If you are an underwriter, you might have encountered questions from curious (or furious, maybe) customers, like “How long?” And the frustration is real, at least from the conversations I found on forums like Reddit. When seen from an underwriter’s POV, there are genuine reasons for the delays – slow processes, communication gaps, data silos, and many others, despite the digital adoption. But why does this all matter?
If you observe the top- and bottom- tier insurers in the US and UK, loss ratio is the most significant factor impacting their operating performance. It’s evident that underwriting plays a fundamental role in an institution’s profitability and performance. Underwriters deserve more powerful technology assistance (AI agents, to be precise!) to rise above these challenges to meet the evolving customer- and industry- demands.
What is an AI agent in underwriting?
An AI agent is a software system that performs tasks on your behalf to achieve a goal. They have the ability to reason, plan, and memorize to execute tasks without human intervention. Agents learn from interactions, adapt to changes in the workflows, and make decisions. That said, they work for you under your rules.
You can create specialized AI agents by training them on large sets of underwriting data and your enterprise data. You can integrate them with tools and systems and enable them to perform specific tasks. For example, you can integrate an AI agent with your email platform to automate email communication.
What is a multi-agent system in underwriting?
Typical underwriting process has a series of tasks, like data collection, verification, risk assessment, pricing, decision making, communication, etc. In a multi-agent system, specialized AI agents collaborate with each other and perform the tasks to execute the process autonomously – a phenomenon we call Agentic Process Automation (APA).
Why do you need multi-agent AI for underwriting automation?
Slower processes is a major challenge in underwriting automation, be it in Insurance, Banking, or Investment banking. You might already be using digital tools offering great capabilities to assist you in the process. Multi-agent approach brings a new layer of intelligence, autonomy, and coordination that traditional tools can’t match on their own. Let me highlight the major challenges with the existing approach.
Fragmented systems
Fragmentation is a big foe in any enterprise scenario. I’m sure you have a dozen tools and systems in your quiver. You need to toggle between them to piece together a complete picture. This switching between systems and searching for data alone takes up to 25% of your time in a workday.
Data quality
Data quality in underwriting is a chronic challenge. Although you use OCR tools, you still need to manually verify and validate the extracted data from structured and unstructured sources. Reports found that 40% of the time goes into data validation before using it to make any strategic decision.
Delayed communication
You get a ten-mile-long list of things to do and a dozen contacts to coordinate with. If anyone in this loop misses to do their part or delay to deliver, it will hold up the entire process. Manual follow-ups and emails between the stakeholders often result in untracked or missed responses and delay the process.
Lack of real-time intelligence
Most of the tools you use don’t integrate real-time data, such as latest regulations, market risks, or updated medical information. The outdated information might lead to inaccurate risk evaluations and increase reworks.
Limited automation
The traditional systems only automate parts of the process. For example, you can automate data extraction and filling up forms. But these workflow automations are rule-based. They can handle only the structured and predictable scenarios. When met with a unique scenario, they might fall apart and need human intervention.
With multi-agent systems, you can transform the underwriting process by creating a connected, autonomous ecosystem that works round-the-clock, utilizing all the essential tools, learns from interactions, and requires human intervention only when truly needed.
We ran several proof-of-concepts to test multi-agent systems in underwriting. We observed that agents can reduce manual work by ~60%. Based on your business scenario, the gains can go up to 80%.
You can see AI agents in action for underwriting automation in your organization. Reach out to us for tailored live demo.
How multi-agent systems transform underwriting automation
A multi-agent system breaks down a complex process into simple steps or tasks and assigns each task to an agent. In underwriting, the typical workflow could be:
- Data extraction
- Data validation
- Risk assessment
- Decision making
- Communication
For each task, you can have a dedicated agent in the system. Well, the flow might be different in your organization. How you orchestrate the system completely depends on your use case. There is no hard and fast rule about the tools and models to be used in the agentic automation solution.
Deploying agentic AI doesn’t mean doing away with your existing tech stack. You integrate these tools and systems with the agents to enable autonomous automation. This means the tasks get executed automatically without a command or trigger from your end.
You can train the agents on your industry- and enterprise-specific data to give them domain specific knowledge and skills.
You might be concerned about agent autonomy, but you can maintain complete control by setting clear rules for decision-making in specific scenarios and defining when to escalate to humans.
Let’s see what type of agents you can have in agentic underwriting automation and how they help you accelerate the process.
Data extraction agent:
The data extraction agent automatically captures relevant documents from email and other sources. The agent is equipped with tools required to convert the unstructured data into structured data.
Data validation agent:
AI agents connected with government databases, EHR systems, CRM, etc., can automatically verify customer details. They identify inconsistencies and anomalies in the data and alert underwriters. With the help of the data validation agent, you can ensure data accuracy before processing.
Risk assessment agent:
Underwriters need to evaluate risk by factoring in applicant’s credit history, health records, and other important personal details. Risk assessment is often influenced by human bias and preconceptions. False positives and false negatives are high with manual risk assessment. This leads to poor customer satisfaction and negative impact on profitability. There are changing regulations that underwriters continuously need to be up to speed.
You can integrate AI agents with advanced ML algorithms trained on large data sets to evaluate risk unbiased. The risk assessment agent generates customer risk score more accurately and faster. The agents can have real-time access to external data sources to get latest information for specialized knowledge.
Decision-making agent:
Poor decisions negatively impact the quote-to-bind ratio. Decision-making factors in the customer risk score, industry standards, and company rules. AI agents use complex algorithms to make the right decisions faster, balancing the profitability and affordability of the product while mitigating risk.
Communication agent:
AI agents connected with PDF generation, email, and other tools automatically communicate information with the stakeholders and customers instantly, with tailored messaging. By breaking communication bottlenecks, the communication agent helps you save time, boost collaboration, speed up the underwriting process.
| Underwriting Aspect | Manual Underwriting | Multi-Agent System |
| Data Intake & Processing | – Data arrives in various formats, requiring manual entry. – Takes days to extract and structure data. | – AI agents auto-capture, extract, and structure data. – Minutes instead of days for data processing. |
| Data Validation | – Manual validation is slow and error-prone. – Hours to days for verifying applicant details. | – Integration with external sources for instant verification. – Real-time validation, reducing approval time significantly. |
| Risk Assessment | – Human bias leads to inconsistent results. – False positives/negatives increase risk. – Days to weeks for thorough risk evaluation. | – AI/ML-driven risk scoring with continuous updates. – Minutes instead of weeks for precise risk assessment. |
| Decision Making | – Manual analysis of risk scores, policies, and industry standards. – Takes days to finalize decisions. – Affects quote-to-bind ratio. | – AI agents analyze data and make recommendations instantly. – Decisions in seconds, improving quote-to-bind ratio. |
| Communication & Collaboration | – Manual follow-ups with stakeholders cause delays. – Weeks of back-and-forth communication. | – Automate notifications and status updates. – Seamless, real-time communication, cutting response time drastically. |
Want to get started with multi-agent systems for underwriting?
You don’t have to build models and agents from scratch. Digital ClerX, a new-age vertical AI agent accelerator, enables you to quickly deploy multi-agent systems to augment the underwriting process and enhance business outcomes. The accelerator offers customizable AI agents and workflows to meet your specific needs.
Talk to us and see how Digital ClerX can transform underwriting in your organization. [Book live demo]
Frequently asked questions (FAQs)
What is underwriting?
Investopedia defines underwriting as the process through which an individual or institution takes on financial risk for a fee.
What is Agentic Process Automation?
Agentic process automation is an approach that uses AI agents to execute underwriting tasks autonomously, minimizing human intervention.
How is agentic process automation better than traditional automation?
Traditional automation (like RPA) handles repetitive tasks, but MAS adds autonomy, collaboration, and intelligence. Each agent can make decisions, learn from data, and interact with other agents or systems to adapt to complex scenarios.
What underwriting tasks can MAS automate?
MAS can take over:
- Document intake & data extraction
- Data validation across systems (CRM, medical records, etc.)
- Risk analysis & scoring
- Quote generation
- Exception handling
- Stakeholder communication
- Compliance checks
- Decision-making triggers
Can these agents integrate with our existing tools?
Yes. AI agents are built to connect with popular underwriting tools and internal databases via APIs. They work with your systems, not around them.
What are the biggest risks or challenges in multiagent systems for underwriting automation?
- Ensuring data security and regulatory compliance
- Designing agents to handle exceptions and real-world variability
- Change management for teams adapting to a new way of working
