AI Data Monitoring for Underwriters: From Manual Rules to Autonomous Agents

Underwriting has always been about managing risk, but some risks are just unnecessary. That includes “manual driving” data governance in a world that increasingly requires autonomy. Leading insurers are moving away from the era of fragile, manual rule-writing backlogs and toward Self-Driving Data.

By deploying a coordinated system of autonomous AI agents, firms are no longer just monitoring data. They are now empowering an “always-on” digital workforce that proactively identifies anomalies, triages issues, and ensures that every underwriting decision is backed by absolute trust in enterprise data.

Trustworthy data is non-negotiable for underwriters

From liquidity analysis to claims history reviews and beyond, underwriting depends on data. Customer-provided information, forecasting datasets from government agencies, and private third-party databases can all be useful reference points for important underwriting decisions.

In many cases, these datasets are massive, to the tune of 100,000 columns and a billion rows. That’s an embarrassment of riches when you’re looking to make statistically sound decisions, but also a large liability if that data develops any anomalies.

At their least impactful, issues like data drift, typos, and dropped rows mean extra time in the decision-making process. But many data issues aren’t caught until after they’ve contributed to much more severe errors, such as suboptimal pricing and inappropriate approvals. These kinds of errors quickly degrade the professional trust that is so central to any underwriting business.

On the other hand, trustworthy data leads to accurate decisions, which in turn help strengthen relationships. This makes data health non-negotiable for any underwriting practitioner.

Outdated data monitoring practices leave results on the table

So how do you get to healthy data? For many firms, the answer used to be manual rule-writing. But those same firms are now seeing these legacy practices for what they are: outdated workflows that can’t scale to modern data needs. Manual rule-writing takes time, institutional knowledge, and strong SQL skills. Even then, it doesn’t catch every issue.

Most importantly, you can’t use manual rules to find issues you didn’t foresee (“unknown unknowns“). As datasets grow larger and more complex, the amount of these unforeseeable issues will only increase. Legacy practices simply cannot keep up with the reality of modern data needs.

Automated AI data monitoring unlocks better results, faster

The good news is that healthy, reliable data is more accessible than ever. Leading firms are now collapsing their multi-year manual rule-writing backlogs into a matter of months, with better end results. This success comes from leveraging automated AI data monitoring.

Automated AI data monitoring isn’t about using AI to generate more deterministic rules. It’s a fundamentally different way of assessing data health. These data monitoring tools learn your data’s patterns and find any kind of anomaly, including cross-column issues and unknown unknowns. They also take seasonality into account automatically, so normal peaks and valleys won’t be flagged. Broad automated coverage is paired with the option to add custom checks, so you can set up exactly the monitoring your table needs.

These modern monitoring solutions work for data across all your warehouses and data lakes, easily scaling to hundreds of thousands of columns without losing monitoring sensitivity. Automated AI data monitoring solutions also integrate with your existing data tools, like data catalogs and BI software. These integrations open up opportunities for teamwork: When you add data health information to the tools your team already uses, more teams can join in data governance efforts.

The age of self-driving data enables even easier workflows

Exciting new advancements in automated AI data monitoring are ushering in the age of self-driving data. Underwriting professionals can now expect the most advanced autonomous AI data agents to proactively surface key insights and provide troubleshooting assistance for data issues. Agents like these aren’t your typical chatbot; they use deep context about your data to help with everything from custom check creation to dashboarding, documentation, root cause analysis, and more.

Working with autonomous AI data agents is refreshingly simple. You don’t need a data science degree or decades of personal knowledge about a table, because every insight comes with context and is presented in natural language. And if you need more information, you can ask the agent to explain further or to run a further analysis.

Strong fundamentals (like broad automated check coverage and a wide range of integrations) are still necessary for a well-functioning data monitoring system, but they’re no longer the ceiling. When you’re deciding on a tool, ask whether your vendor is prepared for the self-driving data future.

Smaller features with a big impact

It’s also worth considering some less flashy features that can still make a big impact on your day-to-day workflow. The strongest automated AI data monitoring tools are built on years of enterprise data monitoring experience, which translates into a deep understanding of the features underwriting professionals really need, like:

• Intelligently-timed alerts and granular notification routing
  Underwriters already have enough on their plate. Smart alerting options like automatic notification backoff schedules and customizable multi-channel routing help reduce alert fatigue, so the alerts that do come in aren’t ignored.
• No-code and low-code options for custom checks
  Custom SQL checks are great for data practitioners with a strong coding background, but not so useful for everyone else. A large library of no-code and low-code check options democratizes data governance, allowing subject matter experts to contribute to data health efforts.
• Monitoring for all of your data types
  Comprehensive monitoring should cover traditional datasets like bureau feeds and credit attribute tables, but also unstructured data like PDFs, customer feedback forms, and non-tabular financial reports. After all, enterprise datasets aren’t confined to one type of data.

Anomalo’s innovative data quality monitoring solution provides all of the features we’ve covered here and more. It’s everything underwriters need for building confidence in their data.

How Nationwide increased data trust with Anomalo

Nationwide’s enterprise dataset measures in the thousands of databases 13,000 total, of which around 5,000 are in production. Their legacy monitoring system relied on manual rules, with the marketing team alone having more than 3,000 custom business rules to maintain.

Rolling out Anomalo was straightforward and immediately impactful. For Mike Randall, the Director of Enterprise Data Governance, it was “probably the fastest I’ve ever seen anything at Nationwide move” in his 31 years at the company.

Now, Nationwide can move faster and trust their data more. In an industry where trustworthy data closes sales, that’s a big win.

Learn more about how Anomalo can help your team

Anomalo was built from the ground up to support enterprise data health for financial services companies. Underwriters can benefit from sound fundamentals, numerous quality of life upgrades, and game-changing agentic AI abilities. All in service of trustworthy data, and all for the benefit of stronger customer relationships.