''Finance thinks it does development best, and in some cases it does''

What can you tell us about your business?

Geospatial Insight provides data and insights derived from the analysis of satellite, aerial and drone imagery. We apply machine learning to capture key features and events, which enables us to produce evidence-based intelligence. We service multiple markets, but for readers of North Starr, the two most relevant are insurance/insurtech and financial services/fintech. In insurance, we provide catastrophe response information and building and environmental information which services risk-based pricing and brokerage models. In financial services/fintech, we’re increasingly finding those same datasets relevant to mortgage valuation and risk assessment, but like many in the alternative data industry we’ve tended to focus more on buy-side alpha-oriented datasets, including physical commodities and consumer footfall.

What has been your journey to current position?

I worked for 20 years at MathWorks. For those with quant, engineering or mathematics backgrounds, that’s the company behind MATLAB. For about half of that time, I was Industry Manager of the Financial Services team. As a geographer among mostly engineers, I could feel out-of-place, but I was also quite good at being able to see the wood or forest while others came to terms with individual trees and so I was able to help the company understand how the many different types of people in finance, economics and insurance were using these engineering toolsets. While there, I was immensely privileged to participate in the evolution of the financial services industry, what I call the Seven Ages of Quant. I’ve also been lucky to have had a holistic bird’s eye view of the industry crossing buy-side and sell-side, niche hedge fund and bulge-bracket bank, navigating front, back and middle offices, into insurance and then into the very different cultures of central banks, NGOs, think tanks and regulators. Towards the end of that time, economists and insurers aside, serious modelling was increasingly a standardized function of the middle office for tasks like risk-based accounting, while the fast-and-furious types of the front offices and hedge funds were taking up open source. My fun quota was diminishing while fewer doors opened to us. More by luck than judgement, 18 months ago I attended a satellite in financial services event courtesy of a freebie from a training services company I had an affiliation with. At the time, I struggled to reconcile the content presented to the finance applications, that I had encountered at least. However, after sleeping on things, attending a couple of thought-provoking alternative data events soon after (most sentiment-based, but usually incorporating satellite chat on their panel discussions), it all began to make sense. I realised how I could both open my mind and get back to the leading edge of finance, while working with the satellite specialists to help translate these great data sources into something useful for the financial services industry and I somehow found myself a job with one of the speakers from the event I had crashed.

What interested you in this space?

Two things: first, getting back to being at the leading/bleeding edge of the industry – it’s lovely to have hedge funds and front office traders interested in what we’re up to. Second, I’m now working for a company where geography intersects with – and is integral to - finance, rather than among applications where it’s abstracted out.

How have you settled into the business?

It’s exciting, challenging, scary, vibrant and hand-to-mouth in equal measure. It’s been a roller coaster, but good for me. I’ve learnt to have sleepless nights, but also challenge myself and be challenged. 

What lessons did you learn in your previous role?

Finance is a unique industry. Most other industries have established processes and model standards. They manage model development cycles well, which is why planes fly safely in the skies and cars (mostly) are increasingly reliable. Finance thinks it does development best, and in some cases it does, e.g. applying agility across development cycles. However, it’s also a technology basket-case, incorporating fad after fad into last decade’s increasingly aging, inefficient and risky legacy stack. On the other hand, the business environment is fast-paced and vibrant, run by people with creativity to see new things and willing to take a risk (perhaps with an inflated bonus in mind) to make good and not-so-good things happen. There are reasons things happen first in finance. At both my last engineering company and my new (according to LinkedIn) “defense and space” company, I’ve conveyed that financial services is a world on its own. You must embrace it on its terms, and not try to impose norms of other industries onto it, and I love it for that.

Where do you see the opportunity for you in the UK and European market?

Alternative data has been led from America and sometimes that means the rest of the world doesn’t always get as much dataset focus as they’d like. That can be useful to those of us building datasets and insights in Europe and the UK.

What are some of the major challenges facing the industry that your company overcomes?

First, a realization that place is important. Geographically contingent information, whether for identifying mis-priced trades, capturing leading edge information on an asset or helping with due diligence, risk assessment and in advanced stress testing is now deemed important. Second, what I call Geofinance and others call “spatial finance”, the blending of geography with financial analytics, modelling and insights, is a real and socially useful thing. It contributes to sustainable finance, in practical terms helping to construct ESG models or incorporate climate scenarios into risk and stress testing models. It’s helping make finance more purposeful than it has been years, more attractive to those considering entering the industry, and we’re part of that trend, particularly in terms of the provision of real data.

How does your company differentiate itself from its competitors?

Imagery, satellite and aerial in particular, when compared with other alternative data sources is globally applicable and non-intrusive. You can count cars in a car park or at a factory, but you can’t identify individuals, unlike payroll information or transaction data. Second, in relation to other geospatial vendors, our experience in insurance is unique. While financial services were perhaps first off the block to use geospatial information, for example counting cars in Walmart parking lots a decade ago, insurance has adopted it much more since. It’s had to as policies are postcode and environment-dependent. Now that finance is picking up geospatial technology again, we’ve gained invaluable experience.

Where do you see the future of the market heading?

In five years time, you will be able to look up images, identify features and draw out structured time-series data on your favourite terminal platform. We want to be part of that mega-trend.

What do you feel are the biggest obstacles facing your organization in the industry?

The alternative data trend or fad represents both an opportunity and a hindrance. The industry is talking about it, learning to use it, but also there are too many vendors offering too many data-sets. As of now, satellite and imagery data-sets have not been top of the list of “altdata” shopping lists, in part because imagery has been perceived as largely static, low-frequency, overly irregular given issues like cloud and too often delivered as an image rather than a time-stamped data-set.

How do you plan to overcome those obstacles?

Some of this is the luck of timing. Technology is changing rapidly. Imagery is increasingly dynamic. Machine learning facilitates automated ongoing analysis. We’re getting better at understanding and structuring data in a way that the industry can ingest, and spatial information feeds are near-at-hand. As noted earlier, our insurance experiences have helped considerably. Also, financial organizations across the spectrum – from regulators to investment managers - are now building use cases, becoming more familiar with the technologies and willing to engage with them. These are truly exciting times to be at the intersection of imagery and financial analysis.

What makes your company an employer of choice?

The diversity of our client base helps. We’re committed to finance, but not dependent on the fads. Some aspects of our company too are fabulously ultra-modern, an ability to work remotely for example. We work hard and late into the night and over weekends, but flexibly.

What are your plans for 2019 and beyond?

My plan is to ride the waves, catch a good one, and in doing so make a difference to the industry, my company and my job satisfaction.

What areas do you see as most ripe for disruption by technology?

Quant and financial modelling. The financial model is and will remain a staple, but in an age of big data, data science and massive computational power, assumptions and levels of abstraction need not be so constraining. Imagine a world where baskets of assets are priced according to spatial time-series parameters, or portfolio theory incorporates point-in-time information reflecting the global components of constituent assets. No longer will portfolio analysts depend on the rational efficient market blanket assumption that price captures all of that and more. Practically, there was no point in challenging the staple theories a decade ago because of computational limitations. Now, aging theory is gatekeeper, but that’s changing.

At Geospatial Insight, Steve Wilcockson is Product Manager for RetailWatch, a consumer sentiment analysis dataset and drives client-facing activities in the financial services segment for all Geospatial Insight products. Steve was formerly Global Industry Manager for Financial Services at the engineering software company MathWorks, developers of MATLAB?, where Steve worked in the field for over 20 years among model builders and implementers such as quants, quant developers, economists, actuaries, algo traders, portfolio managers and risk managers. Steve has a masters degree in Geography from the University of Cambridge and the University of British Columbia.

Steve was the first person to host one of our FinTech Influencer events for advanced technology special interest groups - these are invite only events we host throughout the year along with The Realization Group. We have a report on Steve's event here if you missed it and if you would like to attend future events please email [email protected]

@humansoffintech is a diversity campaign to celebrate all people working in FinTech. This was set up by Harrington Starr to support equality in the recruitment style of the industry. To be featured, email [email protected]