How a Company Used Artificial Intelligence to Expedite ASC 842 Compliance

ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING
How a Company Used Artificial Intelligence to Expedite ASC 842 Compliance

While the Financial Accounting Standards Board’s (FASB’s) new lease accounting standard (ASC 842) was intended to properly recognise the economics of leasing activity on a company’s financial statements, the process of adhering to the new standard has posed multifaceted challenges. Common transition issues include assessing the validity, completeness and accuracy of a company’s existing lease portfolio; potential challenges related to prior lease classifications; and identifying and quantifying the entire pool of leases embedded in other business arrangements and contracts.

One of the new standard’s biggest challenges, however, is less procedural and more human in nature: the mindset shift required to move from paper-oriented lease processes to a much more digital approach. It turns out that artificial intelligence (AI) can help make this transition smoother, faster and more cost-effective.

That’s what a global manufacturing company’s corporate finance group and their information technology (IT) colleagues, along with a team of external consultants, learned when faced with the daunting task of manually abstracting data from hundreds of lease documents in compliance with ASC 842 within a tight, self-imposed deadline to allow time for adequate review and testing. This meant that the company had approximately 26 weeks to analyse a population of 700 to 800 lease documents – or so the project team initially thought.

Getting an accurate read on the organisation’s lease volume proved difficult considering the sheer size and scope of the company’s global operations and its vast sales and distribution channels. While some lease-related documents are easily recognisable, other sources of lease data can be extremely difficult to identify, especially when those details are embedded in customer contracts or service agreements.

To address this implementation challenge, the project team organised and conducted a comprehensive lease discovery operation consisting of confirmations with external vendors, questionnaires and workshops with business owners, and reviews of general ledger transaction details and spend patterns. This thorough effort, which took place over 10 weeks, ensured that all leases (embedded or otherwise) were identified and captured. That was a successful first step. But this success resulted in another challenge: The project team found 1,700 leases, well over twice the original estimate, to manually review and process within the remaining 16 weeks. The company’s lease accounting software required 90 unique fields to be populated for each of the 1,700 leases, a volume of work that the team realised would likely extend well past the adoption deadline given available project resources. So, the project group devised an automated data abstraction tool driven by AI.

Lease documents were uploaded in digital format and converted to text files using high-speed document scanning and optical character recognition (OCR) conversion. The team engaged consulting experts to develop and apply machine learning and natural language processing algorithms to train the system to recognise and “get” key lease accounting terms.

Although not all 1,700 leases could be subjected to the AI capabilities, roughly half of the leases were deemed sufficiently standardised and of good scan quality to subject to the AI tool. Relevant values from these documents were exported to the template and uploaded into the accounting software. Samples were validated to ensure accuracy. Simultaneously, the remaining leases were processed manually due to an overall lack of standardisation among the documents.

This combination of cutting-edge technology, lease accounting expertise, knowledge of accounting software and old-school manual work helped the manufacturing company comply with the new lease accounting standard on time, months ahead of the required deadline. The effort resulted in an estimated 15 to 20 percent reduction in project hours and an estimated 42 percent reduction in the costs that would have been incurred through an entirely manual approach to complying with the new lease accounting standard. This compliance success now has the manufacturing company considering similar applications of AI along with robotic process automation to other areas of corporate finance and throughout the business.

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