A recent article posted by InternationalBanker.com cites a recent report that in 2020, fueled in part by the pandemic and increased use of digital platforms, global venture capital investment in fintech was the second highest level ever after a record year in 2018 clocking in north of 42 billion.
Considering the fact almost any e-commerce platform became such a necessary service to a global population forced into lockdown, it is logical that funding would flow towards the digital payments and banking-as-a-service (BaaS) space where emergence from the worst of the pandemic was not so easy to forecast nor project when the end to it all could be defined.
While the first half of 2020 saw a slowdown in global investment, it was the second half that had a huge reversal signaling what may be a banner year in 2021 if the last three quarters of 2020 are any indication. The chart below provided by KPMG in their Pulse of Fintech report available for download clearly illustrates the reversal.
While the most popular trends in fintech investing have been leaning towards the aforementioned payments and banking services sector, it’s the public markets in the coming months that will see popular trading software apps such as Robinhood, Stash and Etoro either intending to come public or have been hinting at entries in 2021.
Once a few of these apps come Public, the prior mentioned statistics of global investment into Fintech may fall to new records this year.
These popular trading apps for retail investors have varying business models with some incorporating banking services as part of their overall offerings.
As these various popular trading apps compete aggressively with each other for client acquisition and assets, the new prevailing trends of fintech investment interest are indicating that unless some of these companies intending to come public or other new fintech providers embrace these new trends, the investing community market may not be as forgiving and a few of the business models may lose rapid share to their counterparts who do embrace the trends.
The future four trends in Fintech according to a recent Forbes article are:
- Embeddable Infrastructure
- Vertical Software with Embedded Fintech
- Applied Machine Learning
- Intelligent Infrastructure
Now while these trends are broader based in their adoption according to the Forbes article, let us explore how they relate to the retail application trading space and how we here at Fintatech can help new software development entrants meet the new wave of potential investment participation.
This category trend relates to the multitudes of industries who are embedding pre-built fintech solutions directly into their software and delivering them through modern APIs.
As it relates to the trading platform software development cycle, clients should recognize many facets of how their platform need to potentially conform to the traditional broker dealer business model or even the banking as a service model if not independently but even better integrated with an already established player who is well funded, established, licensed and regulated.
For example unless the trading software client intends to form his/her own broker dealer or bank, the path of least resistance and lower costs are better spent as a broker neutral type trading platform which connect via APIs to a third party Broker Dealer or Bank thereby saving the headaches of expensive capital requirements, clearing costs fiduciary roles and back end record keeping requirements resulting in saving tens of thousands if not hundreds of thousands of dollars in startup and maintenance costs.
Through this process, the trading software client now has the ability to offer traditional brokerage operations, banking services, lending or other payment services simple utilizing the software as a tool and the 3rd party provider’s services simply are embedded solutions within the technology stack.
Vertical Software with Embedded Fintech
An example here traditionally can mean any product or service offering that embeds fintech into its business and apply fintech to their specific industries such as a ride sharing app that allows its users to have their own digital wallets. Another example could mean the digital tokenization of a particular product.
Now as it relates to the trading software client, this can mean bringing in other new vertical offerings to the traditional trading app. Using the latter example above, the trading platform can be the sole facilitator of trading that particular digital token of almost any product or asset in question. The trading software app can also provide digital wallets and payment processing for almost any other product or service making the app now a liquidity tool if not a traditional custodian of the assets or wallet suggested.
Essentially as a traditional software trading application trades various asset classes, brand new asset classes can now be introduced to the infrastructure and lately there have been many new startups launching digital assets in almost everything imaginable from real estate to art, music and collectibles.
Applied Machine Learning
The traditional explanation of this category means such open banking type applications allowing user authorized access to financial data such as a company’s payroll or bill payment ledgers, cash flow reports and related. This is allowing new fintech players to analyze all this data using machine learning to get more insights into customer behaviors and other various business metrics.
This principal trend for the trading software app has multitudes of importance both in old traditional styles of financial analysis to now even new reporting datapoints that have become popular in analyzing trader sentiments, volume data unleashing insights into short interest, or any of the other hundreds of technical indicators available on the traditional trading terminal and applied machine learning can serve to open up several business service models to replace the dying traditional brokerage commission trading model and in fact this is where hundreds of new analysis solutions are popping up everywhere in the last few years.
These machine learning models can also lead to analyzing the typical brokerage account to determine lending models, new risk management practices and even again open up new business models to the new software trading client provider.
Machine learning can open doors for all types of access to capital thereby improving standard margin lending practices which can benefit all of the parties involved.
This trend is perhaps the most essential and is spurring all kinds of new innovation. This trend speaks to the development of complex payment system integrations and data analysis of all sorts of industries.
Old payment infrastructure processes such as ACH transfers, fed wire systems, and other paper transactional systems are now in the early days of obsolescence as new payment systems are opening up cross border efficiencies, saving costs and time and radically changing how we all may be moving money in the future.
The back-end infrastructure development process and systems required are no different in their application to the trading software development cycle. Integrating third party APIs for new verticals and services, providing back-end transaction logs and trade journals or other portfolio and risk management modules, ingesting data and/or providing peer to peer engagement if not direct client engagement solutions all require serious attention to the intelligent infrastructure enabling all these processes in one single platform.
The four mentioned recent trends listed above is where much global investment capital is searching for, and the trends are showing no sign of slowdown as the globalization of money, assets and wealth management software solutions are more in demand today than ever before.
We here at Fintatech can help provide the framework or otherwise help you build bespoke the software solutions that are changing the entire Fintech industry. Contact us for more information.