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Assessing credit losses in financial statement audits: A guide for auditors

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Credit losses represent a significant area of focus in financial statement audits. As economic conditions fluctuate and accounting standards evolve, auditors face increasing challenges in evaluating how organizations estimate and report credit losses, and in providing a comprehensive overview of credit loss assessment in financial statement audits. 

This article will explore the concept of credit losses, examine relevant regulatory frameworks, discuss vital challenges auditors encounter, and offer best practices for effectively auditing credit loss estimates. In addition, it will also describe key emerging trends and technologies shaping the future of credit loss auditing.

Credit losses occur when a borrower fails to repay a debt according to the initial agreement. These losses are typically reported as allowances for credit losses or loan loss provisions in financial statements. They represent the estimated amount of debt that may not be collected, reflecting the credit risk associated with a company’s financial assets.

For auditors, understanding how companies calculate and report credit losses is crucial. This process often involves complex estimates and judgments, as companies must forecast future economic conditions and borrower behavior. The shift toward more forward-looking models, such as the Current Expected Credit Loss model in the United States, has further increased the complexity of these estimates.

Auditors must evaluate whether these estimates are reasonable and supported by appropriate evidence, ensuring that financial statements accurately reflect the company’s credit risk exposure.

Regulatory framework and standards

Various standards and regulations govern the accounting for credit losses, which have undergone significant changes in recent years. In the United States, the Financial Accounting Standards Board introduced Accounting Standards Update 2016-13, which implemented the CECL model. Internationally, the International Accounting Standards Board has issued IFRS 9, which includes a similar expected credit loss model.

These standards require companies to recognize expected credit losses over the life of a financial asset rather than waiting for a loss event to occur. This forward-looking approach aims to provide financial statement users with more timely and relevant information about credit risk.

Auditors must stay current with these standards and any related interpretations or guidance issued by regulatory bodies. They must also understand how these standards apply to different types of financial assets and industries to effectively audit credit loss estimates.

Critical challenges in auditing credit losses

Auditing credit losses presents several challenges:

  • Complexity of models: Credit loss models often involve complex statistical techniques and numerous assumptions. Auditors must assess whether these models are appropriate and whether the assumptions used are reasonable.
  • Data quality and availability: The accuracy of credit loss estimates depends heavily on the quality and completeness of historical and current data. Auditors must evaluate the reliability of data sources and the processes used to collect and maintain this information.
  • Judgment and estimation uncertainty: Credit loss estimates involve significant judgment, particularly in forecasting future economic conditions. Auditors must evaluate the reasonableness of these judgments and ensure appropriate disclosure of estimation uncertainty.
  • Rapidly changing economic conditions: Economic volatility can quickly render historical data and assumptions obsolete. Auditors must consider how companies have incorporated recent economic trends and events into their estimates.
  • Internal controls: Assessing the effectiveness of internal controls over the credit loss estimation process is crucial but can be challenging due to the complexity and judgment involved.
  • Potential management bias: Given the subjective nature of credit loss estimates, there’s a risk of management bias. Auditors must remain skeptical and alert to potential manipulations of these estimates.

Best practices for auditors 

To effectively audit credit losses, auditors should consider the following best practices:

  • Develop a thorough understanding: Gain in-depth knowledge of the company’s business model, credit risk management practices and the specific credit loss estimation methodology.
  • Assess model appropriateness: Evaluate whether the credit loss model aligns with accounting standards and suits the company’s specific circumstances. When dealing with complex models, consider involving specialists.
  • Test key assumptions: Critically evaluate the reasonableness of key assumptions used in the credit loss model. This may involve comparing assumptions to industry benchmarks, historical data, and economic forecasts from reliable sources.
  • Perform sensitivity analyses: Assess how changes in key assumptions impact the credit loss estimate to understand the model’s sensitivity and identify potential areas of concern.
  • Evaluate data integrity: Test the completeness and accuracy of data used in the credit loss model. This includes both historical data and current information used to inform forward-looking estimates.
  • Review disclosures: Ensure financial statement disclosures adequately explain the credit loss estimation process, key assumptions and areas of uncertainty.
  • Assess internal controls: Thoroughly evaluate internal controls’ design and operating effectiveness over the credit loss estimation process.
  • Consider management bias: When selecting assumptions or data used in the estimation process, remain alert to potential indicators of management bias.
  • Document thoroughly: Maintain clear and comprehensive documentation of audit procedures performed, evidence obtained, and conclusions regarding credit loss estimates’ reasonableness.
  • Stay updated: Continuously monitor changes in accounting standards, regulatory guidance, and industry practices related to credit loss estimation and auditing.

Emerging trends and technologies

The field of credit loss auditing is evolving rapidly, driven by technological advancements and changing regulatory landscapes. Emerging trends include:

  • Increased use of artificial intelligence and machine learning in credit loss modeling;
  • Greater emphasis on real-time data analysis and continuous auditing techniques;
  • Enhanced data analytics tools for identifying patterns and anomalies in large datasets;
  • Growing focus on climate-related risks and their potential impact on credit losses; and,
  • Increased regulatory scrutiny of credit loss estimates, particularly during economic uncertainty.

The impact of AI on auditing credit losses

Artificial intelligence is revolutionizing how credit losses are estimated and audited. Its ability to quickly process vast amounts of data and identify complex patterns is particularly valuable in this field. 

Here are some key areas where AI is making a significant impact:

1. Enhanced pattern recognition. AI algorithms can analyze historical data to identify subtle patterns indicating increased credit risk. For example, an AI system might detect that customers who make frequent small purchases followed by large purchases are more likely to default. This pattern might need to be more nuanced for traditional analysis methods to catch.

Example: An auditor reviewing a bank’s credit loss estimates could use AI to analyze the transaction patterns of thousands of credit card holders. The AI might identify a correlation between certain spending behaviors and the likelihood of default that the bank’s model hasn’t accounted for, prompting the auditor to question the completeness of the bank’s risk assessment.

2. Improved forecasting. AI models can incorporate a broader range of variables and data sources to improve the accuracy of credit loss forecasts. This includes nontraditional data such as social media posts, online behavior, or macroeconomic indicators.

Example: When auditing a mortgage lender’s expected credit losses, an AI system could analyze not just traditional factors like credit scores and income but also incorporate data on local real estate trends, employment statistics, and even climate change projections for coastal properties. The auditor could assess whether the lender’s forecasting model is sufficiently comprehensive.

3. Real-time risk assessment. AI systems can continuously update risk assessments as new data becomes available, allowing for more dynamic credit loss estimates.

Example: An auditor reviewing a company’s accounts receivable might use an AI tool that continuously monitors customer payment behaviors, news about customer companies, and industry trends. This could help the auditor assess whether the company’s credit loss allowances are updated frequently enough to reflect current risks.

4. Anomaly detection. AI can quickly identify unusual patterns or transactions that might indicate errors in credit loss calculations or potential fraud.

Example: When auditing an extensive portfolio of loans, an AI system could flag individual loans or groups with risk characteristics that don’t align with their assigned risk ratings. This could help auditors focus on areas where the credit loss estimates might need to be more accurate.

5. Automation of routine tasks. AI can automate many routine aspects of auditing credit losses, such as data gathering, reconciliations, and basic calculations. This allows auditors to focus more on complex judgments and risk assessments.

Example: An AI system could automatically gather loan data, calculate expected loss rates based on historical performance, and compare these to the client’s estimates. The auditor could then focus on evaluating the reasonableness of any differences and assessing the qualitative factors that might justify them.

6. Enhanced scenario analysis. AI can rapidly run multiple complex economic scenarios to stress-test credit loss models, providing auditors with a more comprehensive view of potential risks.

Example: When auditing a bank’s loan loss provisions, an AI system could quickly generate and analyze hundreds of potential economic scenarios, considering factors like interest rates, unemployment and GDP growth. This could help the auditor assess whether the bank’s scenario analysis is sufficiently robust and comprehensive.

While AI offers significant benefits, it’s important to note that it also introduces new challenges for auditors. These include ensuring the reliability and appropriateness of AI models, understanding the “black box” nature of some AI algorithms, and maintaining professional skepticism when working with AI-generated insights. Auditors must develop new skills to effectively leverage AI tools while still applying their professional judgment to the audit process.

Auditors should stay informed about these trends and consider how they might impact their audit approaches and methodologies.

Final word

Auditing credit losses remains a complex and challenging task. By staying informed, applying best practices, and leveraging emerging technologies, auditors can enhance the effectiveness and efficiency of their work, ultimately contributing to the reliability and transparency of financial reporting.

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Life insurance performance evaluation strategies for accountants and clients

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Life insurance is an integral part of an overall financial plan. Regular reviews can determine whether the policy is performing according to expectations and meeting the client’s current financial objectives. Most importantly it will determine whether the client’s coverage will be in place when needed, at the insured’s passing. There are many factors to consider that will impact the performance of a life insurance policy. A periodic review of your client’s life insurance portfolio will determine that the product’s features, benefits and costs, as well as the client’s current planning objectives, are being met. One of the most significant reasons for doing so is to determine a life insurance policy’s current and all-important future cash value and how it’s being impacted by the policy’s cost of insurance.

Knowing the current accumulated cash value allows one to make several important assumptions, the most prominent being whether the cash value will be sufficient to prevent the policy’s coverage from expiring prematurely. Non-guaranteed universal life insurance is an asset class that must be actively managed in the same manner as a client would evaluate the performance of their stock, bond, or real estate portfolio. 

During the past 30 years, many owners of life insurance policies have found and are continuing to discover that if they purchased life insurance between the early 1980s and early 2000s, there was a three out of four chance that their policy was of a non–guaranteed nature, meaning its duration of coverage was entirely dependent on the overall accumulated cash value based on the cumulative interest rate earned by their life insurance policy. For example, In the 1980s, when interest rates were 17 to 18% and many owners of these new non-guaranteed universal life insurance policies mistakenly assumed that the current interest rate would always remain in the vicinity of the initial 17 to 18%, over the next 20 to 30+ years. But as rates continuously declined, with the exception of the last two years, they in fact only earned an average of 4 to 5%. Unfortunately, the owners of these non- guaranteed policies have since found themselves in a situation where 30 to 35% of these existing non-guaranteed contracts have been and are continuing to expire prematurely at a steadily increasing rate. The accumulated cash value was simply insufficient to cover the policy’s annual costs when the insured was in their mid-80s 

In the case of a lapsing policy with a loan, the policy owner is subject to income taxes, as a result of forgiveness of debt if the policy expires before the insured. Likewise, if a trustee or grantor forgets to pay the premium or assumes no premium is due when in fact it is, the insurance companies will at the broker’s initial request based on a checkmark on the application pay the premium to keep the policy in force. Further, it will consider those premiums as a loan and charge a cumulative 5 to 6% interest rate on the loan each year. The trustee and the grantor are often unaware that this loan and the accruing interest on that loan are draining the policy’s cash value, thus accelerating the policy’s premature expiration. It’s of paramount importance that the policy not be allowed to expire before the insured does.

My experience over the last 35 years has shown me that a typical unskilled trustee, usually the eldest son or daughter of the insured, was not given proper guidance that a non-guaranteed policy was no longer a “buy and hold” asset     that could be placed in a drawer and forgotten and had instead become a        “buy and manage” asset. As a result, there were no procedures in place to properly manage a personally owned or trust-owned life insurance policy. Further exacerbating the problem is the fact that the insurance agent/broker may no longer be involved, and the insurance company, contrary to popular belief, is not obligated, beyond sending an annual statement with important information about the fact that interest rates could adversely impact the duration of coverage, buried somewhere on page 4 of an eight-page report. 

Here’s a little-known fact: It’s not in the insurance company’s best interest that one’s coverage remains in force. The reason being, they profit when policies expire prematurely. Consider the fact that after years of an insured paying their premiums, a death benefit is not required to ever be paid because the policy lapsed. 

Such are the consequences of sustained reduced interest rates and years of in- attention on the part of the sons and daughters acting as the private owners or unskilled trustees of their parents’ life insurance policies. Sons and daughters that didn’t know that they were 100% responsible for the performance of their policies. Nor did they know they should have increased the premium they paid to the insurance company over the last 20 to 30 years as that would have been the only way they could have made up for the reduced earnings caused by falling interest rates. (with exception of the last two years)

As a result, an increasing number of trust beneficiaries and their families are finding themselves left without the life insurance proceeds they were otherwise expecting to receive. Many of those beneficiaries are now litigating against other family members and their advisors who didn’t know any better but should have. These situations leave owners in a position where they must decide whether it makes sense to continue their coverage so it lasts through their life expectancy at a significantly higher cost than their current premium, or to give up (lapse) all or part of the coverage. 

So how can an attorney or accountant, acting as a trustee themselves or an advisor to the policy owner or trustee, know if the universal life policy they, or their clients, own has problems? The most reliable way to understand how a policy is performing is to order an in-force historic re-projection. This evaluation illustrates the policy from its inception until the present and contains all premiums paid to date and the policy’s current cash values. These values must now be projected into the future based on current guaranteed crediting rates and on the current increasing mortality costs and costs of insurance that the insurance company charges the insured each year. The tools to provide these analytical services are available; they just need to be used. 

The best course of action for a son or daughter acting as an accommodation or unskilled trustee, or for their advisor attempting to maintain their client’s life insurance coverage, would be for them to engage an experienced independent life insurance consultant to conduct a performance evaluation to determine whether the policy funding their trust is one of the 70% of non-guaranteed policies that are most likely to be in danger of expiring prematurely. This is then followed by setting up a plan for corrective action with the objective of making changes in strategies meant to best remedy the current situation so as to maintain the policy’s coverage. 

Should you come across a client in this position, consider an alternate exit strategy rather than merely surrendering the policy back to the insurer and instead engage a licensed life settlement broker to consider the sale of the policy in the secondary marketplace to an institutional investor. In doing, so you will find that it’s common for a client to receive an offer that’s two to three times higher than the cash surrender offered by the insurance company. The ideal candidate for such a transaction is an insured person over the age of 70 and ideally in poorer health than they were when they applied to the coverage. Basically, an older insured person in poor health will receive a better offer than a younger individual in good health. 

Another important reason to consider a sale of a policy rather than allowing it to lapse is in the case of a lapsing policy with a loan, the policy owner can be subject to income taxes, as a result of forgiveness of debt if the policy expires before the insured. If the policy with the debt survives the insured, the debt is forgiven and no taxes are due. Likewise, if a trustee or grantor forgets to pay the premium or assumes no premium is due when in fact it is, most insurance companies — based on the agent or broker checking the box to prevent the policy from lapsing — will automatically pay the premium to keep the policy in force. Further, it will consider those premiums to be a loan and charge a cumulative 5% interest rate on the loan each year. The trustee and the grantor are often unaware that this loan and the accruing interest on that loan are draining the policy’s cash value, thus causing it to expire prematurely.

Many accountants and attorneys have suggested that their high-net-worth clients use an institutional trustee for their trust-owned life insurance policies, while others have chosen to serve as trustees of such trusts themselves. Since institutional trustees charge a fee for their services, only a small portion of trust-owned life insurance policies — less than 10% — use a corporate or institutional trustee to professionally manage a client’s irrevocable life insurance trust. The other 90% ask a family member or close friend or an advisor to act in the capacity of an accommodation or unskilled trustee. 

Lastly, it’s important for any trustee to be aware that with the title and fee comes a significant amount of responsibility and fiduciary liability to evaluate the performance of a client’s portfolio. If they are not equipped to do so, it’s their duty to engage the services of a professional who can.

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Denmark targets investors tied to Sanjay Shah at US tax fraud trial

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Weeks after Danish judges sentenced hedge fund trader Sanjay Shah to 12 years in prison, the country’s lawyers turned to a U.S. court in a bid to recoup about $500 million lost in the Cum-Ex tax dividend scandal.

Lawyers for the Nordic country told a New York jury that a group of US investors helped Shah steal from the Danish treasury by filing 1,200 fraudulent requests for tax rebates on dividends.

“This case is about greed and theft,” Marc A. Weinstein, a lawyer for the Danish tax authority, said during opening statements at a civil trial that started this week in federal court in New York. “They lined their own pockets, the pockets of their friends and families and the pockets of their coconspirators with the funds they stole from Denmark.”

Shah, who was sentenced to prison last month for orchestrating a scheme that netted 9 billion kroner ($1.24 billion) through thousands of sham dividend tax refund applications, has become the public face of the Cum-Ex tax scandal that has engulfed bankers and lawyers in several European countries. Three people have been convicted of Cum-Ex related crimes in Denmark, and about 20 in Germany.

Cum-Ex was a controversial trading strategy designed to obtain duplicate refunds by taking advantage of how dividend taxes were collected and regulated a decade ago. Germany is looking at about 1,800 suspects from across the global financial industry in probes linked to the practice.

Denmark’s Customs and Tax Administration, also known as SKAT, has been pursuing traders and businesses around the world in a bid to claw back the billions it says it lost through trading schemes spearheaded by Shah. The case is the first to go to trial in the U.S. over Cum-Ex fraud linked to the hedge fund founder.

But a lawyer for two of the investors, Richard Markowitz, and his wife, Jocelyn Markowitz, told the jury that SKAT allowed Cum-Ex transactions to flourish for years before trying to stop the practice. He compared the tax agency to the town officials in the movie Jaws who were so focused on the tourist trade that they “didn’t do anything until the bodies started piling up.”

“Rich and Jocelyn did not do anything wrong. They didn’t lie, they didn’t cheat,” said Peter Neiman, a lawyer for the couple, during his opening arguments. “SKAT was not careful.” 

Shah was a suspect in probes in both Denmark and Germany. German prosecutors also accused him of routing Cum-Ex deals through the U.S., saying in one indictment that he used a Jewish school in Queens to execute trades totaling €920 million euros ($948 million) as part of a plan to deceive tax authorities.

Shah, the founder of Solo Capital, became the most prominent figure of the Cum-Ex scandal after a 2020 Bloomberg TV interview where he said that “bankers don’t have morals” and expressed no remorse for taking advantage of what he said were loopholes in some countries’ tax codes.

Denmark says that Richard Markowitz, John van Merkensteijn and two of their partners at a New York financial services firm, Argre Management, were recruited by Shah to take part in the scheme in 2012. Pension plans created by Argre became customers of Shah’s hedge fund, which served as the purported custodian of Argre’s Danish shares, and issued fraudulent statements for a rebate on dividend taxes that were withheld.

The plans, including ones established by their wives, Jocelyn Markowitz and Elizabeth van Merkensteijn, later submitted those statements as proof that the company was entitled to the refunds, the Danish tax agency says.

SKAT has sued approximately 260 pension plans and individuals in the U.S. over Cum-Ex. The country has also filed civil cases seeking to claw back Cum-Ex funds in other countries. A trial in London wrapped up last month where SKAT is suing dozens of traders and businesses. 

If Neiman agreed with the Danish tax agency on anything, it was that Shah was the real villain. He said that Markowitz and Van Merkensteijn, “honestly and in good faith” entered into what they believed were legitimate dividend arbitrage transactions, first in Germany, later in Belgium and then in Denmark, only to find out that Shah had deceived them.

“It was only years later that they found out that Sanjay Shah had at some point stopped doing what he had promised and had begun to lie to them over and over and over again,” he said.

“The blame here lies with Sanjay Shah and Solo,” said Sharon McCarthy, a lawyer for the van Merkensteijns.

The case is In Re: Customs and Tax Administration of the Kingdom of Denmark (Skatteforvaltningen) Tax Refund Scheme Litigation, 18-md-2865, U.S. District Court, Southern District of New York. 

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Tech roundup: Intuit guarantees tax refunds 5 Days early into any bank account

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Intuit guarantees tax refunds 5 Days early into any bank account; IRIS beefs up Firm Management solution, customer success function; and other accounting tech news and updates.

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