In this episode of Calculated Conversations, I had the privilege of speaking with Mr. Matlhogonolo Sebate, a dynamic consultant, private equity investor, and data science executive. With a career spanning data quality, financial intelligence, and investment management, Mr. Sebate has mastered the intersection of data, finance, and policy, which is a combination that is shaping the future of financial services.
As a leader in multiple industries, Mr. Sebate has played a crucial role in leveraging AI and data analytics to drive investment decisions and financial strategy. His expertise in financial structuring, business operations, and policy advisory has made him a key figure in the evolving landscape of AI-driven finance.
Here’s what he has to say:
1. You’ve worked in data science, finance, investment, and policy-making. What led you to combine these fields in your career?
I have always wanted to pursue a career that would tackle development challenges. Having specialized in economics and statistics, using data science was my natural approach to build long-lasting solutions that are data-driven. Whereas my background has been in a quantitative field, I started my work with data quality, which laid the foundation for me in my current role of leading a team of Analysts and Statisticians to produce trends and typologies. I was first introduced to policy-making by my second job, where I worked in the Ministry of Finance and Development back in 2007. During that time, I was involved in architecting the financial system, which around that time embarked on pushing some major reforms, including building the financial regulation system, modernizing the capital markets, and building the financial system’s integrity.
2. You manage several companies across finance, technology, and investments. How do you prioritize and make decisions?
First and foremost, my companies are a structured part of an investment portfolio held by Afrivestment Capital (Pty) Ltd, which is not just a consulting firm but also my investment holding entity. With this in mind, I have built a very clear business plan, implemented with an experienced Analyst to analyze deals and also advise on investments. I also hold positions in client relations and project management in a number of engagements done through my firm, which offers me dedicated time for executing assignments and portfolio management of the firm.
3. What advice would you give to young entrepreneurs looking to start in these industries?
The majority of businesses fail because of management. In my view, it’s important to have a structure, plan, and dedicate more time to execute. It is also important to have advice, whether technical or a mentor offering support, but it’s important to always bounce your ideas with a trusted confine. Advisors also offer support and connections. They are your invisible sponsors since they will market you both directly and indirectly. Entrepreneurship is a journey, and I would like to believe there is nothing boring like a lonely journey. As an entrepreneur, hone your interpersonal skills. Expand your horizon and enjoy the journey as it goes.
4. How do you see AI and automation impacting financial services in the next 10 years?
There is a growing argument that people need banking and not banks. Another notion that banks have become technology companies. Over and above this, in my view, the adoption of technology as a transaction currency has shaped the way we envisage financial services in the next 10 years. To think of it, these emerging technologies have not only transformed operations but even the way we relate to money. The potential of artificial intelligence (AI) and automation has been the most talked-about business trend in recent years. While the excitement surrounding these technologies is understandable – the fourth industrial revolution, et cetera, it is essential to recognize that their integration into financial services is not as a sudden breakthrough but rather as a continuation of a technological journey that has been unfolding over decades.
Financial services have a long history of leveraging technology to enhance operations, improve efficiency, and drive growth. AI and machine learning are among the advancements enabling this ongoing evolution.
One of the most prominent applications of AI in financial services is algorithmic trading across equities, listed derivatives, rates, and foreign exchange. Sophisticated algorithms and machine learning models analyze vast amounts of market data, identify trading opportunities, and execute trades with precision and speed that far surpass human capabilities. The impact of these technologies on efficiency and productivity is vast, delivering major cost, risk management, and performance improvements, while retail investors get incredible product access, real-time price discovery, and zero transaction costs.
The integration of technical agents and automation in financial services has also led to significant changes in the workforce. As machines take over repetitive and data-intensive (and expensive) tasks, human capital is being reallocated to more strategic and value-added roles. This shift is reshaping the skill sets required in the industry, with a growing demand for professionals with technical and quantitative skills.
As technology enables firms to do more with less across most of the liquidity curve, human capital with the requisite fundamental investing skills has self-selected towards areas like private equity, where a nuanced approach is required to make up for the data gaps that otherwise allow a machine to learn, driving enormous growth and returns in that corner of the financial ecosystem.
5. What’s the most valuable lesson you’ve learned in your career?
Every opportunity is a learning curve. I was hired as a Quality Assurance Assistant in my first job, and I must admit, it was a boring job at the time. Because I was fresh from university, I wanted to crunch numbers, but there I was tasked with ensuring data quality for the organization. It was very clerical, and I didn’t feel valued, looking at the nature of my qualifications at the time. I transitioned into business advisory and then onto the entrepreneurial journey. It was during this phase where I met Cloud ERPs, Analytics software such as Tableau, Alteryx, etc. 20 years later, I joined an organization which was marred with data quality. To think of it, there was neither analytics nor data science with bad data. Being tasked with the opportunity of setting up an Analytics Unit, the first thing was to make a decision on how we were going to deal with data quality. The experience of how I learned to approach data quality from my very first job was very useful. It has become the bedrock of producing meaningful ‘intelligence.’
Final Thoughts
In our conversation, Mr. Sebate provided invaluable insights into the power of structured planning, data-driven decision-making, and strategic investing. From his early experiences in data management to his current leadership roles, his journey exemplifies the importance of adaptability, continuous learning, and cross-disciplinary expertise.
His perspective on AI’s role in financial services and investment strategy highlights the need for professionals to not only understand data but also the policies and market forces that shape financial ecosystems. As automation continues to evolve, staying ahead requires a blend of technical skill, strategic foresight, and an entrepreneurial mindset.
A huge thank you to Mr. Matlhogonolo Sebate for sharing his knowledge and experience!
What do you think will be the biggest shift in AI-driven finance over the next decade?
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