Reflections from the DC Fintech Week 2026 Call for Papers
I recently had the opportunity to serve on the DC Fintech Week 2026 Call for Papers Review Committee. My task involved reviewing 68 submissions covering everything from stablecoins and tokenisation to agentic AI, financial integrity, quantum risk and financial inclusion.
I knew it would be a substantial reading exercise. But the challenge I hadn’t anticipated was working out a consistent and fair way to assess submissions that differed considerably in methodology, scope and intended contribution.
How do you compare a paper built around a large empirical dataset with one examining a specific legal ambiguity? Should a well-developed conceptual framework carry as much weight as something that has been tested? And how much importance should you attach to practical applicability when some of the most interesting research is, by definition, ahead of current market practice?
These questions became very relevant as I worked through the submissions and began narrowing down my recommendations.
My background is primarily in corporate and investment banking, wholesale credit risk and institutional digital asset adoption. I’m not a career academic, so I approached the exercise with a practitioner’s perspective, while being conscious that commercial relevance alone isn’t necessarily a measure of good research.
A few things I learnt from this experience.
Judge each paper on its own terms
One thing I found challenging was drawing comparisons across very different papers. Some had substantial datasets and quantitative analysis, while others were examining legal questions or proposing frameworks for problems that haven’t really been addressed before.
I didn’t think it would be fair to favour a paper simply because it had more data. Take a proposed governance framework for an emerging technology, for example. There may not be much historical evidence available to test it against. On the other hand, a paper can have an impressive amount of data and still draw conclusions that aren’t fully supported by it.
So rather than expecting the same type of evidence from everyone, I tried to judge each paper on its own terms. Was the argument convincing? Did the author back up what they were saying? And were they clear about what they couldn’t yet establish?
That felt like a more sensible way to approach the exercise.
Start with the problem
This is something I come across quite often in my own work on digital asset adoption. There’s usually no dearth of ideas about what a new technology could do. We hear about greater efficiency, transparency, accessibility etc, but sometimes the actual problem we’re trying to solve gets lost along the way.
I noticed this while reviewing the papers too.
I was drawn to papers that were clear about the problem from the outset. It could be a gap in existing regulation or an interoperability issue. Maybe a weakness in how certain risks are managed or even an assumption about financial markets that deserved to be questioned.
It became much easier to appreciate the research and what it added to the discussion once I figured out what the paper was trying to address.
Originality didn’t always have to entail new ideas, it could sometimes involve looking at an existing problem differently, or questioning something we’ve taken for granted for years.
Keep practitioner instincts in check
Given my nearly two decades in corporate & investment banking, I couldn’t help looking at some of the papers from an institutional perspective. I kept thinking about how the findings might be used in practice. Would they help a bank or regulator make a decision? Was there something here that could address an existing industry challenge? And how realistic were some of the proposed solutions once you factored in the legal, operational and governance issues that financial institutions deal with?
Nevertheless, I was conscious about this being one of the many perspectives of looking at the research.
I had to remind myself quite often to step away from my practitioner mindset and appreciate each paper for what it was trying to achieve. Having a practical solution didn’t automatically make a paper stronger than others. Some challenged assumptions I’d held for years, while others got me thinking about issues I hadn’t previously given much thought to.
Trust the authors who admit what they don’t know
There were some papers where the authors were open about the limitations of their research. This felt particularly important given how quickly fintech is evolving. Whether we’re talking about tokenisation, agentic AI or new forms of digital money, there’s still a lot we don’t know about how these technologies will develop or behave at scale.
I noticed that some of the arguments I found most convincing were not necessarily those making the boldest claims, but those where the authors were clear about what their research could reasonably establish.
For example, market data can tell us what investors expect, but not necessarily what will happen. A proposed framework might make perfect sense in theory, but that doesn’t mean it has been proven to work in practice.
I didn’t see acknowledging these gaps as a weakness. In fact, I found myself trusting the analysis more when authors were willing to say, in effect, ‘This is what we’ve established so far, but there’s still more work to be done.’
Mind the gap between a good idea and adoption
This was probably the part I enjoyed thinking about the most, perhaps because it’s so closely related to my own research on tokenisation and institutional adoption.
As I went through the submissions, I often found myself wondering what it would take for some of these ideas to work within existing financial markets. A solution might make perfect sense technically, but what happens when you bring in legal requirements, liquidity, governance, operational risks and, of course, the commercial realities of running a financial institution?
These are some of the issues I’ve been exploring in my own work, so I was naturally drawn to them.
At the same time, I didn’t want to dismiss an interesting idea simply because it wouldn’t fit neatly into the way things operate today. After all, research should also make us question whether some of those existing arrangements need to change.
I enjoyed seeing the different perspectives across the submissions. Academics can raise questions that those of us working in the industry might not always stop to consider, while practitioners can bring an understanding of what actually goes into making something work.
I think we need more of that exchange, especially in fintech, where an idea can look quite different once you start thinking about how it would operate in a regulated financial market.
What makes good research
I used a scoring system based on six factors for the initial assessment — relevance, originality, strength of evidence, policy or practical value, clarity and timeliness. This helped me identify the strongest works. Nevertheless, the process still required considerable judgement, particularly when comparing strong papers with very different contributions.
Reviewing 68 submissions was a reminder of just how much interesting work is taking place across the fintech research community. And it made me think more consciously about my own approach to research and writing.
If there is one thing I’ll take away from the experience, it is that a good research paper doesn’t necessarily need to make the biggest claim or offer the most ambitious solution.
I’d much rather finish reading something with a clearer understanding of an important problem, why it matters and what we (do or don’t yet) know about addressing it.
Congratulations to the five winning research teams and all those recognised under Featured Research at DC Fintech Week 2026.
You can read more here: https://lnkd.in/p/e58ZCvF4 | https://dcfintechweek.org/featured-research-2026/
I’m grateful to the organisers and my fellow committee members for the opportunity to contribute. It was a thoroughly worthwhile experience.
Gaya Chandrasekaran is an independent advisor and researcher focused on institutional digital asset adoption and market infrastructure, and an Executive in Residence at Global Digital Finance (GDF). Her research on the institutional architecture needed to scale tokenisation is available as a public whitepaper and a self-paced course for finance professionals.
What Reviewing 68 Fintech Research Papers Taught Me About Good Research was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story.