For a long time, underwriting was done entirely by hand. Underwriters used their expertise, past data, and intuition to assess applications. Given today’s pace of life and tech development, more customers expect digital options and faster service. It’s not surprising that manual underwriting can no longer keep up.

Then, automated underwriting came along to speed up processes and make them more consistent.

Finally, we have artificial intelligence and machine learning that are making significant changes in the insurance industry. AI and ML-driven systems have raised the bar in underwriting, driving major gains in efficiency, precision, and customer experience.

Here’s a look at these approaches in the underwriting landscape.

Comparing Key Elements of Manual, Automated, and AI/ML-Driven Underwriting

Risk Assessment and Data Utilisation

Manual underwriting relies mostly on the underwriter’s experience and intuition. It’s limited to historical and structured data, such as financial records or medical history. This method is slow and requires significant effort, but let’s face it—it brings that personal touch no technology can replace. 

When it comes to traditional automation, underwriting rules are coded based on fixed, predefined criteria—think credit scores, income, or claim history—that can be manually adjusted over time. It works well for straightforward cases, but it can fall short for more complex risk factors or unique situations. And that’s because this approach relies only on structured data and lacks flexibility to handle unusual risk factors or non-standard policies.

AI- and ML-driven underwriting brings in both structured and unstructured data, pulling from sources like text, social media activity, images, and behavioural patterns. AI models connect data in new ways, quickly identifying correlations between seemingly unrelated data points. This helps to reveal new insights about risk factors. AI and ML-powered underwriting can also automate routine tasks, such as data entry and preliminary risk assessments. In addition, it can develop pricing models based on non-obvious risk variables and client characteristics.

How about an example? An applicant’s credit score and medical history are what an old-school automated system focuses on. The way AI/ML-driven one does it is it goes further by, for instance, looking at predictive data—like a customer’s social media habits. This paints a far clearer picture of risk levels, giving underwriters a more comprehensive, data-rich perspective on each case.

Processing Speed and Efficiency

Manual and speed don’t exactly match, do they? So, naturally, it won’t come as a shock that manual underwriting is time-consuming and prone to delays. It usually means going through multiple rounds of review and approval, which can add even more time to the process.

In contrast, automated underwriting can process applications within seconds, making the process also more accurate. In traditional software automation, automated rules make quick decisions by following pre-defined thresholds. However, complex cases that don’t fit within the programmed criteria may still call for manual review, causing delays.

Then, there’s AI and ML underwriting that can handle applications in no time. Well, actually, in real-time (you get the picture), by adapting to each case without needing fixed rules for every scenario and with minimal human input. An AI model learns from past cases and can even flag those that need extra attention. This allows insurers to focus on the most important tasks. Additionally, Optical Character Recognition (OCR) and natural language processing (NLP) can swiftly capture and handle information from documents. This enables insurers to respond to customer inquiries faster, making for a better customer experience.

Error Reduction and Accuracy

As already stated, manual work definitely has the upper hand when it comes to the human touch, as it brings the expertise of a skilled underwriter. Yet, it’s also no stranger to errors due to the huge amount of data and the repetitive nature of some tasks.

While traditional automation reduces human errors by following set rules, it can overlook finer details, especially in unique cases. It still leaves room for mistakes and may reflect biases based on its programmed logic.

Artificial intelligence and machine learning technology in underwriting take cutting down on errors a step further. These systems learn from past data, constantly improving their accuracy. This is a massive help for underwriters, who are then able to proactively address areas of weakness and fine-tune their assessments. Plus, ML models can spot potential fraud (check out our article on counter fraud in insurance) and other warning signs way more effectively by recognising patterns that traditional methods might miss.

A quick comparison: Traditional automation might only detect fraud based on standard red flags, such as a missing document. But AI can dig deeper, spotting patterns of suspicious behaviour across multiple applications. Thanks to this, insurers can act before issues escalate.

The Evolving Role of Underwriters

As automation, AI and ML take on routine tasks, underwriters are moving into more strategic roles. This shift enables them to:

• Leverage data insights to make better-informed decisions rather than focus solely on risk assessment
• Work on complex, higher-value tasks, such as advising on unique cases
• Collaborate with data scientists to refine models
• Work with marketing teams to develop tailored products
• Improve their decisions with a data-driven approach
• Play a more active role in their companies’ digital transformation efforts, especially those equipped with skills in data analysis and AI tools. 

Speaking of digital transformation, you might want to check out our article on the shift from traditional insurance to InsurTech. It’s packed with valuable insights ;).

Customer-Centric Approach in Modern Underwriting

Growing automation and digitalisation in underwriting processes have delivered clear benefits to the insurance industry—both to insurers and their customers. These advancements enable more accurate risk assessments and faster turnaround times. They also create smoother, more transparent experiences for clients. All thanks to:

• quicker policy approvals and decisions,
• fewer intrusive questions in the application process,
• more tailored and precise risk evaluations.

The list of customer benefits of modern underwriting can go on. But at the same time, there’s a concern that this shift could lead to a less personal experience for customers.

The Challenge of Maintaining Human Touch

With AI and automation taking over routine tasks in underwriting, there’s a risk that the personal touch valued by many customers might get lost. This challenge shows up in a few areas:

• Less personal contact: With more aspects being automated, customers might have fewer opportunities to connect directly with human underwriters.
• One-size-fits-all responses: Automation systems tend to rely on templated messages, which can come across as impersonal and may fail to fully address a customer’s specific needs.
• Loss of empathy: AI and ML systems are fast and efficient, but they can’t offer the empathy and understanding that people can,  especially when things get complicated or personal.
• Challenges with unique cases: Automated systems may struggle with unusual or complex cases that need human judgment and a deeper understanding.

Balancing Automation and Human Expertise

Although the lack of (or at least limited) human touch in modern underwriting is a valid concern, it’s not something that can’t be addressed. By blending technology with human expertise and prioritising personalised, meaningful interactions, insurers can maintain and even improve the customer experience in this digital age.

Finding a middle ground between automation and the human element is key to successful modern underwriting. This way, insurers can leverage and enjoy the efficiency of automation while keeping the crucial human aspects of insurance processes—like understanding the context, using professional judgment, and personal connection.

The future of underwriting isn’t about picking between automation and human touch. It’s about combining the best of both to create a process that’s efficient, precise and focused on the customer.

Summary

Underwriting has come a long way, bringing a range of options for insurers looking to balance efficiency, accuracy, and customer satisfaction.

Manual processes offer a personal touch but can be slow and require a lot of resources. Traditional automation speeds things up for simple cases, but it can’t always adapt to more complex ones as it’s based on fixed rules. AI and ML can dynamically analyse massive amounts of data, learning patterns and updating risk assessments along the way. This brings unmatched accuracy, speed, and flexibility, allowing insurers to tap into sources they couldn’t before—like social media, location data, and behavioural trends. But getting started with these advanced tools takes some planning, high-quality data, and the right technical know-how.

That’s why it’s best to take a hybrid approach. By combining different underwriting methods, insurers can make the most of modern technology while still keeping the experience personal and customer-centric. The right way to approach underwriting lies in staying flexible, insightful, and responsive—meeting customers’ needs. Whether through manual review, automated systems, or AI and ML-driven tools,  the right mix will help underwriters deliver better results and achieve key business goals.

If you’re struggling with implementing technology into your insurance business and automating your processes, we’re here to help. Don’t hesitate to reach out. Let’s talk!