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How digital solutions can power ‘Sustainable Banking’?

The financial institutions are struggling, in terms of knowing what are the best practices under ‘Sustainable Banking’ and implementing them

The 2016 Paris Climate Agreement identified low-emission financial flows as one of the main components for ensuring effective climate change mitigation.

While this declaration put private sector financial institutions in a key position to initiate green initiatives, the former, unfortunately, hasn’t made much progress in the last six years.

While the industry leaders have expressed support for the climate change goals, sustainability programmes of only a few financial institutions can meet the targets set by the 2016 declaration.

And it’s affecting the sector badly as two-thirds of consumers are thinking that their banks are indulging in “greenwashing” (an act of these financial institutions supposedly focusing more on promoting themselves as climate-conscious organizations rather than actually trying to lower their carbon footprint).

To execute their climate initiatives, these financial institutions need to incorporate solutions like artificial intelligence (AI) and advanced data analytics, as these breakthroughs set the sustainable banking initiatives at a steady pace without affecting the profitability factor.

As of now, financial institutions are spending money on green financing, inducting environmental specialists in the leadership ranks, and working with international communities to meet climate change goals.

Also, some of these institutions have even pledged to build carbon-neutral investment and lending portfolios by 2050.

However, they are struggling, when it comes to following through their commitments. 60 biggest global private banks invested some USD 742 billion into the fossil fuel industry in 2021, and when it comes to becoming carbon-neutral, nothing big has happened so far. There are factors within the finance industry that are proving to be hurdles for these banks.

One of the hurdles is the banks’ strong ties to non-sustainable industries, along with a lack of enforceable standards for those sectors. For example, JP Morgan on one hand is committed to meeting emission reduction targets, but they have investments in non-renewable energy companies. Even if the financial institution doesn’t meet its climate obligations, there is no law/legal watchdog to hold the company accountable.

While the nations are working on a war footing to create laws/guidelines enabling green banking, the topic is getting politicized as well. And due to the lack of these guidelines, the financial institutions are struggling as well, in terms of knowing what are the best practices under ‘Sustainable Banking’ and implementing them.

While these banks need to identify and implement strategic changes to keep their environmental commitments on track without affecting their profits, they must take the help of data-driven insights to formulate policies in this direction. This means adapting to artificial intelligence, machine learning, and data analytics should be the way ahead.

Despite environmentally sustainable businesses having the long-term profitability factor, for that to happen, data-driven decision-making is a must. Financial institutions need to maintain the balance between climate commitments and ensuring profits in their decision-making. Artificial Intelligence, machine learning, and data analytics solutions can be the game changers here, in terms of identifying green investments and loans with maximum profit returns.

While Artificial Intelligence can perform automated data analysis, machine learning and data analytics have the capability to generate economic and social inputs on these banks’ environmental initiatives.

One prominent example is that of geospatial data helping financial institutions identify climate-driven environmental changes which may affect the long-term viability of controlled assets, helping the banks to take quick decisions on divestment/reallocation matters.

Now market regulatory bodies are coming up with climate-specific stress tests to decide on the financial institutions’ ability to handle risks related to implementing projects supporting ‘Sustainable Banking’. This development will also now force the banks to do their own internal risk management assessments. This is where AI-enabled predictive modelling will come in handy, in terms of identifying climate hazards and risk sources on an industry-specific level. These data will help financial institutions come up with stronger risk management strategies, with specific focus areas and determined roadmaps.

Also, detailed sustainability reporting matters in ‘Sustainable Banking’, in terms of improving the accountability factor across projects. Data analytics can help financial institutions to undertake periodic green projects’ assessments. Generating such reports and sharing those in public spaces will help the banks to win investor confidence as well.

While the journey towards green banking isn’t a simple one, the banks also need to understand the fact that committing to the goal of ensuring net-zero emissions will require tonnes of time and most importantly, billions and trillions of dollars in investments. Accurate decision-making will be crucial here to maintain the balance between ‘Sustainable Banking’ and profitability.

With the customers now demanding more actions and fewer talks, it’s high time that the banking sector incorporates state-of-the-art technologies in its strategy formation process, in order to win on the confidence front.

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