Overview
Financial institutions play an imperative role in driving the transition to a low-carbon economy. One of the UK's largest banks recognized the need for a smarter approach to measuring financed emissions in its residential mortgage portfolio. The bank relied on energy performance certificates (EPCs) to estimate emissions. However, this method lacked accuracy, limiting its ability to provide targeted customer recommendations and track the real impact of decarbonization efforts.
To overcome these challenges, the bank partnered with HCLTech to rethink its emissions-tracking methodology. By integrating machine learning (ML) and advanced analytics, we developed predictive models that integrated multiple data sources, offering a more accurate and scalable approach to emissions estimation. With 85% accuracy, our AI-powered solution is now enabling the bank to improve customer targeting, unlock new green financing opportunities and accelerate its path to net zero.
The Challenge
Accurately measuring financed emissions requires more than just regulatory compliance—it demands a deep understanding of energy consumption patterns, socio-economic factors and property-level characteristics. The bank’s EPC-based estimation method presented major limitations:
- Inaccurate data representation: EPC ratings focused heavily on building efficiency, overlooking factors like energy sourcing, climate and behavioral influences
- Limited customer insights: The existing approach lacked the granularity needed to engage customers with personalized energy efficiency solutions
- Ineffective impact measurement: Broad, national-level estimates made it difficult to track decarbonization progress at a meaningful scale
To close these gaps, the bank needed a data-driven, AI-powered emissions tracking methodology that could deliver precise calculations, support targeted engagement and drive effective net-zero initiatives.
The Solution
We collaborated with the bank to design and implement a machine learning-powered emissions tracking solution that addressed the gaps in existing methodologies. Our approach included:
- Curating high-impact data sources: We analyzed over 150 datasets and identified 15+ authoritative data sources to ensure comprehensive coverage of energy consumption factors
- Feature engineering for accuracy: We performed Exploratory Data Analysis (EDA) on 70+ influencing factors, selecting 12 key features that significantly impact home energy consumption
- Developing predictive AI models: Using XGBoost, Deep Neural Networks (DNN) and regression techniques, we built ML algorithms capable of predicting home energy consumption with up to 85% accuracy
- Unlocking scalable insights: The solution enabled predictions for cost-carbon trade-offs, energy savings and high-value customer engagement opportunities
With this AI-powered framework, the bank evolved beyond generic EPC-based estimations, enabling smarter lending decisions and more precise sustainability tracking.
The Impact
Shifting to a machine learning-driven emissions tracking model gave the bank a sharper, more accurate view of its financed emissions while unlocking major business opportunities:
- 85% accuracy in emissions estimation: Transitioned from a broad-tier intensity model to a granular, AI-powered methodology
- $4 billion in new green financing potential: Identified six high-value mortgage customer segments for targeted energy efficiency renovation loans
- 200% increase in conversion rates: AI-driven targeting enabled highly personalized outreach, significantly improving customer engagement
- 35% boost in sales team productivity: Optimized data-driven strategies helped sales teams focus on high-impact customer interactions
- Real-time net-zero progress tracking: Shifted from annual emissions reporting to a daily tracking model for greater agility
HCLTech remains committed to helping financial institutions drive their net-zero strategies through AI and data science. Our collaboration with the bank has set a new benchmark for emissions tracking accuracy in mortgage lending, demonstrating how technology can turn sustainability goals into measurable outcomes.
As the bank continues to expand its green lending initiatives, our AI-driven framework provides the foundation for smarter, data-driven lending strategies—helping customers, the business and the planet move toward a sustainable future.
