When Have Traditional Risk Assessment Models Failed?
When traditional risk assessment models hit a snag, the insights from top finance professionals become invaluable. In this article, a Finance Partner and a CEO share their experiences and solutions. The discussion kicks off with analyzing a client's project for a nuanced understanding and wraps up with the importance of using metrics for non-normal returns. Discover all eight expert insights that could redefine risk assessment strategies.
- Analyze Client's Project for Nuanced Understanding
- Incorporate Social Media Sentiment Analysis
- Integrate Geopolitical Risk Data
- Negotiate Price Based on Structural Issues
- Adapt to Rapid Market Changes
- Prioritize Agility and Live Analysis
- Seek Guidance from Financial Experts
- Use Metrics for Non-Normal Returns
Analyze Client's Project for Nuanced Understanding
At Renown Lending, while we utilize risk assessment models to evaluate lending scenarios, there have been situations where traditional methods didn't fully capture the nuances of a client's situation. One such instance involved a property developer seeking bridging finance for a high-value project. The traditional model flagged the client as high-risk due to their irregular cash flow and lack of pre-sales, factors that might discourage conventional lenders.
Instead of relying solely on the model, we took a deeper dive into the specifics of their project. By analyzing the quality of their property portfolio, market demand in their development area, and their proven track record of successfully completing similar projects, we were able to identify mitigations that weren't evident in the initial assessment. Based on this nuanced understanding, we structured a bespoke first mortgage loan that addressed their funding gap while ensuring appropriate safeguards for repayment.
This experience reinforced the importance of blending data-driven models with qualitative judgment. By being adaptable and taking a holistic approach, we were able to support the client's goals while managing our own risk effectively—showing that flexibility can often reveal opportunities that rigid models might overlook.
Incorporate Social Media Sentiment Analysis
I discovered a major blind spot in our risk models when they completely missed the impact of social media sentiment on our SaaS company valuations in 2021. Our traditional models were focused on metrics like churn rate and CAC, but weren't picking up how quickly negative Twitter discussions were affecting our portfolio companies' growth trajectories. After that expensive lesson, I started incorporating social listening tools and sentiment analysis into our risk assessment framework, which has helped us spot potential issues weeks before they show up in traditional metrics.
Integrate Geopolitical Risk Data
Traditional risk assessment models once failed us during a period of unexpected market volatility driven by geopolitical events. The models we were relying on used historical data and standard deviation metrics to predict potential risk exposure. These models assumed that market conditions would stay within expected ranges based on past trends. What they didn't account for was the sudden and severe impact of international sanctions, which froze key financial transactions and caused ripple effects across multiple industries.
The failure became evident when some of our clients in the export sector experienced severe cash flow disruptions. The models flagged the industries as medium risk, failing to capture the immediate and systemic risks brought on by the sanctions. As a result, some of the lending structures we had approved based on those risk scores suddenly appeared overexposed.
To address this, we implemented real-time risk monitoring by integrating external geopolitical risk data with our internal models. This allowed us to adjust exposure limits dynamically based on the evolving situation. We also moved quickly to restructure affected clients' loans to provide liquidity while mitigating our exposure. This involved shorter repayment cycles paired with collateral adjustments, ensuring both parties were protected.
Negotiate Price Based on Structural Issues
I have encountered many situations where traditional risk assessment models failed to accurately predict the potential risks associated with a property. One such instance was when I was approached by a client who was interested in purchasing an old residential building for investment purposes.
At first glance, the property seemed like a great opportunity due to its prime location and low asking price. However, upon further inspection and research, it became apparent that the building had some serious structural issues that were not disclosed by the seller.
Normally, traditional risk assessment models would have flagged this as a high-risk investment due to the potential costs of repairing these structural issues. However, my experience in the real estate market and knowledge of local regulations allowed me to properly assess the situation and come up with a solution.
Instead of completely discarding the property, I worked with my client to negotiate a lower price that would factor in the cost of repairs. I also brought in a team of structural engineers to thoroughly evaluate the building and provide us with an accurate estimate of the repair costs.
With this information, we were able to make an informed decision and move forward with the purchase at a revised price. In the end, my client was able to acquire a valuable property at a discounted rate and make necessary repairs while still staying within their budget.
Adapt to Rapid Market Changes
There was a time when traditional risk assessment models failed to predict the rapid disruption in the tech market due to the emergence of new AI technologies. At Software House, we relied on historical data and conventional models to gauge the potential impact of new innovations, but these models couldn't account for the speed and scale of change. As a result, our initial forecasts for market risks and growth potential were off the mark, leaving us unprepared for the rapid shift.
In response, we adapted by incorporating more dynamic, forward-thinking approaches into our risk assessment, focusing on real-time market trends and integrating a broader range of qualitative factors, such as customer sentiment and regulatory changes. We began conducting more frequent scenario analysis and stress testing to anticipate potential disruptions. This shift allowed us to navigate the evolving landscape more effectively and seize new opportunities, proving that flexibility and adaptability in risk assessment are crucial for staying ahead in a fast-changing market.
Prioritize Agility and Live Analysis
At the start of 2020, risk models underestimated the scale of the global pandemic. These models are experience-based, and thus don't always work well for the unexpected. I am a day trader, so I already knew the market could get volatile. COVID-19 took center stage, and the default models were oblivious to the hurricane. That difference presented both a problem and an opportunity.
My solution was to prioritize agility and live analysis rather than strict risk management. I stayed up to date on headlines, economic stats and social media buzz. This enabled me to identify new patterns and modify my trading strategy accordingly. For example, I invested in defensive stocks where a lockdown could be projected to spur demand, such as health care and consumer staples. Classically developed models may have predicted stability but this real-time model was far more effective at handling the market's unprecedented volatility.
The experience reveals how important both quantitative and qualitative evaluation can be. Risk models are the first thing to consider, but they're not the only ones to guide investing. With the smarts, intelligence and drive to get through it, I survived and even made money for my clients in an unpredictable market.
Seek Guidance from Financial Experts
Hello,
As a Financial Health Coach and certified General Lines Agent, I've learned the importance of seeking proper guidance when navigating complex financial situations. One situation where this became critical was when a client's traditionally "safe" investment portfolio began to underperform due to unforeseen market volatility.
The client's portfolio was built around traditional risk assessment models that heavily favored bonds and blue-chip stocks. When interest rates unexpectedly rose, their bond-heavy allocation started losing value, exposing vulnerabilities in the assumed stability of the portfolio. Recognizing that the usual risk models hadn't accounted for this scenario, I turned to financial analysts and market experts to better understand the underlying issue.
With their help, we explored alternative strategies and assessed how different asset classes could mitigate the impact of rising rates. Their insights helped me and the client craft a revised approach that better addressed the challenges at hand.
This experience taught me the value of collaboration and leveraging the expertise of specialists when faced with unexpected challenges. It reinforced that even with a strong foundation in financial knowledge, seeking out the right help can provide fresh perspectives and solutions that wouldn't have been apparent otherwise.
Use Metrics for Non-Normal Returns
Most traditional risk models struggle with new asset classes like alternative investments. Take the classic Sharpe Ratio, which measures risk using the Standard Deviation. This method assumes a normal, bell-shaped distribution of returns. However, complex assets like hedge funds rarely follow this pattern, causing risks to be miscalculated. To solve this, we use risk metrics better suited for non-normal returns, such as the Value-at-Risk, the Sortino Ratio, the Omega Ratio, the Conditional Value-at-Risk, and more.
Another example is why Tactical Asset Allocation (TAA) models don't work well for hedge fund investments. Traditional TAA relies on asset class categories for diversification, but hedge fund indexes often have low correlation with actual fund returns. This can lead to poorly diversified portfolios. To fix this, we focus on factor analysis to identify key macroeconomic factors that drive manager performance, instead of relying on strategy labels.
