Artificial Intelligence Verticals (II): Fintech

If you missed the first AI industry analysis on the Insurance vertical, check that out here.

I. Financial Innovation: Lots of Talk, Little Action?

TThe financial sector is historically one of the most resistant to change you might think of. It is then inevitable that big banks from one hand and startups from the other hand are creating a huge break in the financial industry and I believe this is happening not because of the use of a specific technology but rather because of their intrinsic cultural differences, diverse structural rigidity, and alternative cost-effective business models.

In other words, banks do not innovate either because they are too big to quickly adapt and follow external incentives or because they don’t know how (and want to) truly change. This is not simply true in the industry but also in academia, where until the mid-nineties there were no relevant contributions to financial innovation at all (Frame and White, 2002). In fact, in few survey articles (Cohen and Levin, 1989; Cohen, 1995) with more than 600 different articles and books quoted, none of them was related to financial innovation subjects.

Of course, things changed over the last five years, but my opinion is that was really out of necessity rather than a voluntary push-approach from the banking sector.

Financial innovation is, therefore, something which seems to be usually imported rather than internally generated, and often more characterized by a product-innovation rather than a process one (although this might be a controversial opinion, I guess). Given the new technological paradigm (which is tightening the inner strong causal relationship between innovation and growth) it seems natural to wonder whether a better innovation model can be therefore imported by a different (and more successful) sector.

I found that there is a very specific and interesting case of a sector which had to ‘innovate-to-survive’ rather than ‘innovate-to-grow’: the biopharma industry (Baker, 2003; Gans and Stern, 2004; Fuchs and Krauss, 2003; Lichtenthaler, 2008).

Innovation drivers map. The biopharma companies feel more the urgency to innovate and are also more committed to that. The graph I built plots 25 major banks (blue) and 25 major pharmaceutical (red) companies based on their Innovation Impulse and Commitment. The Impulse variable has been built using the number of patents a company filed (a proxy for the external pressure to innovate) and the number of recorded shareholders (a proxy for the internal pressure to innovate). The Commitment shows instead the R&D intensity (net sales) while the size of the bubbles the net income for each company. The data points were obtained by Medtrack, Osiris, and Zephyr in 2014.

II. Innovation Transfer: the Biopharma Industry

The biopharma industry is not a single sector but actually two different ones: the biotech space, populated by smaller companies that drive the research and exploration phase, and the pharmaceutical companies, big giants that through the last century became huge go-to-market and sales enterprises.

Hence, there is pure (risky) innovation from one hand and pure commercialization skills from the other…Is it something that we have already seen somewhere, didn’t we?

The biopharma industry and the financial sector suffer from a strong polarized innovation

What characterizes the industry is that the risky activity lies in the initial development process rather than in the market phase. The problem is not to match customer demand or find a market for your product, but it is actually developing the molecule in the first place. The probability of success is extremely low and the timeline very long (10–15 years) and the 20-years patents give you only a temporary advantage. More importantly, it looks that only 3 out of 10 of the drugs produced are able to repay the development costs (Meyer, 2002) and that most of the companies operate at loss while the top 3% companies alone generate almost 80% of the entire industry profits (Li and Halal, 2002). A tough business, isn’t it?

The biopharma industry is then no longer simply a human-intensive business but also a capital-demanding one. Innovation is not ancillary but it is the quintessential driver to survive. And this is also why they had to identify a range of different methods to foster their growth-by-innovation: R&D, competitive collaboration schemes, venture funding, co-venture creation, built-to-buy deals, limited partnership agreements, etc.

It should be clear by now where I am heading to: the financial industry doesn’t strongly feel the need to innovate as the biopharma sector and it is not experimenting and pushing to create new models that might spread their innovation risk and make it profitable.

The financial industry doesn’t strongly feel the need to innovate

III. Introducing AI, Your Personal Financial Disruptor

Image Credit: Haru1/Shutterstock

By now you might object “All good man, but FS and biopharma are still sooo different, so why should I import innovation models from a sector which is completely different from mine?”. Well, that’s the catch: I don’t think they are.

And the reason why they are becoming a lot more similar is precisely Artificial Intelligence.

AI is creating a strong pressure to innovate for the financial sector and has a development cycle and characteristics which are somehow similar to the biopharmaceutical one: it requires a long time to be created, implemented and correctly deployed (with respect to the financial industry standards, of course); it is highly technical and requires highly specialized talents; it is highly uncertain, because you need to experiment a lot before finding something that works; it is expensive, both in terms of time as well as monetary investments (talents, hardware, and data are really expensive); it is risky and the risk lies in the initial development phase, with a very high-payout but a high likelihood to fail as well.

AI is creating a strong pressure to innovate for the financial sector

But AI is also introducing a completely new speed and degree of trust in the financial industry, which lowers the tolerable mistakes at the same level of the biopharma sector. If your algorithms point out to the wrong product to sell or the wrong book to be recommended, it is not a big deal. If your system misinterprets some signals in the market or while developing a drug though, you end up losing millions in seconds or even losing human lives.

It is then not only stretching out issues that intrinsically belong to the financial sector such as regulation or accountability, but it is also bringing new problems such as biased data or the lack of transparency to the picture (specifically in consumer applications).

And last but not least, AI is making the question mark on the “build vs buy” matter bigger than even in FS, the same as it was in the biopharma industry back in the nineties and that culminated in the current biotech-pharmaceutical dichotomy (if you are wondering anyway, this choice is all focused around on your data capacity, team and project scalability, and uniqueness of the project with respect to your competitors — do you have enough data to train an ANI? Can your team/project scale? Is the ANI unique or something your peers are doing or need to do as well?).

AI is revolutionizing a centuries-old industry innovation flow from the ground

This is why I believe AI in financial services to be extremely important — not much for the specific innovation or product it is introducing but rather because it is revolutionizing a centuries-old industry innovation flow from the ground.

IV. Segmentation of AI in Fintech

Image Credit: TZIDO/Shutterstock

Artificial Intelligence is using structured and unstructured data in financial services to improve the customer experience and engagement, to detect outliers and anomalies, to increase revenues, reduce costs, find predictability in patterns and increase forecasts reliability…but it is not so in any other industry? We all know this story, right? So what is really peculiar about AI in financial services?

First of all, FS is an industry full of data. You might expect this data to be concentrated in big financial institutions’ hands, but most of them are actually public and thanks to the new EU payment directive (PSD2) larger datasets are available to smaller players as well. AI can then be easily developed and applied because the barriers to entry are lower with respect to other sectors.

Second, many of the underlying processes can be relatively easier to be automatized while many others can be improved by either brute force computation or speed. And historically is one of the sectors that needed this type of innovation the most, is incredibly competitive and is always looking for some new source of ROI. Bottom line: the marginal impact of AI is greater than in other sectors.

Third, the transfer of wealth across different generations makes the field really fertile for AI. AI needs (a lot of) innovative data and above all feedback to improve, and millennials are not only happy to use AI as well as providing feedback, but apparently even less concerned about privacy and giving away their data.

There are also, of course, a series of specific challenges for AI in financial sector that limit a smooth and rapid implementation: legacy systems that do not talk to each other; data silos; poor data quality control; lack of expertise; lack of management vision; lack of cultural mindset to adopt this technology.

So what is missing now is only having an overview of the AI fintech landscape. There are also plenty of maps and classification of AI fintech startups out there (probably the best ones are the one provided by CB Insights, in particular this and this), so I am not introducing anything new here but rather simply giving you my personal framework:

• Financial Wellness: this category is about making the end-client life better and easier and it includes personalized financial services; credit scoring; automated financial advisors and planners that assist the users in making financial decisions (robo-advisor, virtual assistants, and chatbots — check CB Insights map if you like); smart wallets that coach users differently based on their habits and needs. Examples include [robo-advisors and conversational interfaces] Kasisto; Trim; Penny; Cleo; Acorns; Fingenius; Wealthfront; SigFig; Betterment; LearnVest; Jemstep; [credit scoring] Aire; TypeScore; CreditVidya; ZestFinance; Applied Data Finance; Wecash;
• Blockchain: I think that, given the importance of this instrument, it deserves a separate category regardless of the specific application is being used for (which may be payments, compliance, trading, etc.). Examples include: Euklid; Paxos; Ripple; Digital Asset;
• Financial Security: this can be divided into identification (payment security and physical identification — biometrics and KYC) and detection (looking for fraudulent and abnormal financial behavior —AML and fraud detection). Examples include, respectively: EyeVerify; Bionym; FaceFirst; Onfido; and Feedzai; Kount, APEX Analytics;
• Money Transfer: this category includes payments, peer-to-peer lending, and debt collection. Examples include: TrueAccord; LendUp; Kabbage; LendingClub;
• Capital Markets: this is a big section, and I tend to divide it into five main subsections:

i) Trading (either algotrading or trading/exchange platforms). Examples include: Euclidean; Quantestein; Renaissance Technologies, Walnut Algorithms; EmmaAI; Aidyia; Binatix; Kimerick Technologies ;Pit.ai ;Sentient Technologies; Tickermachine; Walnut Algorithm ; Clone Algo; Algoriz; Alpaca; Portfolio123; Sigopt;

ii) Do-It-Yourself Funds (either crowdsource funds or home-trading). Examples include: Sentifi; Numerai; Quantopian; Quantiacs; QuantConnect; Inovance;

iii) Markets Intelligence (information extraction or insights generation). Examples include: Indico Data Solutions; Acuity Trading; Lucena Research; Dataminr; Alphasense; Kensho Technologies; Aylien; I Know First; Alpha Modus; ArtQuant;

iv) Alternative Data (most of the alternative data applications are in capital markets rather than broader financial sector so it makes sense to put it here). Examples include: Cape Analytics; Metabiota; Eagle Alpha;

v) Risk Management (this section is more a residual subcategory because most of the time startups in this group fall within other groups as well). Examples include: Ablemarkets; Financial Network Analysis.

Conclusions

I am arguing since the beginning of the article that AI is making financial services and biopharma much more alike, and that the FS industry might learn something from how the other industry innovates.

The reality is that the financial industry has also very specific traits and challenges it needs to overcome.

The biggest difference I currently see in that is the effect AI is having on the physical products market: while in almost any sector AI is used with the final goal of creating or improving new products (and this is true also for drug development, for example) in the financial ecosystem is having exactly the opposite effect. AI is making the industry more digitalized than ever before. Its final goal will be to create the (frictionless) bank of the future: no branches, no credit cards, no frauds, no menial reporting activities. A bank-as-a-platform with modular components that increases our financial literacy and has no physical products or spaces.

It would definitely be a great world to live in. Can’t wait for it.

Reference

Baker, A. (2003). “Biotechnology’s Growth-Innovation Paradox and the New Model for Success”. Journal of Commercial Biotechnology 9 (4): 286–88.

Cohen, W. (1995). “Empirical Studies of Innovative Activity”, in Handbook of the Economics of Innovation and Technological Change, edited by Paul Stoneman. Cambridge, Mass.: Blackwell. Ch. 6, 182–264.

Cohen, W., Levin, R. (1989). “Empirical Studies of Innovation and Market Structure”, in Handbook of Industrial Organization, Vol. 2, edited by Richard Schmalensee and Robert Willig. Amsterdam: North-Holland. Ch. 18, 1059–1107.

Frame, W. S., White, L. J. (2002). “Empirical studies of financial innovation: lots of talk, little action?”. Working Paper, Federal Reserve Bank of Atlanta, N. 2002–12.

Fuchs, G., Krauss, G. (2003). “Biotechnology in Comparative Perspective”. In: Biotechnology in Comparative Perspective. G. Fuchs (ed.). New York: Routledge, 1–13.

Gans, J., Stern, S. (2003). “Managing Ideas: Commercialization Strategies for Biotechnology”. Intellectual Property Research Institute of Australia Working Paper 01/03: 1–24.

Li, J., Halal, W. E. (2002). “Reinventing the biotech manager”. Nature Biotechnology, 20 Suppl (6): 61–3.

Lichtenthaler, U. (2008). “Open Innovation in Practice: An Analysis of Strategic Approaches to Technology Transactions”. IEEE Transactions on Engineering Management, 55 (1): 148–157.

Meyer, F. J. (2002). “Business Models That Biotech Companies Employ”. Enterprise Development KFBS Biotech Speakers Series November 25, 2002

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Look at my other articles on AI and Machine Learning:

• Unsupervised Investments (I): A Guide to AI Investors
• Unsupervised Investments (II): A Guide to AI Accelerators and Incubators
• Who is best positioned to invest in Artificial Intelligence?