Banking & Financial Services

ESG Reporting: Harnessing the Right Data at the Right Time in the Right Form

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Author(s)

Ashish Manchanda

Director, Business Solutions, Banking and Capital Markets

Jeetendra Singh

Senior Director, ESG and Data Sciences

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Key Points

ESG reporting is gaining importance as banks and financial institutions broaden their objectives to include environmental, social and governance goals. This shift is driven by investor demand for enhanced transparency and a focus on long-term sustainability.
ESG reporting presents inherent complexities due to scattered data, challenges in quantifying social impacts and diverse interpretations of "green" initiatives.
To establish an effective ESG reporting strategy, banks and financial institutions need a robust data architecture, advanced data analytics capabilities and automated reporting complemented by intuitive visualization tools.

In 2019, approximately 14 percent of all client-generated revenues came from customers whose banking choices were influenced by their commitment to purpose and sustainability¹. The global pandemic has further amplified the imperative for banks and financial institutions to broaden their business objectives to encompass ESG (Environmental, Social, and Governance) goals. Recognizing that ESG goes beyond mere compliance, enterprises are now devoted to creating value through responsible behavior that benefits all stakeholders. ESG disclosures present an opportunity to foster long-term sustainability and profitability while fortifying resilience and adaptability in the face of regulatory and policy changes.

According to a recent Deloitte survey, 89 percent of executives are proactively pursuing ESG goals to demonstrate accountability, promote trust among stakeholders and better position themselves to differentiate in the long-term². Furthermore, there is a definitive shift in investors' leanings toward ESG, as evidenced by the PwC Global Investor Survey in 2022³. Investors now demand greater transparency in ESG reporting, coupled with enhanced financial discipline, reporting transparency and numerical reliability.

The Complexities of ESG Reporting

As the demand from regulators, investors and organizational leadership increases, financial institutions must re-think their approach to ESG data management and reporting. ESG data comprises financial and non-financial data, often scattered across various systems within organizations and even requiring data from external sources. While quantifying data related to the environment, such as energy consumption, carbon emissions and water wastage, can be achieved, reporting on social conditions such as human rights, labor rights, diversity and inclusion necessitates gathering information from performance reports by third parties, partners and suppliers.

Obtaining quantitative data alone involves significant effort. Collecting data and evaluating the impact of downstream activities, involving partners and the supply chain, can be challenging and hugely frustrating. Simultaneously, companies must also consider assessing the outcomes of transition activities, such as determining the environmental impact of materials and equipment used in constructing green buildings.

Additionally, different entities may have conflicting taxonomies for ESG. For instance, there is a need for clarity on whether an organizational initiative in nuclear energy or carbon capture should be considered “green.” Similarly, measuring and reporting the ESG activities of a fossil fuel company that has invested in renewable energy presents a unique challenge.

In the absence of a standard framework, companies typically establish their own metrics to measure and assess performance independently. These metrics are often guided by the need to meet rating organizations’ frameworks and comply with specific regulatory requirements relevant to their respective industries.

Designing an Effective ESG Reporting Strategy

Banks and financial services firms must prioritize the accuracy and reliability of data to facilitate outcome-oriented decision-making. For instance, accurately measuring and assessing carbon footprints in business operations will determine the future course of action. However, it is vital to address the prevalent issue of “greenwashing,” as 87 percent of investors believe there is a gap between reported data and its actual effectiveness due to poor reporting quality⁴.

Companies need robust IT systems designed to systematically aggregate, collect and report ESG metrics to meet investor expectations. The systems should be integrated with comprehensive risk assessment frameworks. Only via metrics-driven data will companies be able to take corrective measures, such as adopting new technologies to replace production systems with non-emissive technologies.

Designing An Effective Esg Reporting Strategy

Leveraging our expertise in business and digital technologies, we have helped numerous customers across sectors harness ESG data and create meaningful roadmaps to achieve strategic goals. Based on our extensive experience, a robust ESG reporting initiative must encompass the following elements:

Data Architecture: A data architecture designed to meet the goals of ESG reporting must be significantly enhanced to capture data from different applications within the organization, partner systems and third-party data sources. Integration with finance and risk frameworks is crucial. The architecture should encompass ESG-specific data from emission models, climate risk models, ESG scorecards and climate stress tests.

It is imperative to streamline and systematically gather enterprise data residing in silos across various departments and systems while also programmatically harnessing data from external sources. All data must be stored in a central data platform, preferably in the cloud, as a single source of truth. Stored data must be scrubbed, cleansed, transformed and cataloged for easy retrieval.

The data repository must have central ownership and oversight. It should be managed with a robust governance framework. This framework includes guidelines for accounting principles and data protocols aligned with regulatory requirements for data collection, integrity, security and access management. Automated data capture and monitoring tools help maintain data sanctity and provide visibility and traceability into any changes.

For instance, a third-party logistics provider created a central data repository with automated pipelines to extract data from enterprise systems, including Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), warehousing, inventory management, order fulfillment, shipping and transporting, and partner systems. The architecture encompassed environment management, governance and financial performance. External sources included sensors, weather forecasting and traffic reports. An embedded governance framework for high-quality data and access management policies orchestrated the seamless data capture for carbon emission, analysis and reporting.
Analytics: Data analytics provides rich insights by contextualizing structured and unstructured data. It enables an integrated view of supplier risks and compliance (including financial, operational and ESG aspects) by ingesting data from public sources to identify ESG hotspots and design a comprehensive risk-scoring heatmap.

Artificial Intelligence (AI) bolsters the organizational capacity to input enormous public data and design a realistic roadmap by separating the signal from the noise. At the same time, AI is helping companies better manage performance with comparative insights, align indicators with goals, classify spend and assess risks. These become crucial inputs to improve strategy, tweak indicators, funnel investments in the right direction and design risk-mitigation strategies.

A global pharmaceuticals and consumer goods company successfully monitored the sustainability of its suppliers by designing a data architecture that harvested data from the company website, analyst reports, and financial and operating metrics and assessed performance against benchmarks. The algorithm was designed to provide insights into ESG risk indicators, including financial, social, economic, compliance and governance.
Reporting: Automated reporting is essential to the data strategy with visualization and dashboards for quick reference via Application Programming Interface (API) integration. An intuitive portal supports easy navigation, and automatic report extraction maintains momentum. Reports can be granular, offering insights into specific activities at a global level or delving deeper into different countries, regions, sites and business lines.

Toward a Greater Good for Humanity

Managing and operating ESG data at scale is complex, requiring expertise to systematically gather and refine data, establish metrics, benchmark progress, iterate and collaborate with stakeholders to create impact.

ESG reporting is an ongoing journey that demands commitment and passion for continuously improving and adapting systems and processes. It begins with a strong data foundation and gradually expands technical capabilities to gain deeper insights. This collaborative approach necessitates the active participation of all stakeholders – employees, partners, suppliers and customers – to develop ESG goals and work together to achieve them.

To know how WNS is enabling global organizations to streamline ESG reporting with the power of data and analytics, contact us.

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