Senior Vice President and Head of Global Consulting at ITC Infotech.
Global pressure for sustainability in the financial sector, driven by the need to achieve net zero emissions, is increasing demand for environmentally friendly practices in the banking sector. Financial institutions face increasing pressure to comply with strict ESG regulations, such as those from the European Banking Authority. mandate for climate disclosures from December 2023. To comply, they must implement robust risk management and regulatory reporting policies and procedures, requiring the use of intelligent analytics platforms to collect and analyze ESG data.
Many financial institutions have already integrated AI and analytics in various functions, leveraging machine learning algorithms to increase revenue, assess credit risk, improve financial operations, and much more. AI can now contribute to sustainable business strategies by effectively integrating ESG considerations, offering real-time scores, transaction analytics and risk assessments for informed decision-making.
Sustainable banking initiatives
Companies that actively conceptualize and implement their sustainability programs have a head start in the race to achieve their sustainability goals. Key areas for responsible banking initiatives include:
Sustainable products
Financial institutions have long actively integrated sustainability principles into their business strategies, offering ecological financial products, such as green auto loans, green savings and bonds, green mortgages, etc. Data analytics and AI/ML can be a game changer in designing a sustainable banking customer journey by enhancing existing products with sustainability features. This includes offering favorable conditions for purchasing sustainable goods with credit cards, implementing mechanisms to offset the carbon footprint and introducing green deposit accounts exclusively designed for sustainable investments.
Furthermore, the “climate-friendly” aspect of financial companies is increasingly scrutinized with the proportion of “climate-friendly loans, advances and debt securities relative to total assets“becoming an essential evaluation metric. AI-based solutions are emerging as powerful tools to help investors assess the financial prospects of companies alongside ESG (environmental, social and governance) factors.
Matthew Slovik, head of global sustainable finance at Morgan Stanley, strong points the importance of this integration: “The integration of AI into sustainable investing could mark a profound turning point in investors’ ability to navigate the complex web of ESG factors. By leveraging AI’s analytical capabilities, investors can identify companies with strong ESG performance, mitigate risks, and shape portfolios better aligned with sustainability goals.
Supply Chain Ecosystems
For a supply chain to be sustainable, it is essential to have a strong and effective rating system that prioritizes organizations that adhere to certain sustainability standards. Advanced analytics are needed to assess and manage ESG risks across the network.
For example, companies can assess the volume and categories of greenhouse gas (GHG) emissions contributed by each supplier to overall supply chain emissions and set policies that set minimum standards. Many major international banks, such as Citi Bank, Bank of America, DBS, Deutsche Bank and others, are actively engaged in sustainable purchasing practices. These practices aim to minimize waste, promote fair working conditions and contribute to economic growth. Additionally, banks are increasingly routing large funds to third-party providers, demand sustainability from suppliers as a key criterion for improving ESG accountability and transparency throughout the supply chain.
Regulatory compliance and ESG assessment
Implementing ESG in banking requires advanced technology and a nuanced understanding of the data points crucial for current and future ESG metrics. Although there is an abundance of data, is not standardized, making comparisons difficult, which decreases reliability and value. Gen AI solutions can automate data collection, organize dictionaries and metadata from internal IT systems and map them to an ESG data model. AI is particularly useful for discovering information from siled IT systems and various unstructured formats.
Sustainability Initiative Challenges
Measuring sustainability One of the main challenges in measuring sustainability is the lack of a uniform industrial framework. Existing frameworks such as the BEI (Business Environment Index) and TCFD (Task Force on Climate-related Financial Disclosures) are not universally adopted.
Unlike financial reporting with predefined parameters like assets, liabilities, net profit or EBITDA, ESG reporting does not have a uniform mechanism. The absence of such standards creates variability in approaches within the banking sector. Ongoing efforts by industry consortia aim to establish standardized procedures for ESG assessment, promoting a more consistent approach across the sector.
Data collection and management
Financial institutions typically obtain ESG data through open channels, information provided by companies or external providers. Gen AI can compile this data from various sources to build a reliable database for further analysis. However, scaling requires adapting IT systems to systematically collect, aggregate and report a wide range of sustainability data. Many institutions a comprehensive approach to integrating sustainability data is lacking in existing reports and act on sustainable development KPIs and KRIs.
Concerns about AI
AI in ESG assessment, while powerful, raises privacy concerns due to extensive data collection. Risks include the potential lack of reliability and accountability in AI-generated information, with the possibility of bias – algorithmic or human – creeping in without guarantees of transparency and accountability. Despite these concerns, AI’s learning and adaptation capabilities improve over time, resulting in better accuracy.
Conclusion
Sustainability and AI are two key trends shaping the future of financial services. Driven by ESG regulations, financial institutions are adopting intelligent analytics platforms driven by technology, data and talent. Although challenges exist in data collection and measurement, solving them requires a multidisciplinary approach, involving technological advances, regulatory compliance and a move towards more systematic and integrated frameworks for sustainability reporting and assessment .
As the financial sector addresses these challenges, AI and data analytics are emerging as both a solution and a central force, propelling the sector towards a more sustainable, responsible and innovative future.
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