI, it’s essential to have a clear strategy and strong execution plan in place.
Ready to explore how AI can transform your insurance business? Contact us today to learn more about how we can help you drive innovation and achieve measurable results.
Digital transformation in the insurance industry webinar
Below you will find the presentation and recording from our recent webinar, Digital transformation in the insurance industry: Embracing artificial intelligence to drive innovation and efficiency. For more information on the subject, and to learn more about how we can assist your organization with its AI strategy, refer to our artificial intelligence and insurance webpages.
