Abstract

In recent times, environmental, social, and governance (ESG) data has started playing a crucial role in the banking and financial services industry.

While ESG data is essential in risk assessment and performance analysis, financial institutions face critical challenges in integrating ESG data into operations. The difficulties include data availability, data quality, data source quality, agnostic presentation of performance, and data governance. What makes the integration more complex and intriguing is that present-day ESG reporting is often suggestive and largely pursued to improve brand image rather than being a mandatory practice. With increased enforcement of social accountability in the industry, reporting is yet to be standardized, despite various standards and formats already present. We highlight the data challenges that entities face in assessing ESG performance and integrating it into mainstream operations and the role that technology can play in closing the gaps.

Seamless ESG integration

Finance drives economic growth

But improved incomes and returns at the expense of sustainability are catastrophic. In fact, positive ESG performance has a beneficial effect on corporate financial performance (CFP) through the elimination of penalties and compliance issues that affect brand equity. Renewed focus on ESG investing and lending, green bonds, green real estate, green mortgages, green trade finance, and sustainability-linked loans is improving ESG performance.

Global regulations on ESG, such as Sustainable Finance Disclosure Regulation (SFDR), EU taxonomy alignment, and Task Force on Climate-related Financial Disclosures (TCFD) for non-financial information, are slowly becoming mandatory. Further, the evolving directive on mandatory reporting for the Corporate Sustainability Reporting Directive (CSRD) will demand more robust sustainability reporting standards from companies than what the erstwhile Non-Financial Reporting Directive (NFRD) required. Consequently, investors, banks, and insurers are focusing on being ESG compliant now more than ever. Many companies and countries talk about their sustainability and net-zero strategies on their website and other publications. Lately, even end-consumers and individual investors have become ‘sustainability natives’ when it comes to their social, financial, or consumption habits. Thus, institutional financial investors have little choice but to become ESG-conscious while investing directly or indirectly. This applies to emerging crypto investments as well, where governance is a crucial element. The challenge is to derive meaningful insights from voluminous disclosures, enticing investor reports, and colorful web pages. Banks are facing a similar situation in lending, asset management, green financing, and circular financing. Given these rising sustainability aspirations, ESG reporting, disclosures, and associated data needs are also undergoing a transformation.

Facing data challenges

Financial institutions need a critical, unbiased, and transparent view of their ESG performance.

It is important for presenting a long-term view and avoiding misconceptions of deception and greenwashing. However, this is difficult to achieve as ESG performance in the commercial sphere has never been prescriptive. It has always been an incidental outcome of efforts, voluntarily pursued and disclosed. The reported sustainability impact is highly subjective, with performance data submerged in glossy text-laden reports that are open to interpretations and have less traceability. The absence of quantifiable and standard data further poses challenges of comparability, agnostics, and aggregation difficulties in terms of:

• Tackling non-performance in ESG

• Formulating sustainable strategies and plans for the future

• Providing investors with a true picture

• Managing risks effectively

An increase in new reporting dimensions and a plethora of agencies in the fray have only complicated the domain. Rating agencies use proprietary and hardly comparable methods to formulate ESG scores, resulting in apples-to-oranges comparisons while investing in funds, assessing lending proposals, and understanding the quality and ESG risk of assets. Data is either piecemeal in its lowest granularity or wholesome (aggregate or pillar level score). Today, bankers, investment analysts, and underwriters are interested in getting a custom handle on the ESG information of clients, customers, companies, and suppliers. Their focus is to analyze the data with their private risk parameters and analytical dimensions limited to their set of client profiles rather than getting bulk third-party ESG data topped with analytics bias. The following aspects further amplify the challenges:

Disparate data sources

ESG data sources include self-published reports of companies; commercial or subscribed data from data aggregators, rating agencies, and other industry and regulatory organizations; and social media. The primary need is a defined set of criteria to understand the authenticity of the data and qualify it. This is important, as at times, data from even a circumspect source can bring down the credibility of data in investment decisions, asset management, and underwriting.

Poor data quality

Data is present in formats ranging from quantitative reports to qualitative commentaries, making it non-homogeneous in terms of representations, units of measurement, and methodologies adopted in derivations. Data is highly relative but not always absolute. Companies often report data that is not time-synchronized to be comparable within their operations or with peers. ESG data feeds from social media further complicate the situation. Furthermore, data is not available from private companies, which form a sizeable clientele of financial institutions. This calls for the abstraction of data from macro-industry statistics, peer comparison, and the like. Without high-quality and authentic ESG data, decisions on lending, investment, underwriting, and reporting are bound to be defective, resulting in business losses, lack of opportunities, non-compliance, and a dent in brand equity.

Not agnostic

Third parties use proprietary algorithms to procure ESG data. Hence, it comes with an inherent ‘proprietary analyst bias,’ which may differ from the analytical perspective of financial institutions that use this data. As institutional strategies and perspectives change, there is a desperate need for subject matter experts to use granular raw data. The availability of an enabling platform powered with raw data provides enormous flexibility in the hands of research analysts and ESG experts. The platform would be suggestive of standard indicators in addition to building custom indicators on ESG performance.

Data is expensive

ESG data is available at a cost. In addition to a license fee, it comes with fixed conditions in terms of usage and distribution. Indiscriminate use of ESG data within financial institutions may result in substantial costs, which is why there are usage, storage, distribution, and geo-specific restrictions in place. 

Seeking order in chaos

Data is available through internal insights, external rating agencies, and real-time feeds.

Investment analysts, managers, and financial impact analyzers can synchronize the data to take meaningful decisions.

Without this, financial institutions defaulting in the decarbonization trajectory may face penalties and see themselves out of business in the future. Reporting ESG information and integrating into mainstream operations involves specific data sources, standardized data formats, and essential solution components (see Figure 1). To manage ESG data, financial institutions must:

• Implement an internal insights solution to handle granular preliminary ESG data and supplement third-party data and real-time ESG data feed with a 360-degree view of ESG performance. This necessitates a maneuverable ESG integration solution with data sources assimilation, data ingestion, storage, processing, quality management of both source and data, consumption and visualization, and governance.
• Implement seamless data ingestion from open data sources, company websites, published reports, and sustainability data providers through judicious use of AI and ML techniques and intelligent document extractions. Wherever operations are in the cloud, capitalizing on the gateways, marketplaces, and analytics hubs from cloud service providers will streamline data acquisition and governance.
• Procure data from all ecosystem players, such as traditional and paid ESG data providers, through APIs, which can enhance the richness of data. Since the data is ill-structured with gaps, it requires advanced tools and techniques for unobtrusive acquisition. AI and ML techniques are probable options for acquiring data from free text, graphs, tables, and infographics.
• Perform sentiment analysis to compare real-time ESG performance with past disclosures.
• Incorporate standardization based on most common metrics and missing data, bolstered by robust data logic.
• Enforce quality management (authenticity of sources, levels of abstraction, and data quality) to get near-perfect data.
• Manage source quality through assessment tools.
• Manage ingestion, distribution, and storage of information through robust data governance across the data pipeline.
• Establish baselines based on the latest available data. Critical use of baselines can help set meaningful targets for net zero journeys backed by strategies, action plans, and governance. 

Figure1: ESG integration data play