I have been writing about the importance of data in banking and what ICOs could bring to banks and financial institutions. In this short article, I would like to sum up what’s new in the financial services industry according to the World Economic Forum Report “The new physics of Financial Services”.
First of all, it is remarkable to notice how major banks and asset managers are leveraging their automated Back Office departments and using them as new revenue stream. To be more specific, Black Rock, with the Aladdin initiative, Ping An, with the One Connect project, are offering the so called “Back Office as A Service” and helping other banks through the automation of repetitive tasks. The collective use of these automated tools brings more efficiency in the sector, allowing a continuous improvement virtuous cycle.
When we think about emerging technologies, we usually have the perception it is just about buzz words and unreal utopian scenarios, but I’m convinced about the fact it is not at all the case. Artificial Intelligence and intelligent automation are enabling workers to perform more human tasks, by reducing errors and generating time savings when running repetitive tasks.
Cloud Computing has already revolutionized the way companies stock data, information, and process operations. Nowadays Blockchain is allowing a mind shift, a paradigm change from a centralized business philosophy to a decentralized network of peer-to-peer disintermediated transacting parts. Quantum computing has the promise to break down some of the advantages brought by blockchain thanks to cryptography. Nonetheless, those rising technologies, if combined can unleash an immense potential. Let’s think, for instance, at the combination between Blockchain Smart Contract and Artificial Intelligence: honestly, the finest automated execution with tamper proof stamp of decentralized consensus.
For many reasons, some of these technical improvements, still have to overcome obstacles and conceptual barriers. For instance, financial services do not fully rely on Cloud Computing as they do not feel ready to stock data on someone else’s machines. Blockchain was born to cut off intermediaries, and this very reason makes banks feel unconfident when thinking about the technology.
Luckily, we see different progresses concerning Artificial Intelligence and automation. Specifically, thanks to automation, financial institutions are seeking for innovative differentiators in order to reach new clients. If in the past, market leaders were those financial players able to propose lower price, highest speed and easier access, today, a leadership position in the market cannot be reached without the following:
– Ability to capture attention
– Developing ecosystems
Indeed, thanks to the rise of API and Open Banking, building platforms is not enough anymore. Big Tech like Microsoft, Apple, Amazon taught us a new dimension is required. Similarly, to Microsoft which enables developers to build partnerships with other players, financial institutions need to do the same by creating ecosystems and securing a leadership position among all the stakeholders.
Due to this, new frameworks for shared accountability and collective solutions will be mandatory and its correct establishment is crucial to the success of the network. As a result, financial system safety can improve, compliance will become a commodity, also because of the rise of RegTech, and cyber-risks will be the new operational risks. Depending on the position within the network, or the data alliance, there will be winners, who will take out all advantages when it comes about governance, and losers, who will just follow their partners. In addition to this, another important aspect to be considered, is the ability to manage talents. As a matter of facts, all business sectors are now converging toward technology adoption. Financial Services are not an exception. In this context, why a young developer should go for a job in a bank, instead of going for a position in one company of the GAFA?
This issue can be easily solved because of the infinite amount of IT needs a bank or an Investment Manager might face. As an example, some of the most important Investment Managers use cases are as follows:
– Deep Learning for cutting edge investment analysis
– Leveraging emerging market potential
– Running in-depth macro-economic analysis
– Wealth Management chatbots’ deployment
– Advisor Customization
Some of the most prominent example are:
– Goldman Sachs’s Deal Link is automating half of 127 steps of an informal IPO checklist
– Centrl is automating due diligence platform Assess 360 provides repository for all due diligence contents
– Clearwater is enabling automated data collection to speed up report’s creation
– Deutsche Bank and IBM Watson entered in a partnership to improve Back-Office and Front-Office processes
– Natixis Equity Derivatives uses Machine Learning to detected anomalous projections generated by stress-tests.
To sum up, the future is limitless and innovation comes from combination of existing technologies and expertise as well as relentless and good work.