At CeFPro’s recent Sustainable Finance conference, SMBC's modeling team warned that banks face severe challenges in sourcing usable data for climate risk modeling. AI may offer game-changing tools, but without strong governance, it introduces its own risk
For years, banks have talked about integrating climate risk into financial modeling, but it is only now, in the face of pressing regulatory deadlines under rules such as those included within the Corporate Sustainability Reporting Directive (CSRD) that the conversation is reaching a critical point.
However according to risk leaders within the EMEA division of the Sumitomo Mitsui Banking Corporation (SMBC), most banks still want for the one thing that, they say, is essential to success: good data.
"As financial regulations like CSRD demand greater transparency, one of the biggest hurdles we face is not only to understand climate risk, but to find good data to model it effectively," said Ying Poikonen, SMBC’s Head of Modelling, during CeFPro's recent Sustainable Finance conference.
Her colleague, Jing Zhang, who leads climate risk modeling, also used the session on the challenges of finding quality data to outline how the bank’s journey began in 2019 with Prudential Regulation Authority (PRA) guidance.
She explained that early scorecard models leaned heavily on expert judgment due to data scarcity, but went on to describe how, over time, a more structured approach emerged—identifying risks, mapping exposures, and quantifying financial losses.
Each step brought new data requirements: from carbon pricing and decarbonization pathways to hazard-specific damage ratios and climate-adjusted macro forecasts.
But the reality is that in real-world modeling, finding that data remains a massive challenge. Public datasets like NGFS or IPCC are free and credible but require extensive transformation.
"It can be very time consuming and technically demanding," Zhang warned. Commercial vendors offer ready-to-use metrics but bring cost, quality, and consistency concerns – especially for smaller institutions.
The session also highlighted additional issues around the application and interpretation of data, with many organizations in the financial services industry struggling to convert climate indicators into financial impact.
Translating rising temperatures or policy shifts into monetary losses requires complex functions and assumptions, and as Zhang noted, banks are often forced to use damage ratios as ‘haircuts’ to asset values – a workaround that, while necessary, remains imprecise.
This, Poikonen argues, is where artificial intelligence enters the equation as a solution that, she believes, could dramatically improve ESG data quality.
