ESG Data Debate Chronicles: The Illusion and Reality of ESG Ratings (2024)

Over the past three years, the (re)insurance industry's engagement with ESG and climate ratings has intensified, highlighting the deficiencies in the ratings and the underlying data provided by existing suppliers - including those discussed in the 13 March 2023 edition of this newsletter. As a result, a rising number of organisations within our industry are proactively seeking help to identify the underlying data flaws. My initial recommendation is to confront the inherent biases that exist within each ESG data provider's approach - an in-depth discussion on climate data will follow in an upcoming edition of our newsletter.

Running the ESG Maze: When Subjectivity Meets Data Contradictions

In order to better understand these biases, let's take a look at common ESG data biases and their implications:

1. Inconsistent methodologies and metrics: As previously reported, different methodologies and metrics employed by each ESG rating agency can cause rating inconsistencies, complicating comparison and insight generation.
2. Size bias: Larger companies often score higher in ESG ratings due to their extensive resources dedicated to ESG reporting and initiatives. Conversely, resource-limited smaller companies might be under-represented in ESG ratings.
3. Industry bias: Several industries will inherently face more significant ESG risks, leading to lower ESG ratings compared to other sectors. This bias may not accurately reflect a company's actual ESG performance within these industries.
4. Geographical bias: Companies in specific regions may face different ESG risks and adhere to varied reporting standards, introducing biases in ESG ratings.
5. Subjectivity: ESG ratings involve qualitative assessments and analyst's subjective judgments, leading to potential rating inconsistencies and trust issues among stakeholders.
6. Data contradictions: ESG rating agencies often rely on diverse data sources, including company disclosures, third-party databases, government reports, and media coverage. This plethora of sources may contain contradictory information due to differences in reporting standards, definitions, and time frames.Moreover, self-reported data from companies may be prone to inaccuracies, green washing, or manipulation.It's worth noting that one agency predominantly relies on self-reported data from industry-specific questionnaires, and an in-depth review revealed a considerable disconnect between assigned ratings and corporate risks as perceived by (re)insurers.
7. Weightings: As previously reported, ESG rating agencies use different weightings for various ESG factors, potentially leading to misalignment with a company's specific risk exposure or strategic priorities.
8. Materiality assessment: ESG rating agencies may use different materiality assessments, which determine the ESG factors most relevant to a company's risk exposure and performance.
9. Data quality and reliability: The challenges in assessing data quality and reliability can affect the final ESG rating.
10. Model complexity: ESG rating agencies use complex models and algorithms to process and analyse large volumes of data, leading to potential issues with model transparency and interpretability. The complexity of these models can also make it difficult for companies to understand the underlying factors and weightings driving the ratings to and how their actions influence the ESG ratings.
11. Dynamic ESG risk assessment: ESG risks are often dynamic and can change rapidly due to factors such as regulatory developments, technological advancements, and stakeholder expectations.

These biases can have particularly pronounced impacts on smaller companies, putting them at a distinct disadvantage in several ways. Firstly, they may face difficulties in attracting capital, or they may be subjected to higher costs of capital due to lower ESG ratings. Secondly, these lower ESG ratings - due to resource constraints and not actual ESG-related risks - could lead to higher insurance premiums and limited coverage options, as (re)insurers may perceive them as higher risk, even when that may not accurately represent their true risk profile. Lastly, there's a potential for inadequacy in the risk management advice provided by brokers and select (re)insurers, particularly if their assessments are primarily based on these skewed ESG ratings. All these factors together illustrate the multifaceted challenges smaller companies face due to biases in ESG data.

Could a Risk Assessment of Our Own Uncertainty Be Too Meta?

In the face of these challenges, brokers and (re)insurance companies should consider adopting a more nuanced approach to ESG ratings. Here are some strategies to consider:

1. Develop a proprietary ESG assessment framework: Complement third-party ESG ratings with an in-house ESG assessment framework that incorporates standardised metrics, weightings, and methodologies.It is critical that these methodologies are continuously refined and updated to capture evolving ESG risks and align with emerging best practices. This will help a broker or (re)insurer differentiate itself by providing more accurate and insightful risk assessments.
2. Use multiple data sources: Utilise a combination of data sources, including third-party ESG data (not the ratings), company disclosures, regulatory filings, media coverage, existing underwriting data (which provide significant and existing ESG data insights) and specialised databases (the secret sauce!), to gather a comprehensive set of information on a client's ESG performance. This will help address data contradictions by providing a more holistic understanding of a company's ESG risks and performance.
3. Employ advanced analytics and AI: Leverage advanced analytics, machine learning, and natural language processing tools to efficiently process large volumes of structured and unstructured ESG data. These tools have always helped me create market leading insights as they support the identification of patterns, trends, and correlations in the data, which can be valuable in understanding the underlying ESG risks and making informed underwriting decisions.
4. Risk-based pricing and incentives: Develop a risk-based pricing model that accounts for a company's ESG risk exposure and performance. Offer incentives to clients demonstrating strong ESG performance or making significant progress in their ESG transition. This can encourage clients to invest in ESG initiatives and help mitigate ESG risks.
5. Develop a strong ESG research and advisory team: This team can provide clients with high-quality insights, guidance, and support for their ESG transition provide high-quality insights, guidance, support, and contribute to the creation of new (re)insurance products.A number of global banks and asset managers have been successfully deploying this strategy for years.
6. Engage with clients: Establish a proactive dialogue with clients to gain deeper insights into their ESG performance, risk management practices, and future plans. This engagement can help identify potential data gaps or inaccuracies in third-party ratings and enable a more accurate assessment of ESG risks. Regular engagement can also help monitor clients' progress on ESG initiatives and assess their commitment to ESG improvement as they navigate the challenges and opportunities of their ESG transition.
7. Support clients' ESG transition: Offering value-added services can enhance clients' ESG performance. An insurance policy alone is not enough to support transition!!!
8. Embed ESG principles in your organisation's culture: Lead by example by embedding ESG principles into your own organisation's culture, operations, and decision-making processes. Implement robust internal ESG policies, set ambitious sustainability targets, and transparently report on your company's ESG performance. Clients are requesting similar requirements from their supply chains and vendors, and they will expect the same from their (re)insurance partners.

Turning ESG Data Confusion into Long-Term Value and Success

ESG factors represent a transformation of risk for the (re)insurance industry, broadening the conventional scope of risk to encompass a more extensive range of elements that may influence a company's performance, value, and overall sustainability. While this brings challenges, it also generates new opportunities.

By implementing strategic actions to tackle the deficiencies of third-party ESG ratings and data, brokers and (re)insurers can enhance risk management, fine-tune underwriting decisions, aid in clients' ESG transition, bolster client relationships and capture new growth opportunities. The moment has arrived to transform the ESG data conundrum into a pathway for enduring value, benefitting brokers, (re)insurers, and clients alike.

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