The rapid growth of renewable energy projects worldwide has created a pressing need for effective refinancing strategies. Renewable energy finance supports the development, deployment, and operation of clean energy sources such as solar, wind, hydro, and biomass. Refinancing these projects – restructuring or replacing existing debt or equity to improve the financial terms – is a complex process that involves a network of stakeholders, risks, and opportunities. Key factors that make renewable energy refinancing unique include:

  • Complex project structures, including power purchase agreements (PPAs), tax incentives, and long-term contracts.
  • Long-term revenue projections based on both energy generation and sales, necessitating accurate cash flow forecasting.
  • Limited historical data due to the sector’s relative novelty, making pricing and terms determination challenging.

As the industry continues to evolve, artificial intelligence (AI) integration and digitalization present a significant opportunity to enhance the efficiency and effectiveness of renewable energy refinancing. Addressing data reliability issues, reducing costs, and improving decision-making are among the benefits AI and digitalization bring to renewable energy lenders and developers alike, enabling adaptation to market challenges and further unlocking capital to deploy toward new opportunities. 

Technology-Driven Renewable Energy Refinancing

The need for renewable energy project refinancing arises from existing expensive short-term debt, fluctuating interest rates, expansion efforts, and sometimes even impactful regulatory changes. Current refinancing methods involve extensive manual processes that are time-consuming, error-prone, and costly. Leveraging AI technologies, such as machine learning and predictive analytics, can significantly improve the efficiency and profitability of renewable energy refinancing. AI will contribute to:

  • Improved risk assessment: AI algorithms can analyze vast amounts of data from various sources, such as financial records, energy production data, and market trends. AI can identify patterns and predict potential refinancing challenges. Leveraging AI-powered risk assessment models  enables organizations to make more informed decisions, mitigate risks effectively, and optimize the financial terms of renewable energy projects. AI algorithms can assign risk scores to renewable energy projects based on financial stability, project location, technology used, and regulatory environment. 
  • Accurate cash flow projections: AI-powered models can examine project performance data, market conditions, and other relevant factors to generate cash flow projections. These projections enable stakeholders to evaluate refinancing options more confidently and optimize financial outcomes. Improving the cash flow projection accuracy provides stakeholders greater visibility into the project's viability, increasing their confidence in refinancing decisions.
  • Portfolio optimization: AI algorithms can assist in identifying the optimal refinancing strategy across a portfolio of renewable energy projects. By considering various factors such as interest rates, maturity dates, and project performance, AI can provide valuable insights to maximize returns. This portfolio optimization capability allows stakeholders to allocate resources efficiently and to refinance multiple projects simultaneously.
  • Cost reduction: AI and digitalization can significantly reduce costs associated with renewable energy refinancing. Re-financing costs can significantly impact the overall financial viability of renewable energy projects. These costs typically range from 1% to 3% of the total loan amount and include expenses such as legal fees, administrative costs, financial advisory services, and potential consulting fees. While actual cost vary based on project size, complexity, prevailing market conditions, and specific refinancing requirements, AI-driven margin improvements of 1% or more would represent a significant advancement. 
In the context of refinancing, AI has potential benefits for both lenders and asset owners. AI-powered risk assessments, for example, will help lenders make informed decisions about loan terms, interest rates, and overall financing conditions. But these same tools can be used by asset owners, enhancing the company's ability to secure refinancing by presenting a compelling case to lenders.

For lenders, it’s worth noting that AI use cases in corporate refinancing will be quite different from AI use cases in areas like consumer lending. In consumer lending and small business financing, the sheer volume of loans and the simplicity of relevant datapoints will increasingly enable AI-powered auto-decisioning. Corporate refinancing decisions present unique challenges due to the complexity of financial data, bespoke deal structures, the involvement of numerous financial experts, the need for in-depth risk assessment, and a complex regulatory environment. For corporate financing in general and renewable energy financing in particular, AI will not enable auto-decisioning for the entire project, but rather will provide clear directional guidance related to discrete aspects of the proposed refinancing effort.

The Transformative Opportunity with AI

The integration of artificial intelligence and digitalization presents a transformative opportunity for renewable energy refinancing. However, three critical challenges must be addressed to fully harness this potential. 

First, robust data privacy measures are essential to safeguard sensitive project information from unauthorized access or breaches. Ensuring data security is paramount to maintaining stakeholder trust and protecting valuable data assets. Additionally, AI and digitalization should seamlessly align with relevant regulatory frameworks governing renewable energy refinancing. Compliance with legal requirements not only mitigates potential risks but also ensures ethical practices throughout the process. Finally, while AI enhances decision-making processes, human expertise remains indispensable. Stakeholders must strike a delicate balance, leveraging AI capabilities while relying on human judgment to make informed refinancing decisions.

As the renewable energy industry continues to evolve, embracing AI and digitalization will be essential in unlocking the full potential of renewable energy refinancing. The energy transition will be an extremely capital-intensive effort. AI is well poised to give renewable energy developers, asset owners, and lenders the insights required to effectively unleash capital at scale and drive sustainable growth.

About the Authors

Phani Solomou
Senior Partner

Phani is Wipro’s AI Consulting leader for Europe. She has over 25 years of advisory experience on business transformation working with clients across sectors. She is a member of Wipro’s Responsible AI Taskforce. In addition to collaborating with clients, Phani is driving a number of internal initiatives to reimagine consulting for Wipro, upskilling 8,000 colleagues to deliver value for themselves and clients.

Sundar Thyagarajan
Principal Consultant

Sundar is a seasoned consulting professional, with over 15 years of experience in digital consulting, business transformation, AI. He has led multiple large accounts across the UK and Australia, driving business transformation, process re-engineering, and value creation through AI.