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Generative AI for Sustainable Banking — Reducing Carbon Footprints and Promoting Eco-Friendly Spending
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Generative AI for Sustainable Banking — Reducing Carbon Footprints and Promoting Eco-Friendly Spending

Last Updated on November 5, 2023 by Editorial Team

Author(s): Balu Subramoniam

Originally published on Towards AI.

Cover Photo by Micheile Henderson on Unsplash


“The Earth has enough resources for our need but not for our greed.” — Mahatma Gandhi

In the face of the growing climate crisis, individuals and institutions alike are increasingly recognizing the need to reduce carbon emissions and adopt more sustainable practices. Banks, as financial intermediaries with a wide customer base, are in a unique position to encourage and incentivize environmentally conscious behaviors among their customers. Generative Artificial Intelligence (AI), with its ability to analyze data, provide personalized recommendations, and facilitate engagement, offers a powerful tool for banks to help their customers reduce carbon footprints from their spending transactions.

This article explores a few use cases where Generative AI can empower bank customers to make eco-friendly choices and also enable banks to offer incentives for sustainable behavior. It also provides a reference architecture using AWS services for building a sustainable banking application for these use cases.

I. Data Analysis and Insights

Generative AI can start the journey toward reducing carbon footprints by conducting a comprehensive analysis of a customer’s transaction history. It can categorize expenses into various carbon footprint categories, such as transportation, food, and energy. By doing so, it offers a clear picture of where a customer’s spending habits have the most significant environmental impact.

For instance, the AI can identify that a customer’s frequent use of ride-sharing services contributes significantly to their carbon footprint. Armed with this knowledge, banks can provide personalized recommendations to reduce this impact, such as suggesting carpooling, using public transportation, or switching to electric vehicles.

II. Personalized Recommendations

Generative AI can provide customers with actionable recommendations tailored to their spending habits. These recommendations go beyond generic advice and are rooted in the customer’s actual transactions, making them more relevant and likely to be adopted.

Imagine a scenario where a customer often dines out at restaurants known for their high carbon emissions. The AI could suggest alternative dining options with a lower environmental impact or encourage the customer to explore home-cooked meals. These personalized suggestions empower individuals to make informed choices without drastically altering their lifestyles.

III. Carbon Footprint Tracking in Real-Time

To truly impact behavior, Generative AI can calculate the carbon footprint of each transaction in real-time. This means that as a customer makes a purchase, they receive immediate feedback on the environmental impact of their decision. This feature can be seamlessly integrated into a customer’s banking app, making it easily accessible and actionable.

For example, when a customer buys a plane ticket, the AI can calculate the associated carbon emissions and display them alongside the transaction. This not only raises awareness but also encourages customers to consider alternative travel options with lower emissions.

IV. Incentive Programs

One of the most compelling ways banks can leverage Generative AI is by developing incentive programs for sustainable spending. Customers who actively reduce their carbon footprint or make eco-friendly choices can earn rewards. These rewards can take various forms, such as cashback, lower interest rates on loans, or discounts on green products and services.

Consider a customer who consistently uses public transportation instead of owning a car. The bank’s AI system can track this behavior and reward the customer with cashback or discounts on environmentally friendly products and services. This not only encourages sustainable behavior but also fosters customer loyalty.

V. Carbon Offset Integration

While reducing carbon emissions is crucial, it’s not always possible to eliminate them entirely. Generative AI can suggest carbon offset options, allowing customers to compensate for their emissions. These offsets may involve investing in renewable energy projects, supporting reforestation efforts, or funding other sustainable initiatives.

Banks can provide a seamless integration with carbon offset providers through their platforms. This way, customers can easily calculate the emissions associated with their spending and choose to offset them directly through their bank’s app or website. It’s a practical way for individuals to take responsibility for their carbon footprint.

VI. Gamification and Engagement

To make sustainable spending engaging and enjoyable, Generative AI can gamify the process. By setting challenges and goals related to carbon reduction, customers can earn points, badges, or other rewards as they progress. For example, achieving lower carbon footprint milestones could unlock additional rewards or recognition within the banking community.

Gamification not only encourages eco-friendly behavior but also fosters a sense of competition and achievement among customers. This can further boost engagement and inspire long-term commitment to sustainability.

VII. Educational Content

Educating customers about the environmental impact of their choices is a crucial aspect of reducing carbon footprints. Generative AI can generate educational content on sustainable living, providing customers with information on how different choices impact the environment and how they can make positive changes.

For instance, if a customer frequently shops online, the AI can provide information about the carbon emissions associated with shipping and suggest ways to reduce this impact, such as choosing eco-friendly shipping options or consolidating orders.

VIII. Feedback and Progress Tracking

Generative AI can offer continuous feedback on a customer’s progress in reducing their carbon footprint over time. By tracking and visualizing their improvements, customers can see the positive impact of their choices. This feedback loop can be highly motivating, encouraging customers to continue making eco-conscious decisions.

For instance, a customer who switched to a renewable energy provider can see how their electricity-related emissions have decreased over time. This visual representation of progress reinforces the importance of their sustainable choices.

IX. Community Building

Banks can foster a sense of community among their customers by creating online forums or communities where individuals can share their experiences and tips on reducing carbon footprints. Generative AI can facilitate discussions and answer questions related to sustainability.

These communities provide a platform for customers to support and inspire each other on their sustainability journeys. Moreover, the bank can actively participate in these forums, showcasing its commitment to environmental responsibility.

X. Predictive Analytics

Generative AI can use predictive analytics to anticipate potential future carbon emissions based on a customer’s spending patterns and external environmental data. By doing so, it can suggest preemptive actions to minimize the environmental impact of upcoming purchases.

For instance, if the AI predicts that a customer’s upcoming vacation involves a high level of carbon emissions, it can recommend options for offsetting these emissions or choosing more eco-friendly travel accommodations.

AWS Reference Architecture for a Sustainable Banking Application

AWS Reference Architecture by Author

Following is a brief overview of the AWS architecture for each of the functional components:

1. User Interface:

Customers can access applications globally from multiple devices (Web, Mobile, etc.) enabled by the following AWS services:

· Amazon Route 53 provides DNS routing to access applications from the internet.

· Amazon CloudFront distribute static contents (videos, images) and get dynamic responses (APIs) using Amazon’s CDN for seamless customer experience.

· AWS Amplify is the frontend and backend development platform for hosting, authentication, and serverless function deployment, for web and mobile applications.

· AWS API Gateway enables API management and exposes backend microservices securely.

· AWS Lambda provides serverless computing for executing backend logic based on the requests.

2. Core Banking Systems (CBS) Integration:

In banking, key internal data sources are real-time banking transactions and offline customer information stored in core banking databases. Following AWS Services are used to integrate with CBS to gather data for various features:

· AWS DMS is used to replicate offline data required for analytical purposes from CBS into AWS RDS (based on requirements, other suitable DBs can be substituted).

· AWS Kinesis Firehouse captures banking transactions for real-time analytics and predictions.

· Amazon S3 scalable data lake stores all the raw data from various sources for further processing.

3. Third Party Integration:

In banking, third-party data mainly originates from SaaS applications and third-party providers (like Amenity, SASB and RepRisk for Sustainability). Following AWS Services helps to integrate this data:

· Amazon AppFlow automates data gathering and cataloging from different SaaS (like Salesforce CRM).

· AWS Data Exchange enables to find and subscribe to more than 70+ sustainability datasets like Environmental, Social & Governance (ESG), Emissions, Weather and Satellite.

4. Data Transformation & Big Data Processing:

Data transformation and big-data processing are required to curate data for training Generative AI Models to generate predictions and insights. The following AWS Services can be leveraged:

· AWS Glue automates data transformation on the raw data from S3 data-lake and AWS RDS.

· Curated Data is staged in Amazon S3 for downstream AWS Services.

· Curated Data is also loaded onto Amazon Redshift data-warehouse for analytics & insights features.

· Amazon EMR is used for big-data processing, analysis using statistical algorithms, and predictive models — to find spending patterns, customer behavior, and personalized recommendations.

· Amazon Athena is used to prepare data for analytical dashboards from Amazon S3 and Redshift.

· Amazon DynamoDB (No-SQL Database) stores data for gamification, progress tracking, community building and carbon offsetting.

5. Generative AI Services:

Using the curated and transformed data, AWS SageMaker Service enables to develop, train, deploy and monitor Generative AI models. Following features of AWS SageMaker are used:

· Foundation Models (FMs), built-in algorithms from Amazon SageMaker Jumpstart.

· Continuously monitoring Generative AI model outputs using Amazon SageMaker Model Monitor.

· Manage ML workflow end-to-end (with CI/CD practices) using Amazon SageMaker Pipeline.

AWS announced newer Generative AI services like Amazon Bedrock, which provides access to FMs from Amazon and leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, and Stability AI. As of this blog writing, these services are in limited preview and awaiting general availability. When available, these services can also be easily integrated using APIs.

6. Insights and Notifications:

Customers get predictions and insights in the form of dashboards (scores, spending pattern), Workflows (actions, status) and Alerts (push notifications, text messages) using the following AWS Services:

· Rich data visuals and interactive dashboards embedded in Web and Mobile applications using Amazon QuickSight’s Embedded Analytics.

· Amazon Step Functions orchestrated workflow management and trigger notifications.

· Amazon SNS delivers alerts and notifications to customers via SMS and mobile push.

7. Authentication and Encryption:

Customer’s private data needs to be highly secure and compliant to security standards. Following are some AWS Services which can be ensure this:

· Amazon Cognito offers customer authentication (sign-up and sign-in features) and controlling access to web and mobile application features.

· AWS IAM to define and manage roles and access to data and resources in AWS and prevent unauthorized access.

· AWS KMS is used to generate keys to encrypt data for enhanced security.

8. Audit and Monitoring:

Customers needs to access banking services seamlessly. Regulations mandate banks to maintain audit and compliance controls with logging. These can be implemented using the following AWS Services:

· Amazon CloudWatch continuously observe, monitor and visualize AWS Services performance and alert/trigger automated actions.

· AWS CloudTrail continuously monitor events, user activity and access and logs them for audit purpose.


In an era where environmental consciousness is paramount, banks have a unique opportunity to facilitate positive change by harnessing Generative AI. Through AI-driven initiatives, banks can empower their customers to reduce their carbon footprints and make eco-friendly choices. These efforts not only benefit the environment but also position banks as socially responsible institutions that prioritize sustainability. Furthermore, this can foster stronger customer loyalty and engagement, as customers appreciate the value-added services that align with their values.

Banks that embrace Generative AI for sustainability initiatives are likely to see positive impacts on both their bottom lines and their reputation as responsible corporate citizens. By working hand in hand with their customers, banks can play a vital role in mitigating climate change and promoting a greener, more sustainable world.

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