{"id":39138,"date":"2025-11-04T12:35:10","date_gmt":"2025-11-04T11:35:10","guid":{"rendered":"https:\/\/nr.no\/en\/?post_type=bc_industry&p=39138"},"modified":"2026-04-09T12:36:51","modified_gmt":"2026-04-09T10:36:51","slug":"risk-modelling-and-data-driven-insight-in-finance-and-insurance","status":"publish","type":"bc_industry","link":"https:\/\/nr.no\/en\/industries\/finance-and-insurance\/risk-modelling-and-data-driven-insight-in-finance-and-insurance\/","title":{"rendered":"Risk modelling and data-driven insight in finance and insurance"},"content":{"rendered":"\n
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At NR, we use statistical modelling and machine learning to solve complex challenges in finance and insurance. We help companies better understand risk, predict market trends and make more precise, data-driven decisions. Our work promotes innovation, efficiency and resilience in areas like risk assessment, credit rating and property valuation.<\/strong><\/p>\n\n\n\n

In collaboration with partners and clients, we develop models and tools that enhance precision, control and confidence in decision-making processes. <\/strong><\/p>\n\n\n\n

\"Downtown
Image: Benjamin Esteves \/ Unsplash.<\/figcaption><\/figure>\n\n\n\n

Risk modelling and machine learning<\/strong><\/h2>\n\n\n\n

We develop models for risk assessment, portfolio optimisation, time-series analysis, forecasting and anti-money laundering. Our methods provide decision-makers with deeper insights, stronger analytical foundations, and contribute to financial stability in volatile and uncertain markets.<\/p>\n\n\n\n

Machine learning is used to improve processes related to credit scoring, property valuation, and predictive accuracy. This gives banks and insurance companies a stronger basis for managing risk, understanding customers and setting strategic priorities. <\/p>\n\n\n\t

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Modelling risk for the insurance industry<\/strong><\/h2>\n\n\n\n

For many years, NR has collaborated with SpareBank 1, DNB, and Fremtind to develop methodologies for calculating Solvency Capital Requirements (SCR)<\/strong> under Solvency II – the EU framework designed to ensure financial stability and protect consumers.<\/p>\n\n\n\n

Our work also includes estimating risk premiums in non-life insurance, for instance through analyses of the relationship between climate and water damage for Gjensidige.<\/p>\n\n\n\n

We also develop models for predicting customer churn, detecting money laundering and insurance fraud, and analysing clickstream data to improve risk management and customer adaptation.<\/p>\n\n\n\t

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Explainable artificial intelligence (XAI)<\/strong><\/h2>\n\n\n\n

Artificial intelligence plays an increasingly important role in financial services, but explainability is crucial for trust and accountability. Our XAI solutions make it possible to interpret and explain AI-based decisions, ensuring transparency, safety and fairness.<\/p>\n\n\n\n

NR has a leading research environment in XAI, with expertise in LIME, Shapley values and counterfactual explanations. We have developed eXplego, a decision-support tool that helps developers select the most suitable XAI methods for their projects.<\/p>\n\n\n\n

Several of our clients are already using explainable AI to ensure fairness and transparency in their solutions. We are always open to new collaborations with partners and clients in this field.<\/p>\n\n\n\n

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Get in touch to learn more about our work in finance and insurance.<\/strong><\/p>\n\n\n\t

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Kjersti Aas<\/p>\n\t\t\t\t\t\t\t

Research Director SAMBA<\/p>\n\t\t\t\t\t\t<\/svg>\n\t\t<\/div>\n\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\n\n\t

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Our partners include<\/strong><\/p>\n\n\n\n