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Opportunities for AI in Lease Accounting

Since late 2022, generative AI has begun to revolutionize how work is approached. From the qualitative aspects of developing marketing campaigns to the technical aspects of developing code much has been impacted by generative AI. Increasing amounts of data and reporting requirements may make AI an indispensable tool in the near future. This future will likely not see AI as a replacement for practitioners in the field but as a valuable assistant that can aid them with certain tasks and complex analysis.

There are several current and developing use cases for AI in lease accounting. These use cases include:

Automation in Lease Accounting: Automation technologies powered by AI are adept at handling the complexities and nuances of lease accounting. Tasks like lease classification, computation of lease liabilities and recognition of right-of-use assets, which can be labor-intensive and prone to human error, are streamlined through AI algorithms. These algorithms can automatically extract terms and figures from lease documents, apply relevant accounting standards, and generate journal entries and financial statements. This automation saves considerable time and reduces the risk of errors associated with manual data entry and calculations. AI-enabled automated systems also continuously learn and adapt, improving their precision over time. For lease accountants, this can translate into an enhanced ability to manage large lease portfolios with greater confidence and ease and frees them up to focus on more strategic aspects of lease management and decision-making.

 

Predictive Analytics for Lease Decisions: AI in predictive analytics in lease accounting represents a shift toward a more data-driven decision-making process in the management of lease portfolios. By harnessing the power of AI and machine learning, predictive analytics tools can delve deep into historical lease data, tenant payment histories and broader market trends to forecast future scenarios. These sophisticated models can predict the likelihood of lease renewals and potential defaults and assess the impact of economic shifts on lease viability. For lease accountants, this means having a forward-looking perspective that aids in making proactive decisions. Whether it is determining optimal lease terms, anticipating and mitigating risks, or identifying lucrative leasing opportunities, AI-driven predictive analytics provides a level of insight that was difficult to achieve in the past.

Optimizing Lease Terms and Payments: The utilization of AI in optimizing lease terms and payments can offer tailor-made solutions that align with both lessor and lessee objectives. AI-driven models can analyze many factors, including market conditions, property values, lessee creditworthiness and historical lease performance to suggest advantageous lease terms and payment schedules. For instance, AI can forecast market rent trends, enabling lease accountants to set competitive yet profitable lease rates. It can also recommend lease duration and escalation clauses based on projected economic conditions. On the payments side, AI models can optimize cash flow by predicting optimal payment structures, considering factors like lessee liquidity and interest rate fluctuations. This level of optimization can enhance the financial performance of the lease portfolio and improve tenant satisfaction by offering realistic and favorable terms. AI can act as a sophisticated decision-support tool, enabling lease accountants to make data-backed decisions that maximize returns while managing risk effectively.

Contextualized Lease Analysis: AI, equipped with Natural Language Processing (NLP) and machine learning capabilities, can sift through extensive lease data, extracting information and trends. It then synthesizes this data into coherent, understandable commentary. For example, when analyzing a lease portfolio, AI can highlight significant patterns such as clusters of leases approaching renewal, identify leases that are underperforming, or spotlight those at higher risk of default. The AI can articulate these findings in a narrative format, making complex data more accessible and understandable for stakeholders. This feature can be particularly valuable in reporting to management or investors, where clear and concise explanations are important. AI-generated commentary not only potentially saves time but also adds depth to the analysis, offering a narrative that may contextualize the numbers and trends within a broader business and market framework.

There are some risks currently associated with the use of AI. AI models trained on datasets that are not representative of the true population can provide misleading or biased results. These incomplete datasets can be responsible for several types of errors including inaccurate forecasts and unfavorable leasing terms driven by unintentional bias in the data. Generative AI may also hallucinate, meaning that it will generate incorrect commentary or results. Also, AI may be responsible for intellectual property infringement due to its reliance on a broad set of data from the internet used to train the models.

To make the most of the benefits and mitigate the associated risks, organizations can implement a framework similar to Deloitte’s Trustworthy AI Framework which provides guidance on how to govern AI:

  • Safe and Secure: Ensure the AI system is protected from risks that may cause individual and/or collective physical, emotional, environmental and/or digital harm.
  • Robust and Reliable: Build models that produce consistent and accurate outputs, withstand errors and recover quickly from unforeseen disruptions and misuse.
  • Accountable: Develop policies that determine who is responsible for the decisions made or derived using AI.
  • Responsible: Implement AI that is purposefully designed and operated in a socially and ethically responsible manner.
  • Private: Build controls that ensure privacy is respected and data is not used or stored beyond its intended and stated use and duration; users can opt in/out of sharing their data.
  • Transparent and Explainable: Promote awareness of how AI is being leveraged, particularly in making decisions and ensure that these decisions are easy to understand, auditable and open to inspection.
  • Fair and Impartial: Design AI to operate inclusively with an aim for equitable application, access and outcomes.
Conclusion

AI can provide opportunities to enhance the capabilities of lease accounting and drive value in organizations, but these opportunities are not without their risks. A sound governance practice that leverages leading AI management frameworks can reduce the risks and make the most of AI investments in a responsible manner.


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Categorized With:

  • INNOVATION
  • LEASE ACCOUNTING