The Role of Noise in Risk Management

The Role of Noise in Risk Management

The Role of Noise in Risk Management: A psychologist’s take on an often underappreciated and often misunderstood topic.

Prior to working in management consulting, I was a career academic. I taught a variety of psychology courses at a university and conducted cognitive psychology research (although I also engaged in large-scale replication work trying to address fundamental issues in how psychological science was conducted). Through my doctoral training, research, and professional experience in the field, I began to become acutely aware of how misconceptions or misunderstandings of key elements of psychology and neuroscience have crept into society’s day-to-day topics. Risk management, as a discipline, is no different. While there are certain elements of psychology within risk management, such as things like cognitive biases, which are reasonably well understood, communicated, and applied to risk management problems, there are others where I still see gaps (which is only natural).

One key element I think most people inherently understand about psychology is that as a discipline, it approaches the same problem or situation from multiple layers of analysis which all mesh together in our larger understanding and appreciation of our own reality. This ranges from rather large macro-level elements within social psychology and reduces all the way to cellular (and in some instances sub-cellular) levels within the nervous system. My area of expertise sits in the cognitive layer with a large emphasis on how neurological principles and cognitive function are related. From a basic science perspective, that is what always interested me as a researcher. One of the areas in this layer I was fascinated by was the concept of noise. Thanks to popular researchers, like the late Daniel Kahneman, the idea of noise in risk management has gained some traction in recent years. However, the element of noise I was interested in was far more fundamental than what Kahneman was describing. I was interested in neural noise and how it changes, and fundamentally increases, across the lifespan. I won’t go into all of the details here but essentially; neural noise influences every single element of cognitive processing from the sensory input stage all the way to behavioral output. Furthermore, the distribution of noise fundamentally increases over time as we age (2nd law of thermodynamics).

When people think about noise in risk management, I think they often miss the boat a little bit. They understand the elements that Kahneman describes in the book, which is a great starting point, but they miss the bigger picture. Every single thought, sensation, behavior, social interaction, and so on, is influenced by noise. Even the simplest of perceptual tasks are measurably influenced by noise. In the field of experimental psychology and psychophysics (no not crazy physics as fun as that would be) these concepts have been fundamental to how we think about perception, behavior, and the underpinnings of decision-making for over 150 years (dating back to Weber-Fechner Laws) and beyond. Concepts like “discriminal dispersion” which was introduced by Thurstone almost a hundred years ago (building on Weber’s and Fechner’s foundations) in his paper “The Law of Comparative Judgement”. Furthermore, these elements have been studied and expanded upon consistently over the past 50 years. Work from Lester Krueger and Philip Allen (and many others there is a long list of great researchers that spring to mind, but I won’t list them all out) inspired me to study this more and more. Interestingly, all of the probability concepts that are covered in the more quantitatively rigorous areas of risk management and probability theory align perfectly with these ideas which was largely my inspiration for joining Hubbard Decision Research in the first place.

Tying this back to risk management and the broader inspiration for me writing this came from a recent podcast titled “Unlocking Resilience. Mass Media & Prioritization.” with Brandon Daniels and David Merritt (I’ll put the link to that podcast at the bottom as it’s worth a listen). There is a lot of great substance in the podcast, but one key point stuck out to me which was made by David Merritt around the 19-minute mark in the podcast and it has to do with how to prioritize human attention and, ultimately, optimize those precious finite human resources. While this was one item in a broader conversation, it made me stop on my walk with my dog and write down a note on my phone’s notepad. Human attention, human cognition, and human performance in general is fundamentally influenced by noise and developing a risk quantification framework that buttresses and supports decision makers in the face of inevitable (and neurologically fundamental) noise is essential. Risk management at large organizations is complicated; there are so many moving pieces. Providing the right people with the right tools to make better strategic decisions under uncertainty and target the right risks ultimately requires a consistent, unambiguous, and stable approach to risk management despite the internal noise that we deal with as biological beings.

Doug Hubbard repeatedly says when we talk to clients (and he’s not alone in the risk quantification space with this) that “I’d rather not have to do that math in my head”. The field of quantitative risk management is, in essence, eclectic with psychology being an important, and often overlooked area. Understanding how foundational concepts within psychology apply to risk management is a competitive advantage. Essentially, every single cognitive process is in some way limited or capped. Recognizing those limitations and developing solutions to minimize the impact of those limitations (such as developing quantitative risk models rather than relying on unaided intuition or replacing sub-optimal qualitative scoring approaches which actually add more noise with even simple quantitative models) will protect organizations from their best (yet most flawed) asset, their people (most flawed might be an exaggeration but it helps get the point across).

Unlocking Resilience. Mass Med… – Cybercrime Magazine Podcast – Apple Podcasts

Revolutionizing Agricultural Productivity: Project Prioritization in Crop Science

Revolutionizing Agricultural Productivity: Project Prioritization in Crop Science

  • Client: A Global Leader in Agricultural Sciences
  • Industry: Crop Science
  • Objective: To forecast and prioritize new corn varieties to maximize future product success.

Executive Summary

Our client, a trailblazer in agricultural sciences, sought to gain a predictive edge in the crop science arena by being able to accurately forecast which crop varieties from their diverse portfolio would yield the most success in the upcoming years. With the help of HDR’s robust Risk Return Analysis (RRA) model, they could optimize their selection process and set the stage for groundbreaking efficiency in crop production.

A-high-level-dashboard-showcas_image.png

Challenge:

In the dynamic field of crop science, the challenge was multi-faceted: predicting agricultural product success in an environment fraught with uncertainties such as climate change, market demand, and regulatory shifts. Our client needed a measurement and a prioritization system that could sift through the complexities and forecast the performance of prospective products in their pipeline.

An-image-of-a-farmer-at-a-cros_image.png

Solution:

The HDR team crafted a comprehensive RRA model that integrated historical data, current market trends, and expert insights. The model enabled a data-driven approach to evaluate the myriad of potential corn varieties and isolate those with the highest potential returns, ensuring that the client’s resources were allocated to products most likely to succeed.

A-visual-metaphor-of-a-kernel-_image.png

Results:

Our predictive model served as a crystal ball for the client, providing highly accurate forecasts that the client was able to verify against actual market data. As a result, the client was empowered to make informed decisions that enhanced their portfolio performance substantially.

An-infographic-featuring-ascen_image.png

Conclusion:

By leveraging the RRA model developed by HDR, our client achieved a phenomenal leap in their ability to forecast and prioritize future corn varieties, marking a new era in agricultural productivity. This strategic advantage not only propelled their research and development efforts but also reinforced their position as a visionary leader in crop science.

Measure What Matters

Strengthening Financial Fortresses: Transformative Cybersecurity Workshops in Banking

Strengthening Financial Fortresses: Transformative Cybersecurity Workshops in Banking

  • Client: An Established Midwestern Financial Institution
  • Industry: Banking
  • Objective: To develop the bank’s cybersecurity framework through in-depth workshops, empowering internal teams to manage and improve their cyber risk analysis using HDR’s models.

Executive Summary

With cyber-attacks becoming increasingly sophisticated, a prominent financial institution recognized the urgent need to elevate their cybersecurity posture. They partnered with HDR to enhance their internal capabilities in identifying, assessing, and managing cyber risks. HDR’s model provided a structured and consistent framework, while their coaching ensured the bank’s team fully grasped the complexities of cybersecurity risk analysis, enabling them to independently handle their defense strategies effectively.

An-image-illustrating-a-compre_image.png

Challenge:

Despite having an existing cybersecurity protocol, the financial institution’s approach to risk analysis was inadequate for the increasingly dynamic threats they faced. There was a significant need to refine their strategy to quantify and manage cyber risks more effectively. The challenge lay in adopting a method that was both comprehensive and could be seamlessly integrated into their day-to-day operations.

An-image-depicting-a-digital-b_image.png

Solution:

HDR addressed this challenge head-on by providing expert-led cybersecurity workshops tailored to the institution’s context. An HDR-crafted template version of their advanced cybersecurity model was shared, along with strategic training sessions. This enabled the bank’s team to extensively train and eventually take full ownership of their cyber risk analysis. Furthermore, HDR furnished the team with additional tools like the Lens modeling method and various estimation techniques, thoroughly equipping them to maintain robust cybersecurity independently.

An-image-representing-the-mome_image.png

Results:

The intensive training and the practical adoption of HDR’s models yielded remarkable results. The financial institution’s internal security team could now effectively identify potential threats, assess their impact, and prioritize mitigation efforts profoundly. This strategic transformation empowered the institution to safeguard its assets, customer data, and reputation more robustly than ever before.

An-image-symbolizing-the-trium_image.png

Conclusion:

The advanced workshops and model implementation orchestrated by HDR culminated in a comprehensive boon to the financial institution’s cybersecurity measures. The strengthened defenses, coupled with the ability to conduct in-depth internal risk analyses, established the bank as a paragon of digital safety within the industry, ready to take on the future’s challenges.

Measure What Matters

Revolutionizing Risk Management in the Insurance Sector Through Cybersecurity Assessment

Revolutionizing Risk Management in the Insurance Sector Through Cybersecurity Assessment

  • Client: A Global Leader in the Insurance Marketplace
  • Industry: Insurance
  • Objective: To enhance cybersecurity risk management by developing a comprehensive risk and control model tailored for the insurance industry.

Executive Summary

In a world where cyber threats are rapidly evolving, a pioneering insurance company sought to fortify their cybersecurity risk posture. The organization recognized the need for a robust cybersecurity risk model that would cater to their unique industry requirements. In collaboration with HDR, they embarked on a journey to dissect and categorize their cybersecurity risks into high-level macro risks and specific threats to business-critical applications, culminating in the creation of an innovative likelihood model and a NIST-based control model.

An-image-showcasing-layers-of-_image.png

Challenge:

The insurance company grappled with categorizing and assessing cybersecurity risks in an industry plagued by sophisticated threats. The task at hand was to identify and stratify the potential risks associated with high-level macro variables and business-critical systems, and determine the probable impact on the organization, such as the number of records that could be compromised in a breach. Additionally, there was a pressing need to establish a baseline for cybersecurity measures that aligned with recognized standards.

An-image-of-a-digital-lock-imp_image.png

Solution:

Addressing the complex challenge, HDR adopted a holistic approach that mapped out the insurance company’s cyber threat landscape. A detailed risk model was constructed, outlining macro risks, vulnerable business-critical applications, and establishing a likelihood of incidents. Every application was examined to estimate the potential loss of records in the event of a cyber incident. Furthermore, a foundational control model was created, drawing from NIST guidelines, to enhance the client’s cybersecurity protocols and safeguard against imminent cyber threats.

An-image-depicting-a-dashboard_image.png

Results:

The engagement with HDR delivered a tailored cybersecurity analysis that empowered the insurance company with a nuanced understanding of their risks and provided robust mechanisms for risk management. The risk and control models developed not only met but exceeded industry standards, positioning the client to proactively tackle cybersecurity threats and protect their vast repository of sensitive information.

Conclusion:

The strategic partnership with HDR was instrumental in equipping the insurance provider with advanced tools for identifying and mitigating cybersecurity risks. The project outcomes have substantially uplifted the client’s resilience against cyberattacks, showcasing a significant leap forward in securing the company’s digital assets and maintaining their industry-leading position.

Measure What Matters

Optimizing IT Infrastructure for Enhanced Banking Performance

Optimizing IT Infrastructure for Enhanced Banking Performance

  • Client: A Leading Silicon Valley-Based Financial Institution
  • Industry: Banking
  • Objective: To conduct a cost-benefit analysis for consolidating data center operations, which involves advanced Monte Carlo models and data-driven dashboards

Executive Summary

A top-tier financial institution in the competitive Silicon Valley landscape faced the challenge of updating its IT infrastructure for improved performance and cost efficiency. The project involved a detailed cost-benefit analysis of data center consolidation that leveraged Monte Carlo simulations to generate innovative data-driven dashboards.

Challenge:

Amidst an evolving digital banking landscape, the client struggled with inefficient data center operations that led to unnecessary costs and operational delays. The lack of a robust framework for financial analysis hindered their ability to forecast and quantify the benefits of IT infrastructure upgrades.

Solution:

To tackle this, a top consultancy firm crafted a detailed plan that encompassed modernizing the client’s data analysis techniques through Monte Carlo simulations and sophisticated dashboards. This enabled a holistic view of prospective costs, benefits, and risks associated with data center consolidation.

Results:

The elegant solution provided transparency and data-driven insights into the decision-making process. Post-implementation, the firm recorded a substantial improvement in operational efficiency and a marked reduction in costs, propelled by better capacity planning and resource utilization.

Graphic-illustration-of-positi_image.png

Conclusion:

With the new analytics framework in place, the financial institution can now make strategic decisions regarding IT investments and data center operations with greater confidence and accuracy, securing a competitive advantage in the technological forefront of the banking industry.

Measure What Matters

Evaluating Desert Restoration for the United Nations Environmental Program

Hubbard Decision Research conducted an analysis on behalf of the United Nations Environmental Program (UNEP) to determine the impact of restoring an overgrazed area of Inner Mongolia known as the Kubuqi Desert. Once a green pastureland, this area along the Yellow river was overgrazed and became a desert.  Over the last 30 years a Chinese corporation, Elion, has been working to restore this to a productive agricultural area with thriving communities.
(more…)