By Rohit Boda, Group Managing Director, J.B.Boda Group & Chairman, 0910 Holdings
The insurance industry no longer assesses risks only through historical loss patterns and static models. Across underwriting, claims, and portfolio management, AI has shifted attention towards what is coming to light in real time. Satellite imagery, live environmental data, behavioural signals, and connected systems are now shaping how exposure is understood, priced, and monitored. As the industry heads further into 2026, this shift will stand out more, with systems starting to initiate decisions rather than just assist them.
Market reports suggest that by 2026, about 40% of enterprise applications, including those used across financial services and insurance operations, are expected to widely adopt task-specific AI agents.
This evolution has brought sharper visibility into risk, while also altering how risk takes shape internally. Decisions are made faster. Patterns connect sooner. And responsibility often sits with people even when actions are triggered deep inside automated systems. For insurance providers, the question is no longer whether these tools are useful, but how their impact is understood, governed, and absorbed within existing risk and capital frameworks.
Tackling The Black Box Outcomes
One of the first pressures AI introduces is opacity. As models grow more complex, tracing how a specific outcome was derived becomes harder. Loss pathways could become challenging to trace, particularly when multiple systems interact across underwriting, claims, and third-party data sources. This complicates accountability when errors show up and makes internal decision-making harder to explain, challenge, and defend.
Among mature players, the response is not to slow adoption, but to strengthen governance. Clear documentation, defined decision boundaries, and explainable outputs are becoming essential. Accountability is being anchored at key points so automated decisions can still be reviewed, questioned, and explained when issues arise.
Managing Risk as Patterns Begin to Overlap
AI-driven tools are particularly effective at spotting trends early. However, at times, exposures can quietly cluster around the connected lines of business and geographies. This build-up is often gradual and easy to miss until losses emerge together rather than in isolation.
Leading firms are addressing this by pairing predictive capability with continuous monitoring. Greater emphasis is put on model stress-testing against unusual developments. Scenarios are designed to test what happens when multiple signals move at once, or when assumptions break simultaneously across portfolios. Importantly, these alerts are treated as prompts for judgment, not automatic action. The focus is less on predicting the exact outcome, and more on understanding where concentration could arise.
Keeping up with New Fraud Patterns
AI has also changed the fraud landscape. Manipulated documents, altered evidence, and identity misuse are moving faster than traditional checks were designed to handle. Claims validation, which was once largely procedural, now requires far closer scrutiny.
Here, AI is part of the solution as much as the problem. Pattern recognition, anomaly spotting, and cross-signal verification are helping insurers respond more quickly and with greater consistency. The firms need to recognise both sides of the equation by preparing for new fraud techniques while ensuring technology strengthens scrutiny rather than weakens it. Regular model review, escalation thresholds, and experienced claims judgment remain crucial.
Leadership is Changing in the AI Era
In today’s insurance space, leadership is no longer defined by capital alone. It is shaped by judgment, governance and discipline as complexity increases.
The firms moving ahead are treating AI as part of their core setup, not a side project. That commitment comes with real cost. AI systems demand sustained investment in data quality, monitoring, talent, governance, and oversight.
When tools are adopted for trend value rather than clear risk outcomes, they can introduce new exposures faster than organizations are prepared to manage. Just as importantly, it must be ensured that these systems can be explained, with clear data sources and decision logic that boards, regulators, and clients can understand and question.
Especially in the Indian market, this carries weight. Scale, data diversity, and underinsurance create room to innovate, but also raise the stakes. Solutions shaped for these conditions often prove relevant in other emerging markets.
Looking Ahead: Where This Leaves the Industry
What the industry needs now is better judgment alongside better prediction. Risk tends to break down when assumptions go untested and systems move faster than the people overseeing them.
With the right discipline, this moment offers an opportunity to build stronger approaches to risk, ones that value transparency, accountability, and human intelligence alongside automation. The question is not whether models will fail, but whether organizations can recognise the signs early and act in time.