Four years ago I found myself in an unlikely position: co-founding Toucan, a climate-fintech start-up. My background is in geospatial data science and biology, not finance and economics, but an intuition kept pulling me forward: if we want a livable planet and the room to keep innovating, the global financial system is one of the biggest leverage points to focus on. Toucan was an experiment in reprogramming the financial system to support living systems.
Throughout my work at Toucan, I often felt off-balance. I could discuss the finer points of a geospatial data pipeline or smart contract architecture, but when I tried to explain our work to portfolio managers and risk officers I slipped into the gaps between three conflicting thickets of jargon: tech, climate, and finance.
Last week I took the chance to strengthen my footing at the London School of Economics Sustainable Finance & Impact Investing executive education programme. Five long days, thick binders, and a seminar room full of people running global teams — with me there asking dumb questions, offering my perspective as a technologist and entrepreneur. Here are some insights from the week:
We began with portfolio theory: expected return, variance, the Sharpe ratio, cost of capital. These equations shape how institutions decide whether a climate project is “investable.” Breaking the back of finance jargon has already proven to be a huge unlock in my understanding of how things fit together.
Mapping debt, equity, insurers, regulators, asset owners, asset managers, and rating agencies — all with classmates who had occupied those seats — made the market feel less abstract. In a system this complex, a high level mental map is a powerful tool to navigate it successfully.
Case law and policy briefings showed how “acting in the best interest of beneficiaries” is being re-interpreted in real time (May 2025). Ignoring climate risk is edging from permissible to negligent, but boundaries differ by jurisdiction and evolve quickly. Anyone designing new products has to watch this space month by month.
We dug into the difficulties of measuring and quantifying “impact”, in its many forms. Here my data science background — and insight into the limitations of data — led me to voice some spicy opinions. Still, I left appreciating the complexity of the problem, and the value of taking a systematic approach to the problem. More soon on how I hope my work with UMD will help make impact measurement and verification a bit more tractable …
The numbers are blunt: when investors “divest” from dirty company, the action doesn’t move the needle on corporate behavior or the cost of capital — in fact, it means pro-climate votes are absent. I was reminded of some of the flaws in our theory of change with Klima DAO, namely that in liquid or deep markets even large volumes of capital shifting around don’t do too much … Divestment can still matter symbolically, but engagement does the heavier lifting. That said, engagement requires a huge amount of specialist work — so it’s complicated.
The week’s single most useful lesson had little to do with formulas. From the outside, senior financiers look powerful. Up close you see the constraints they operate within: regulatory capital rules, client mandates, quarterly reporting, reputational risk, personal career incentives. Real agency comes not from removing those constraints but from recognising them early, and designing within them.
I left Toucan some time ago, but the goal that took me there remains: build bridges that let innovation, capital, and environmental need meet in the middle. After last week at the LSE I feel better equipped to do that translating work without slipping into jargon. The finance world didn’t suddenly become simple; it became legible — and that is enough to move forward.