Behavioral analytics could’ve detected and prevented Evergrande fraud

Hui Ka Yan, founder and chairman of the Evergrande Group, one of China’s largest property developers, once led the high life of a billionaire. In 1996, he started Evergrande in Shenzhen, a special economic zone, when the Chinese Communist Party had begun experimenting with capitalism. He grew his company by accepting prepayments on apartments that weren’t built yet and funds from enthusiastic investors. Evergrande exploded exponentially but then finally collapsed in 2021 purportedly because of overexpansion, excessive borrowing, mismanagement and alleged financial misreporting. The company exaggerated revenue by $78 billion.

In 2024, the China Securities Regulatory Commission accused Hui (also known as Xu Jiayin in Mandarin Chinese) of “organizing fraud.” He was fined $6.5 million and banned from Chinese financial markets for life. As of September 2024, he was reported to be held in a Shenzhen detention center. The Chinese government ordered the liquidation of Evergrande in January of last year. Thousands of homebuyers were left with unfinished properties, and financial institutions faced significant losses. The broader economic implications included a decline in real estate prices and increased market volatility, underscoring the systemic risk of such large-scale frauds.

(See “China Evergrande’s Crash Was Accelerated by Questionable Accounting,” by Alexandra Stevenson, The New York Times, Dec. 5, 2023; “China Evergrande Founder Accused of Exaggerating Revenue by $78 Billion,” by Alexandra Stevenson, The New York Times, March 19, 2024; and “Exclusive: Evergrande Chairman kept in special Shenzhen detention center,” by Clare Jim and Julie Zhu, Sept. 13, 2024.)

Like so many large frauds, Evergrande’s crimes flew under the radar for many years because regulators, investors, accountants and the company itself hadn’t examined its inner workings. Classic story. However, I believe that if these parties had just used standard behavioral analytics they could’ve discovered debt concealment, asset overvaluation and other problems early on. Also, if Evergrande had had an ethical tone at the top, the company would’ve empowered its departments to examine themselves with behavioral analytics to comply with national regulations.

Behavioral analytics is a method of fraud detection that analyzes user patterns to identify potential fraud or other illegal activity. It uses machine-learning algorithms to track and analyze transaction data and learn from new data points to detect evolving fraudsters’ methods.

Most of you know the value of data and behavioral analytics. Here’s more evidence of their worth when you need to persuade management during purchasing season.

Early signs of financial misreporting

In 2016, Evergrande’s financial statements showed rapid growth in debt, with liabilities increasing from $54 billion in 2015 to over $90 billion by the end of 2016. (See “Evergrande: Not So Grand Financial Statements?,” by Binod Shankar, CFA, Enterprising Investor, Nov. 1, 2021.)

This significant deviation in debt levels should’ve raised red flags. In reality, Evergrande diverted funds from its core real estate projects to invest in unrelated industries, such as electric vehicles, tourism and health care. Evergrande misled investors about project completions, sales figures and overall financial health.

How behavioral analytics could’ve helped

Behavioral analytics could’ve analyzed the company’s borrowing patterns and compared with industry benchmarks. Analysis tools could’ve identified unusual spikes in borrowing that didn’t correlate with revenue growth or asset acquisition. Monitoring of fund allocation could’ve revealed discrepancies between reported and actual project expenditures, indicating potential fraud.

Behavioral analytics could’ve analyzed communication patterns, including emails, reports and public statements. Analyzing the consistency and content of communications with investors and stakeholders could’ve revealed inconsistencies and misleading information, prompting further scrutiny.

Abrupt changes in asset valuations

In early 2018, Evergrande announced a substantial increase in valuation of its real estate assets. This was inconsistent with the market trends and economic conditions.

The company’s annual report for the fiscal year 2017, released in March 2018, attributed the increase to improved market conditions and successful project completions.

By mid-2018, analysts began scrutinizing Evergrande’s financial statements, noting the discrepancy between the company’s reported asset valuations and the broader market trends. Independent market reports indicated a slowdown in the Chinese real estate market, with declining property prices and reduced transaction volumes. In late 2018, financial journalists and market analysts published reports questioning the veracity of Evergrande’s asset valuations. The sudden increase in valuations was inconsistent with economic conditions, sparking concerns about potential financial misreporting. (See “The Evergrande debt crisis, explained,” by Edirin Oputu, Temple Now, Oct. 26, 2021 and “China Evergrande Group Annual Report 2017.”)

Evergrande issued multiple public statements that said the increased asset valuations were because of strategic land acquisitions, project completions and favorable market conditions in specific regions.

How behavioral analytics could’ve helped

Continuous monitoring of asset valuation patterns would’ve identified sudden and unjustified increases in asset values as an outlier. This would’ve prompted deeper investigations into underlying data and assumptions used to calculate these valuations, potentially revealing discrepancies and manipulative practices. Regulators could’ve monitored these valuation changes and flagged discrepancies. This would’ve involved cross-referencing asset valuations with external property market data and historical trends, revealing overvaluations indicative of financial manipulation.

Also, analytics tools could’ve highlighted the inconsistency of Evergrande’s sudden asset value increase in 2018 with previous economic reports. Analysis of emails, financial disclosures and public statements made by Evergrande executives during 2018 could’ve revealed inconsistencies or deceptive practices. Real-time integration of market data with Evergrande’s reported figures would’ve highlighted inconsistencies immediately, enabling regulators and stakeholders’ prompt actions.

Unusual borrowing patterns

Throughout 2019 and 2020, Evergrande accumulated debt through unconventional borrowing methods, such as high-interest loans from shadow-banking institutions. (See “How Evergrande’s downfall signaled China’s property crisis,” by Engen Tham, Julie Zhu and Clare Jim, Reuters, Aug. 31, 2023.)

By the end of 2018, Evergrande was already one of China’s most indebted real estate developers. The company faced mounting pressure to meet its financial obligations amid slowing economic growth and tightening regulations on property financing. To sustain its operations and ambitious expansion plans, Evergrande turned to shadow banking — a sector known for its high-risk, high-interest lending practices. Unlike traditional banks, shadow-banking entities are less regulated, allowing them to provide loans that circumvent official lending caps and regulatory scrutiny. (See “China’s Most-Indebted Developer Has a Risky Shadow Loan Habit,” by Blake Schmidt and Frederik Balfour, Bloomberg, Oct. 24, 2018.)

In early 2019, Evergrande began securing significant loans from trust companies, which are part of China’s shadow-banking sector. Trust companies typically pool funds from individual and institutional investors to finance high-interest loans for companies like Evergrande. These loans often come with interest rates ranging from 10% to 15%, significantly higher than those offered by traditional banks. (See “How Evergrande’s downfall signaled China’s property crisis” and “China Evergrande Misses 3rd Round of Bond Payments, Intensifying Contagion Fears,” by Clare Jim and Andrew Galbraith, Insurance Journal, Oct. 12, 2021.)

By mid-2019, Evergrande expanded its use of wealth management products (WMPs) to raise additional funds. WMPs are investment products sold to retail and institutional investors, promising high returns. Evergrande’s subsidiaries issued these WMPs, using the proceeds to finance real estate projects and repay existing debts. However, the high returns promised to investors (often exceeding 10%) added to Evergrande’s financial burden. (See “Lured by promises of high returns, thousands gave Evergrande cash,” Al Jazeera, Sept. 22, 2021.)

The COVID-19 pandemic in 2020 further impacted Evergrande’s financial health as property sales slowed and construction projects were delayed. Despite these challenges, Evergrande continued to secure high-interest loans from shadow-banking institutions to stay afloat. In January 2020, Evergrande raised approximately $2 billion through private placements of high-yield bonds, targeting international investors seeking higher returns amid global economic uncertainty, according to a Financial Times article. (See “Debt-laden Evergrande raises more debt,” by Jamie Powell, Jan. 20, 2020.)

How behavioral analytics could’ve helped

Analytical models could correlate borrowing patterns with cash-flow statements to detect inconsistencies. Behavioral analytics could’ve identified unusually high-interest loans and flagged them for further investigation. Sudden increases in borrowing from nontraditional financial institutions would’ve also been detected as deviations from standard borrowing patterns. Behavioral analytics could’ve exposed complex networks and hidden financial flows by mapping financial transactions and relationships between Evergrande and shadow-banking institutions. Identifying frequent interactions with high-risk entities would’ve raised red flags.

Behavioral analytics can compare financial statements over time, identifying discrepancies and patterns indicative of manipulation. For instance, sudden changes in debt levels or asset valuations that didn’t align with market trends would’ve been flagged. By forecasting expected financial metrics based on historical data and market conditions, behavioral analytics can compare actual reported figures, highlighting significant deviations for further examination.

Financial institutions and regulators should’ve closely monitored Evergrande’s borrowing activities, focusing on the sources and terms of loans. Implementing a regulatory framework that requires detailed disclosures of borrowing terms and conditions would’ve helped identify high-risk loans and prevent excessive leverage.

Significant debt and liquidity issues

By mid-2021, Evergrande struggled with liquidity, unable to meet its debt obligations. The company’s financial health deteriorated rapidly, leading to defaults on loan payments and missed bond-coupon payments. Significant debt-level deviations and abrupt asset valuation changes would’ve triggered alerts. When cross-referenced with historical financial data and industry benchmarks, these anomalies would’ve highlighted inconsistencies in Evergrande’s economic health. The company began defaulting on loan payments and missing bond coupon payments. In September 2021, Evergrande missed a $83.5 million interest payment on a dollar-denominated bond, triggering concerns among international investors.

How behavioral analytics could’ve helped

Continuous monitoring through behavioral analytics could’ve shown early signs of liquidity stress, such as frequent short-term borrowing to cover operational costs and delayed payments to creditors and suppliers. Such patterns would’ve indicated underlying financial instability, warranting closer scrutiny. As liquidity issues became evident, proactive measures, such as enforcing debt restructuring and liquidity support, could’ve been implemented to stabilize the company’s finances. Behavioral analytics could’ve assisted in crisis management by predicting potential defaults and enabling timely interventions to protect creditors and stakeholders.

Powerful tools can alert regulators and stakeholders

The Evergrande fraud highlights the importance of early detection and intervention in preventing financial misconduct. Behavioral analytics offers powerful tools for identifying financial anomalies and enhancing oversight. These tools can find suspicious transaction patterns, financial statements and other relevant data to alert regulators and stakeholders to potential fraud, enabling timely corrective actions and mitigating further economic damage.

Jarvis Curry, Ph.D., CFE, is the internal audit manager for a California state agency and an adjunct professor at Bay Atlantic University and the University of the Cumberlands. Contact him at jarviscurry@aol.com.