Steve Wilcockson

Product Marketing at Quantexa

Message Posts: 26 Comments: 9

Bio I enjoy model-led and data-driven technologies in financial services. I work with quants, data scientists, data engineers, developers and business stakeholders on their research to production workflows on the buy-side, sell-side, front office, middle office, in insurance, fraud detection, and more. Career History I've worked most recently for decision intelligence (fraud detection, risk decisioning) firm Quantexa since April 2024. Prior to Quantexa, I worked for KX, the popular kdb time-series database and language q which is used for real-time, high-frequency and quant-led trading and risk management, especially in tier 1 front offices. Before that, I worked at Azul, a Java runtime specialist facilitating Java's use inn financial low-latency and big data applications like trading, back-testing, pricing, payments, fraud detection and risk management. Before that, I was at altdata and sustainable finance satellite imagery firm Geospatial Insight, and until June 2018 I was Industry Manager for Financial Services at MathWorks, aka MATLAB.

Blogs

Capital Markets Technology

Niklaus Wirth, and How Financial Computing Can Help Save The Planet

11 Jan 2024

More than a few computing luminaries have sadly passed over recently, unsurprising given many pioneers from the fifties, sixties, and seventies reaching end of life. One who caught my eye was Niklaus Wirth who departed this world on New Year's Day. He was a Swiss computer scientist, associated most with ETH Zurich, but he also had sixties Silicon V...

Artificial Intelligence and Financial Services

Three GenAI Terms Financial Practitioners Learnt in 2023

03 Jan 2024

2023 was a troublesome year for many on our planet – wars, violence, population displacement, catastrophes, extremism, higher costs of living and poverty. Folks working in our industry were comparatively lucky, some of us invigorated by the exciting GenerativeAI tornado. Just as HFT transformed the vocabulary of capital markets in the 2000s, and d...

Big Data

Bitemporality, Helping Lower Costs for Financial Services Use Cases

15 Nov 2023

I was at an “all things data and analytics benchmarking” event last week. It was fabulous, most major banks were represented, also the hottest of hot hedge funds, a chunk of exchanges, and some of not-the-usual vendors. I found it refreshing, like most events where tangible analytics gets discussed. My early career was quant /data science/analytics...

Artificial Intelligence and Financial Services

From RAG to Riches in a GenAI World: Some Jargon Explainers and Current Trends

06 Nov 2023

What is RAG? Here's the simple 30 second definition, A deeper dive will follow. RAG (Retrieval Augmented Generation) is the buzziest word on the GenAI internet right now, more jargon to confuse the uninitiated. RAG, stands for retrieval augmented generation. It simpy means you can apply and RETRIEVE knowledge in front of the LLM, to AUGMENT your LL...

See all Steve's blogs

Steve is Commenting on

Blair Institute sets out 'progressive vision' for fintech

Putting the Labour/Blair thing to one side and responding to the fraud expansion comment, I do see UK Fintech leaders in real-time fraud detection and complex fraud detection (tax, AML, fincrime, etc). I've worked for two such firms. Complex financial crime is easier to detect than it used to be. Money launderers, organized criminals and tax dodgers have far fewer places to hide. Also, streaming real-time analytics, including model inference at scale in-memory and with distributed memory, with much-improved model deployments (types and agility to recalibrate) is powerful and increasingly democratized. That said, I'd also note that fintech favourite, crypto, is the criminal's friend and I'd hope there's a responsible crypto strategy in the bag of tricks of the next UK Govt.

19 Apr 2024 Reply Read article

3 GenAI Use Cases for Capital Markets: The Power of the Vector

Very good. I'd not seen it! Quite agree. Cassandra is my old stomping ground!

13 Jul 2023 Reply Read article

3 GenAI Use Cases for Capital Markets: The Power of the Vector

Hey Ketharaman Swaminathan. Thanks for the comments! Great to hear from you. This dates me, but my baseball knowledge is centered on Randy Johnson and Ken Griffey Junior (apparently there's a junior version of junior I saw the other day). Ex.3 - having just watched a webinar earlier today on the failure of stress testing models (admittedly capital/risk ones rather than bog standard portfolios) with GFC and more recently Covid, I wonder if I should have waited before posting it because I might have added extra layers. My inspiration at the time was less the model itself but more around the democratization of the human linguistic prompt that any non-Quant/coding PM might make. After today's webinar, though, I might be tempted to throw in the real extreme scenarios into the mix, the black swan scenarios which the LLM might be able to make sense of, but my model perhaps can't "imagine". Thus the LLM output could adapt weights and correlate up dependent factors, possibly make sense of related factors that inform the "copula"-like thing which seeds the simulation, etc. Your point is completely valid!

13 Jul 2023 Reply Read article

Is Embedded Finance the Future?

Great article and extremely well-written, but the use of terms like "embedded technology" and to a lesser extent "embedded finance" has software engineering/development connotations, which gets morphed yet more when applied to finance.

22 Sep 2020 Reply Read article