Articles & Alerts
Navigating the Impact of the New Current Expected Credit Loss Accounting Standard for FinTech Companies
Written by: Kiriaki Giannoulas, CPA, and Zhanna Korneva, CPA
The Financial Accounting Standards Board (FASB) issued an Accounting Standards Update (ASU) which proposes a significant shift in the accounting treatment of credit losses on financial assets by introducing the Current Expected Credit Loss (CECL) model. Replacing the previous incurred loss model, CECL requires an expected loss approach which aims to enhance the forward-looking and proactive recognition of credit losses. While its primary impact is on financial institutions, CECL is relevant to any entity carrying trade receivables, loans receivable, off-balance sheet credit exposures, and any other financial asset with contractual rights to receive cash. The standard does not apply to certain financial assets, such as those measured at fair value and marked to market each reporting period, or receivables between entities under common control. However, its impact on businesses carrying trade receivables and other assets within the scope of CECL may be significant. The new standard is effective January 1, 2023 for private companies reporting on a calendar year.
Under the incurred loss model, entities were required to record a loss when losses were deemed as probable. This model assumed receivables and loans were collectable until evidence to the contrary was identified. Under the new CECL model, entities are required to record expected lifetime credit losses for the financial asset when it is originated. Determining expected credit loss under the CECL model involves forming pools of assets with similar risk characteristics and selecting a consistent method for estimating credit losses based on historical experience, current conditions, and reasonable supportable forecasts. While there are several measurement methods an entity can use to estimate expected credit losses, the FASB does not permit the use of hindsight when applying CECL, allowing only the use of information available as of the reporting date.
The impact of implementing CECL on a technology company’s financial reporting will vary depending on the types of assets held by the company and its overall revenue model. Subscription-based SaaS companies that collect most of their subscription revenues in advance will likely see minimal effects on financial reporting due to a smaller pool of receivables at risk of credit loss. For these companies, loss reserves under the CECL model may end up similar to those under the existing incurred loss model.
In contrast, FinTech companies offering products geared towards lending services will likely see a greater impact, and should perform detailed analysis of their financial assets to determine a relevant estimation methodology for each distinct pool of assets. A FinTech company may hold various categories of financial assets based on its revenue streams, each with unique characteristics requiring significant judgment in analysis. Some examples might be buy now-pay later receivables, credit card receivables, loans or advances to customers, or off-balance sheet commitments for credit extended to borrowers. Depending on the asset pool, factors considered in measuring expected credit losses on these assets may encompass economic conditions like interest rates and unemployment fluctuations, venture capital and private equity market activity, historical loss experience, credit ratings, and borrower creditworthiness. For consumer-facing FinTechs, factors like geographic location, age demographics, and inflation may also be relevant factors in credit loss estimation.
Implementing the CECL framework in the Fintech sector presents several challenges. Earlier stage startups will likely encounter difficulties due to a lack of historical loss data, complicating the process of establishing a reliable baseline for calculating future expected credit losses. Furthermore, for companies involved in consumer lending, evaluating creditworthiness and risk profiles of borrowers is becoming an increasingly complex exercise requiring various data inputs such as customer credit scores, income levels, borrower behaviors and trends, and market conditions. Navigating this estimation process within the constraints of data privacy and cost requires careful consideration and thorough analysis. Expected losses on credit card receivables can be particularly difficult to evaluate, due to the revolving nature of the balances and uncertainty in determining contractual life of the receivable. Given these challenges, the technical complexities of CECL may surpass in-house capabilities for companies operating with lean teams and constrained budgets, which may require allocating funds for external expertise.
Adapting to this new standard is not only about staying compliant with the accounting standard, but it is an opportunity for tech leaders to fine-tune their financial approaches, leveraging data, innovation, and forward-looking insights to navigate the evolving terrain of credit risk with resilience and foresight.