London (HedgeNordic/Tatja Karkkainen) – Fintech has been frequently on the headlines of financial press. This has been mostly for the growth in the consumer-focused financial services through the new business model companies as the traditional banks have not kept up with catering the demands of retail customers. Fintech nevertheless has been and is catering well the Hedge Fund industry – mostly through facilitating trading ideas, improving efficiency and cost cutting. One of the areas where Fintech help to improve efficiency and cost is Regtech. Whereas hedge funds are under increasing regulatory pressure that can affect instruments traded and increase the costs of distribution, there are third party solutions that cut down the cost in reporting or compliance. The regulatory burden is not expected to become lighter. Another application that improves efficiency and cuts down operational costs is distributed ledger technologies, or blockchain, that is said to mature as a technology as soon as 2025. As a technology, its name has been marred with the troubles with the cloud (the Internet) based blockchain currencies. However, its applications are instantaneous settlements, smart contracts and pre-agreed execution. For instance, this makes a great difference in settling analogical corporate loan transactions.

Peer-to-peer lending brings in hedge funds tradable instruments in alternative income space. Since the credit crunch 2007 and the following regulatory Basel II framework, the banks have been scaling back their lending capabilities and corporate borrowers have had to move on to capital markets for raising finance. Even if the peer-to-peer lending still  is relatively small, it has been growing especially among SME borrowers whose financing needs the banks are not able to meet. Whilst larger companies have had and access to corporate bond issuance, the SME area has had the option to loan through P2P on the basis of their credit scoring. Whilst the name says peer-to-peer, the lender might be an institution – which is increasingly the case. It is expected when this industry matures and possibly grows, so does the interest from more institutional investors. The dynamics of fee structuring and regulation compared to banks are different: when banks can take deposits, leverage or lend money working in a central bank frame work. P2P’s are not able to take risk on their balance sheet or receive or structure any interest income from the deposits, however, in this way, they do not qualify for the capital requirement regulation.  P2P lenders intermediate by pooling and tranching smaller borrowers by credit ratings and passing on this exposure to multiple lenders and by doing that they generate the revenue from fees and commissions received from borrowers and/or lenders. On the platform, lenders can select the specific risk/return and maturity required. P2P’s to succeed however they must find ways prohibit or control “cherry picking” of assets by institutional investors on the expense of retail investors.  There has been also a growth in equity crowdfunding, but this might be something more for private equity companies as smaller amounts of equity shares that can be purchased on these platforms are not particularly liquid.

Besides now widely used big data applications in trade idea research, Artificial Intelligence (AI) takes the data analysis a step further. As a technology, this is well known for its use by companies like Netflix or Google to give intelligent suggestion on the basis of the users previous usage, but also it was recently mentioned on a few media platforms, when it came to AI driven trading systems’ successful response to Brexit. The results were promising when the algorithms traded on the asymmetrical pay off patterns. Artificial intelligence does not only facilitate a brute research capability of assessing (evolutionary computation) and what makes it different from the static quant models, reassessing the market (Bayesian statistics), but it also does it in a way beyond of human capabilities. Traditionally, the quantitative trading strategy driven funds would have a large team to build statistical models, but the machine learning capability can cost much less than a large research team of PhDs, which can improve the fee structures when competing for restrained fee paying investors.

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