Mikhail Grishin, Chief Operating Officer at Malaysia-based reinsurance company Mandarin Re, suggests one of the biggest values of artificial intelligence (AI) in reinsurance is its ability to provide greater clarity over underwriting portfolios, enabling firms to identify where underwriting time is best spent and improve the speed and quality of underwriting decisions.
In an interview with Reinsurance News, Grishin emphasised that AI’s most immediate impact for reinsurers is helping underwriters see their portfolios more clearly, starting not with new technology, but with the data companies already have.
He explained, “Before AI can tell you something you did not know, it can often show you something you should have seen already. That distinction matters more than many firms realise. Over time, this creates a structural inefficiency that is difficult to see from inside the operation.
“Underwriters are busy. Response times may be reasonable. Cases are being reviewed. But a meaningful share of that activity can still consume capacity without producing results, while the cases that genuinely deserve deeper engagement receive the same limited time as everything else.”
Grishin stressed that this is not a failure of effort, but a visibility problem.
He continued, “Not every submission represents a genuine opportunity. Some of what arrives in the pipeline is well matched to appetite, priced appropriately, and comes from relationships with a real track record. A significant portion may not. The challenge is that both categories often land in the same queue and receive the same level of underwriting attention.
“In a softening market environment, underwriting attention itself becomes a scarce resource. When competition increases and pricing pressure builds, the ability to understand where underwriting time should be focused becomes just as important as the ability to analyse the risk itself.”
Grishin revealed that at Mandarin Re, the firm ran a structured analysis of its submission flow using data from its underwriting management platform, combined with AI-assisted pattern recognition across the portfolio. The aim was to understand where underwriting capacity was actually going and whether that distribution made sense.
“What we found was that the quality of incoming flow depends less on geography alone and more on which broker is generating it. Some partners were producing high submission volumes with limited returns. Others, typically those with direct local market relationships and genuine cedant engagement, were consistently delivering results above our portfolio average. Refocusing on the right sources within the same geographies produced a measurable shift,” he said.
As a result, Mandarin Re restructured how it allocated underwriting capacity across its broker network, shifting focus and prioritisation.
“The outcome was tangible,” explained Grishin. “By concentrating underwriting time on higher-probability opportunities, we freed approximately 23% of underwriter capacity that had previously been absorbed by lower-return activity. Alongside that, our acceptance ratio and hit ratio improved meaningfully. The business we are writing is more aligned with our appetite, and the conversion from submission to bound case is more efficient.”
He added, “One of the clearest gains from combining AI with structured data has been in how submissions are handled before they reach the underwriter. Rather than arriving as a raw package requiring full interpretation from scratch, each case can now be pre-analysed against our internal underwriting criteria. The underwriter receives a structured brief: the key risk characteristics, the relevant parameters, and the areas that need closer attention. The analytical groundwork is done before the conversation even starts.
“This changes the quality of the underwriting decision, not just the speed of it. When the underwriter engages with a case, they are engaging with the substance of the risk rather than working through the mechanics of information extraction.”
Alongside this, Grishin noted that the firm has been investing in standardising how underwriting is reported and tracked internally.
“Consistent formats, data points and benchmarks matter. When every case and every period is captured in a consistent way, it becomes possible to see the portfolio clearly and make informed adjustments. Without that foundation, data analysis can produce noise rather than insight,” he said.
Grishin stressed that none of this replaces the underwriter. Risk assessment, relationship judgment and the final decision on terms remain with the underwriter; it is the environment around them that changes.
He continued, “AI and structured analysis can reduce the interpretation gap between what is happening in the portfolio and what leadership can see.
“They surface patterns that would otherwise take weeks of manual work to identify. They make it possible to ask specific operational questions and get answers quickly enough to act on them.
“In a business where decisions compound over years, and where poor risk selection may only become fully visible long after the fact, that kind of clarity is a genuine competitive advantage. Not because it removes complexity, but because it makes complexity more manageable.”
Grishin emphasised that firms benefiting most from AI in underwriting will be those building data discipline now, while the tools are still being adopted rather than assumed. This includes consistent data collection, structured analysis of what they already know, and the willingness to act on what the data shows.
He concluded, “We are still building, but the direction is clear, and the early results have confirmed that the work is worth doing.
“The future advantage in reinsurance may belong not to the firms with the most AI, but to those that achieve the clearest understanding of their own portfolios.”
