How AI underwriting could fix the insurance industry

Back in the day, modern commercial insurance supposedly started with underwriters crunching premiums over coffee. Over the decades, those casual calculations evolved into heavy statistics and complex linear models.

Now, underwriting is facing another overhaul. The culprit is the manual grind: endless forms, buried fine print, and painful review cycles that test everyone's patience.

AI and machine learning are stepping into this bottleneck. According to Maruti Techlabs, the real game-changer is unlocking unstructured data, including text, speech, and images that traditional systems simply ignored. Underwriters can now tap into risk signals that used to be nearly impossible to gather.

Fixing the manual paper trail

Manual underwriting has a fundamental flaw: it is slow, resource-heavy, and prone to human error.

Evaluating risk variables by hand across mountains of paperwork often leads to pricing inefficiencies, missed details, and prolonged turnaround times. Customers dread the form-filling, and companies burn cash trying to keep up.

Automated systems help by ingesting massive datasets across varied formats before an underwriter even looks at the file. Pre-set models flag the patterns, and the human expert steps in to make an informed decision based on clean data.

Because machine learning algorithms are designed to learn from historical mistakes, they significantly cut down the time required for due diligence while keeping pricing consistent.

Chasing cyber risks and freeing human talent

Risk itself is also changing, especially as businesses migrate their infrastructure to the cloud.

Cyber threats and fraudulent claims evolve faster than rigid underwriting checklists can handle. Insurers are now deploying machine learning models to identify emerging fraud patterns and anticipate cybersecurity dangers before they strike.

This shift is also redefining what underwriters actually do day-to-day. Leading commercial carriers are retraining their staff to take on higher-value client relationships rather than sorting documents.

When low-complexity tasks are handled by software, underwriters have the bandwidth to design personalized account services, structure risk-sharing pricing models, and focus on customer retention.

Why it matters

For applicants and policyholders, this shift means fewer endless forms and significantly faster decisions. Instead of waiting weeks for a manual review, risk assessment can happen near real-time with pricing that actually reflects their specific posture.

For the insurance industry, integrating unstructured data raises the competitive bar. Carriers that rely on manual workflows will struggle to compete on speed, precision, and operational overhead against tech-enabled competitors.

We think using machine learning for intake and fraud detection is a brilliant operational move, but it carries real risks around transparency. When algorithms ingest unstructured text, voice, and images to price risk, explaining why an applicant was denied or quoted a higher rate becomes tricky, especially under strict financial regulations.

In our view, fully autonomous underwriting remains unrealistic. The winning formula looks like an AI-assisted setup: let the algorithms handle data parsing and fraud flags, while human underwriters retain final accountability and manage high-touch customer conversations.