The Relevance of an Adaptable Model Risk Management for Financial Institutions in an Ever-changing World

Rodanthy Tzani, Head of Model Risk Management, New York Life Insurance Company

Rodanthy Tzani, Head of Model Risk Management, New York Life Insurance Company

I am a Physicist by background, earned my Ph.D. in Theoretical Particle Physics at City College of CUNY, and worked in academia for several years at various universities in the USA and Europe before switching to finance.

I started my career in finance in 2001 (just before the September 11 event) as a rating analyst in the Credit Derivatives Group of Moody’s Investors Services. My work consisted of rating tranches of structured products with a variety of underlying portfolios, which included corporate and asset back securities, bank loans or tranches of CDOs (known as CDO squared), and other exotic derivatives or transactions, e.g., insurance CAT bonds or stand-alone companies. The work included reading lengthy contracts (Indentures) agreed between the issuing banks and the investors and translating these contracts (the deals) into excel-based models, which were used to produce an evaluation of the creditworthiness of each deal (the rating).

Every transaction was represented by a model. Information about the underlying portfolios as well as assumptions and parameters were inputs in the model, calibrated to ensure that the output (rating) represented the current market conditions. Going through the mathematics of these deals allowed me to evaluate the risks vs pricing, and I started wondering: ‘how financial institutions actually make money?’ Ratings reflected the quality of the deals, and they had an impact on pricing. As these deals proliferated between 2004 and 2006, they became more complex, with increased innovation in the portfolios and features, and they priced quickly and (usually) much more favorably for the issuing banks.

Signs of the 2008 crisis appeared in early 2007. I had moved to a new position in 2006 and was working in the risk management of a company that, among other things, managed and invested in structured products. I was managing numerous models of the company, including models that priced tranches of structured products and models that calculated risk and capital.

The 2008 crisis was claimed to be due to a model. The Gaussian Copula, a simple physically motivated model based on the normal distribution, had been adapted by Dr. David Li, an actuary and statistician, and was used by financial institutions to price tranches of CDOs, CLOs and mortgages. Some market participants claimed that the prices provided by the model were optimistic and did not represent reality, and that as a result the financial institutions ‘misunderstood the risk’ and increased their leverage, and that created the bubble that busted in 2008 and resulted to the crisis. Model risk management was in its infancy at that time.

In the midst of the crisis, the Copula model was deemed unfit for pricing because the prices ‘received’ by the model would imply correlations of the underlying securities higher than 100 percent! This is a mathematical impossibility, and it was an eye-opening moment for me. Here is the explanation: The market, in its desire to make money, had been arbitrarily ‘pricing’ tranches of CDOs by assuming the ‘desired price’ and forced the model to imply correlations of the underlying securities that produced that desired market price! These implied correlations peaked above their natural maximum of 100 percent, to allow the model to produce the market prices. Well, every model is wrong when it is misunderstood or misused. The need for managing the use of the models became apparent to the technical experts and risk managers in the industry.

Insurance companies played a crucial role in the 2008 crisis as well. Similar to banks, large insurance companies had ‘misunderstood the risk’ they were taking as they priced their investments using the same Gaussian Copula model. In early 2007, I participated in the Investment Committee in the company I was working and presented the internal model results for a specific transaction. However, the model results were too onerous and were not consistent with the senior management’s expectation and desire to invest in the underlying transaction. The results produced by the model were then completely ignored as ‘not relevant.’ The company invested in this transaction and realized heavy losses a year later, in 2008.

The 2008 crisis was a ‘regime changing’ event and after that regulators scrutinized the models and techniques used by the financial institutions. This was the era of the Value-at-Risk (VaR) and Stress Testing models.

"The 2008 crisis was a ‘regime changing’ event and after that regulators scrutinized the models and techniques used by the financial institutions"

In 2009, I took a position as a senior examiner at the Federal Reserve Bank of NY in the Models and Methodologies Department. The Federal Reserve hired numerous market experts across various disciplines to help with understanding the situation and addressing the market disruption. Models were at the center of the market collapse, and they needed to be assessed. Domestic and Foreign Systemically Important Financial Institutions (SIFIs) were reviewed and evaluated for their risk-capturing modeling techniques. Qualified examiners (mostly Ph.D. holders) assessed the banks’ VaR models and their risk capturing techniques to evaluate their ability to measure Capital, their underlying pricing models to ensure that the VaR and Capital models received accurate information, and their Stress Testing and CVA models. As a result, increased emphasis and importance were given to the models.

The 2008 crisis played a critical role in raising the level of understanding of the importance of models by both financial institutions and regulators. Model awareness increased and the SR-11 7 was born. This marks the institution of a formal requirement for model risk management to oversee model development, implementation, and use.

Fast forward, in early 2020 another event perturbed the normalcy of the market. The COVID pandemic interrelated with a low Interest Rate (IR) environment stressed the models of many companies, and especially those with Life products. Life Insurance companies were not incorporating pandemic events like the COVID-19 one into their stress testing and/or did not incorporate low IR scenarios in the pricing and valuation of their products and reinvestment assumptions. Uncertainty in the market increased and companies needed to revisit their models, reassess, reevaluate, rebuild and/or update them. The value and importance of Model Risk Management was highlighted.

This brings us to the recent explosion of artificial intelligence techniques and machine learning models. The ideas and development of AI have been evolving for some decades (since the 1970s). It is not a coincidence that this turned into an explosion right now. The volume and complexity of data that is now being generated have increased the power of AI techniques, but also the need for those techniques to analyze the data. AI, and specifically ChatGPT, created a sudden developmental burst and financial institutions are trying to adapt and take advantage of the opportunities and come ahead in using AI technologies. It is critical for the industry to think of and prepare for the potential consequences.

The development of Large Language Models and AI in general represents a ‘quantum leap’ in the way financial institutions will solve problems. AI can be unpredictable, and it is crucial to have a solid risk oversight of the models and algorithms that implement it. A new challenge is ahead of us in model risk management.