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We have all heard the cliché that diversification is the only free lunch in investments. And in technical terms we would normally define diversification as adding investments that have less than perfect correlation with our other assets.  And the more negative the correlation the greater the risk reducing aspects of the new asset. That’s why we created the 60 equity 40 fixed income balanced portfolio. The fixed income is designed to provide low correlation assets to our equities and reduce portfolio volatility. But do bonds really offer lower correlations, or at least enough to meaningfully reduce portfolio risk? 

Financial professionals are embracing AI and machine learning to keep up with the industry transformation.  Skills development is the most important step in this transformation. 

The 2022 Nobel Prize for Physics– awarded to three experimental physicists: Alain Aspect, John Clauser and Anton Zeilinger – evoked a lot of thoughts and emotions. As a grad student, I studied quantum mechanics – the laws for particle interactions at the nuclear level. Now, I marvel at the philosophy of what rules the sub-atomic world VS the “real” world. Well, in the form of quantum computing, the two are coming together by harnessing the “entanglement” theory of quantum mechanics! Implications are huge – massive AI based applications in drug discovery to future of energy to general artificial intelligence to name a few. Stay tuned, I will publish some more in this area...

Artificially Intelligent: The Reality of AI's Limitations in Data Analysis OpenAI’s newest model, GPT-4, promises a utopian future for data scientists where extracting insight from data is as simple as asking the right question. And judging by the speed at which these models are improving, I expect that day will come sooner than most realize. Unfortunately, GPT-4 is not a panacea for data analysis in its current state. While it is particularly astute at generating code to answer a question, it may also @March 17, 2023 Artificially Intelligent: The Reality of AI's Limitations in Data Analysis 2 confidently provide you with an incorrect numeric answer for the same question. The only remedy is to check its work and verify the response, and at that point it may have been quicker to not use the tool at all. The point of this article is not to dissuade anyone from using ChatGPT for data analysis; quite the opposite. The goal is to create some dialogue in this community around the practical use cases and limitations of these AI models today and to encourage hands-on experience with AI technology in order to become a more efficient analyst who works smarter, not harder. In an attempt to test the current state of GPT-4’s usefulness in data analysis, I provided the AI with a small data set of monthly log returns for the S&P 500, 400 and 600 over the last 3 years. I then gave it a series of tasks common in the data scientist’s workflow, including cleaning data, generating descriptive statistics, and creating a forecast of one of the variables. If the AI could perform these tasks well, it would significantly ease the burden of conducting extensive exploratory data analysis. A logical first step would be to clean the data set. To test ChatGPT, I made one observation NA and one an outlier ( 900 ). Let’s see if it can catch it. (Note: Data sets be pasted into the chat box and the AI can infer the rows and columns.) Prompt: Here is a data set of stock index log returns. Can you clean this data for me? date ^GSPC ^SP400 ^SP600 2020-01-31 -0.001629419602 -0.02742027258 -0.04132477276 (…) Response: Artificially Intelligent: The Reality of AI's Limitations in Data Analysis 3 This is quite impressive. It identified the date field and suggested to turn it into a datetime type, it recommended a way to fix the NA , and it found the extreme value ( 900 ). What’s impressive (but impractical) is that it decided to return an example of clean data set written out in a table like the one above. It would not be a stretch to expect the model to return a .csv with the cleaned data in the future, but for now instructions on how to clean will be sufficient. A natural next step would be to calculate some descriptive statistics. I’m going to load in a clean data set and ask it to do that for me. Prompt: Here is a new data set of stock index returns. Please provide me with some descriptive statistics and a correlation matrix. date ^GSPC ^SP400 ^SP600 2020-01-31 -0.001629419602 -0.02742027258 -0.04132477276 (…) Response: Artificially Intelligent: The Reality of AI's Limitations in Data Analysis 4 Here we have our first example of the AI confidently giving the wrong answer. It returned the correct Min and Max for all but the Min for ^SP600 . The Mean and Standard Deviation , however, were wrong. And, while directionally correct, it also managed to get the correlation matrix wrong. Here are the expected values: Artificially Intelligent: The Reality of AI's Limitations in Data Analysis 5 You can undoubtedly see the potential, despite these answers not being correct. The model gets close, and future iterations of these models may include features that can execute better mathematically, but for now, the best practice is to ask it for code that generates an answer to the question instead of asking the AI directly for the answer. Although we’ve seen instability with easier tasks, let’s throw a modeling question at it. The data set we’re using is a time series, so let’s see if it can use an AR(1) model to predict the next value of the S&P 500. Prompt: Using the data set I gave you, can you fit an AR(1) model and give me the prediction for the next value of ^GSPC? Response: It’s important to note that this is generally good code, but it will throw an error. disp is not an argument of model.fit() and statsmodels has updated its packages so that the proper import would be from statsmodels.tsa.arima.model Artificially Intelligent: The Reality of AI's Limitations in Data Analysis 6 import ARIMA but once those two issues are rectified, the code generates a Jan 31, 2023 prediction of 0.012863 . If we then ask it to compute it mathematically it walks through the steps (which is neat!) and even explains the assumptions. It references the data set correctly, and even shows its work. The way it estimates alpha and phi , however, is different from the python implementation it generated, thus resulting in an incorrect forecast: Artificially Intelligent: The Reality of AI's Limitations in Data Analysis 7 Incorrect numerical answer aside, the information it provides is quite useful. It shows how and AR(1) model is formulated, how the parameters are estimated, and provides an example of how the math would be done, should you have proper parameter estimates. The steps are right, it just confidently executes them incorrectly! It is truly impressive how far OpenAI’s ChatGPT has come in such a short time. The possibilities with this technology cannot easily be overstated. It is a natural symptom of the hype cycle to get lost in all the promises implicitly made by new technology, but as people of science and data we must ensure we approach AI in an intellectually honest way. These tools can be useful in several parts of the EDA process as exemplified in this article. It’s particularly good at suggesting paths forward in EDA, generating relevant code, and explaining mathematical methods and the accompanying assumptions in plain English. The responses GPT-4 generates from mathematical or statistical data analysis, however, are fallible and require significant human oversight. Left unchecked, these issues can create adverse downstream effects. It can be as harmless as ~0.05 difference in the correlation matrix for a personal project, or it can be immensely consequential, like a judge using it for a ruling, thus dictating the fate of the defendant.(1) As we see AI models get integrated into our systems and products, the real winners, I propose, will be humans with a curiosity for the technology, an uncommon amount of common sense, and a healthy skepticism of results who can take advantage of the tool without becoming dependent on it. (1) https://www.cbsnews.com/news/colombian-judge-uses-chatgpt-in-ruling-onchilds-medical-rights-case/

In today's rapidly evolving business landscape, companies face the pressing need to adapt and embrace digital transformation to stay competitive. The disruptive forces of technology have revolutionized industries across the board, requiring organizations to rethink their strategies, processes, and customer experiences. To gain valuable insights into this transformative journey, I had the privilege of sitting down with Tamara Zimmerman, Head of EY Wavespace^TM and Innovation at EY Canada. Our conversation focused on the advice they have for companies aspiring to innovate and embrace digital transformation, as well as the necessary conditions for success. The following interview sheds light on the crucial steps and key considerations required for companies to thrive in an era of digital disruption.

(Links were inserted into the article where specific people or events were referenced.)

Cordell: You’ve been working in digital transformation and innovation for over two decades, how would you say it has evolved over the years? Have you found differences in the pace over the years?

Tamara: The pace of innovation is significantly increasing year over year and as technologies become more accessible, the way organizations think about generating new value becomes more mature. In particular, over the last few years we’ve observed that organizations are increasingly focused on generating business value by tapping into new innovative technologies.

Cordell: What characteristics within companies have you found to be necessary to effect change and succeed in their transformation goals?

Tamara: Here are four characteristics that come to mind:

·Openness to challenge the status quo – this includes the need to think differently about the possibilities of transformational change and the untapped value it can bring.  

·Taking a human-centered approach to transformation – including early engagement of stakeholders so that all relevant parties are included in defining and enabling the change journey.

·An organization’s ability to operate in a dual mode – meaning they can continue with current operations without compromising the current business model while simultaneously effectively planning new operational processes.

·A relentless focus on business outcomes – this includes a commitment to tracking and reporting to realize value.

EY’s Human@Centre approach dives into this further by covering the 6 drivers of successful transformation. Within it, my colleague, Gordon Sanford, Partner, Digital Transformation and Future Ready Workforce, shares his perspective and reveals that: “Taking a human-centric approach is key to successful transformation. The human element should not be seen as a single workstream, but as a focal point underpinning and enabling the entire transformation”.

Cordell: What have you found to be the greatest impediment for companies to implement change?

Tamara: As with any change, the greatest impediment to achieving the envisioned results tends to be the quality and scale of adoption. The organizations that get it right are the ones that spend as much time and energy, if not more, on change management. This means taking the time to ensure that all stakeholders involved understand not only how to execute the change but also the why and the value behind it. The earlier stakeholders are involved in the process of defining the change, the better the implementation and results.

Cordell: Explain a little bit about the discovery process you go through with companies when you first start a new mandate.

We typically take an outside-in and inside-out view to helping organizations think through innovation and transformation. There are three key elements that are important to get this approach right from the start:

·Firstly, it is important to start with a clear understanding of the current state and align on a high-level, purpose-driven aspiration for the future.

·Secondly, a clearly articulated ‘exam question’ outlining the opportunity to explore or the business problem to be solved.

·Thirdly, a good understanding of all relevant stakeholder perspectives, coupled with a solid market scan of case studies and success stories from across geographies and industries to get inspired by the art of the possible and avoid getting stuck in ‘status quo’ thinking.

Cordell: Is there any weakness within a company’s culture or structure that you would identify right away as being a problem? And how do you deal with that?

Tamara: Every organization has its strengths and weaknesses, but the most common barrier to realizing impactful innovation is the natural tendency for large organizations to work in silos. Organizations that bring silos together early on, will be better positioned to envision, define, and implement change.

Cordell: What advice do you have for companies that are just starting on the transformation process?

Tamara: My number one piece of advice is to think of this as the new normal. A transformation journey is never complete – fostering an organizational culture that can embrace change is one of the most impactful things an organization can do to drive long-term success.

Cordell: We’ve spoken a lot about transformation at the company level. What about at the individual level. What can individuals at a company do to create innovation and enable digital transformation?

Tamara: The more an individual is curious and willing to learn new things, the more they will be able to contribute to and enable an organization’s innovation journey. That is not to say that all individuals in a company need to innovate – in fact that would probably have the opposite effect of driving a cohesive, strategic transformation plan. Fostering a culture of innovation by encouraging curiosity, learning and even providing an inlet for innovative ideas from professionals who want to contribute to the transformation strategy is an important way for an organization to harness the innovation potential of the individuals that make up the organization.

Cordell: How is EY Canada approaching AI? Given how new these applications are, how do you as an organization learn what to do fast enough to be effective for your clients, and better than your competition?

Tamara: At EY, we help our clients drive exponential transformation through applied AI. We work with clients, ecosystem partners and alliance partners to bring cutting edge solutions to market. For example, we recently announced our strategic collaboration with Microsoft to develop EY Intelligent Payroll Chatbot leveraging Azure Open AI as part of the Next Gen Payroll Platform. We work relentlessly to drive new business value in the market and are proud to have been recognized as a Leader in AI Services in the 2023 IDC Marketscape: Worldwide Artificial Intelligence Services, as well as being named the 2022 Global AI Partner of the year from Microsoft Partner of the Year Awards.

Biren Agnihotri, EY Canada Partner and National Leader, Digital and Emerging Technologies and Data, Analytics and AI, has helped lead EY Canada’s efforts in becoming a trailblazer in AI. Over seven years ago, we became founding members of the Vector Institute for Artificial Intelligence, worked with MILA - Montreal Institute of Learning Algorithms and partnered with MIT CSAIL from an academic perspective. We have also built a sizeable practice of data scientists to help our financial and non-financial services clients in Canada. We got involved early on with our clients to build their AI Centers of Excellence. We have been continuously investing in these sorts of disruptive technologies and recently started our own Generative AI Center of Excellence. We have built many assets in the short period of time since this technology launched and are using our experience to help our clients with their AI strategies. At EY Canada, we are forward-thinking with these technologies, and we focus on choosing high value use cases for our clients. Rather than prioritizing the technology first, we use a sector-first lens. This unique combination of sector expertise, technology expertise and a focus on value generation is a unique recipe for us. We also continuously encourage our staff to grow professionally from an education perspective and pilot many tools or solutions internally to learn from experience and then bring it to our clients. Our clients are always getting a curated view of these AI innovations and we are minimizing their risk.

Cordell: Tammy, more on a personal level, what do you love about your job, and what are you most passionate abut?

Tamara: The best thing about being in this field is the opportunity to constantly be working on new challenges with new people. Every context has its unique circumstances, every organization has its unique culture, and every business has its own way of defining and realizing new value. While we use proven methods and models from our experience to help organizations innovate and transform, there are always new and different kinds of value that are identified, created, and delivered. It’s a really exciting balance between bringing expertise to the table and being part of creating something new in every initiative we take on.

Cordell: And how do you, an industry veteran, get better at what you do and improve your own skill set?

Tamara: Every project is an opportunity to learn and grow. I think that as long as you never assume that you have all the answers, you will learn new things and pick up new skills in everything you do. Of course, it is also important to proactively take initiative to sharpen your skills and capabilities by pursuing relevant professional development opportunities. At EY, we are lucky to have excellent, high quality, learning opportunities. For example, I recently had the privilege of attending the Disruptive Technology Program at our EY Tech University in Palo Alto. I also make it a point to be proactive in staying up to date by reading the latest thought leadership from trusted sources while also making the time to meet with colleagues in both my internal and external networks to keep a pulse on what is happening on the ground.

Cordell: Tammy, thank you so much for talking with me. We really appreciate it!

Acknowledgements: The DigitalHUB team would like to thank Biren Agnihotri and Gordon Sanford of EY Canada for their contributions to the article.