Data Science Inputs to FinTech and Digital Transformation: My Comments

Introduction

Digital transformation for us in our company started post-Covid-19 when we moved completely from our physical location to our online websites and social media platforms. Now, we want to advance further to make our products and services more easily accessible and operational virtually and faster.

Digitization vs Digitalization

We have gone from digitization well to digitalization where we are working from time to time to make sure we automate customer service through data science and machine learning. As we make this happen, we are moving into digital transformation. In essence, digital transformation involves not just moving business online but also automating the organization’s operations and using fintech solutions.

Business Objectives

As a company, the sole essence of our business is to move professionals abroad or help them work remotely earning in US Dollars, to seek greener pastures overseas, help companies to make money online and get more clients. So, our financial inclusion framework will work better virtually than physically or being restricted to a location. It is great lesson to be unique with company technological strategy not copying other companies' own blindly. It is crucial for an organization to know its aim and objectives well enough in order to easily initiate their digital transformation.

RegTech and InsurTech

To make sure that digital transformation is void of bankruptcy or deception, there is a need for regulatory bodies online and virtual insurance companies. Indeed, regtechs are needed to assist fintech and insurtech companies to become more transparent, rewarding and fraud-proof.

Blockchain, Cryptocurrency and Cybersecurity

A major way that FinTech, RegTech and InsurTech companies safeguard their customers from theft and cybercrime is through the application of blockchain technology. Blockchain is more or less a decentralized cloud computing system where no cloud-based provider exists like AWS, Google Cloud or Microsoft Azure. So, encryption in Blockchain can be said to be the cybersecurity mechanism in form of digital signature that AI makes possible. Indeed, ownership of money in the blockchain cryptocurrency is much more traceable and trustworthy than traditional money market.

Other Applications of Blockchain

However, blockchain has transcend beyond fiat money and FinTechs. From this, one can deduce that the tech that makes Cryptocurrency possible is here to stay in and out of banks. Courses' certifications on Corporate Finance Institute and even this 10*1000 Tech for Inclusion sites are protected through such blockchain concepts not only in the world of cryptocurrencies. And, Cybersecurity is the big niche that fintech and financial companies needs to invest more to protect their customers and themselves.

Collaboration Outcome

One + one = three when the good players in the fintech, the financial industry, wealthtech and regtech collaborate to generate dividends for their investors. I have learnt that customers are meant to be at the center of the digital transformation model while culture and strategy should be in its core as well. Put differently, customer satisfaction is the goal of digital transformation while the positive organizational structure and competitive tech strategy are the pathway to get there.

Artificial Intelligence, Machine Learning and Deep Learning

To transform digitally, companies need some or all of these aspects of data science in that title order. In most cases, the data ingredients and techniques may be more than that. Moreso, big data is required for machine learning modeling and training with lots of options firsthand and even later independently to test with new set of data.

Downsides of these Methods

AI and/or ML is limited in multiple scenarios. This means that training robots or ML models can be delimited since each iteration can only be used in a single tech situation. Although, they could help out in so much conditions of influence but not in all.

Amazingly and sadly, even the works of AI/ML engineers and data scientists are already being automated too. This begs for the question; is the duty of these tech professionals going to be obsolete in years or decades to come?

ANI, AGI and ASI

It is doubtable that AI will move into ASI - Artificial Super Intelligence as films portray it. So, can we go against such creativity? Or just allow them to focus on AGI - Artificial General Intelligence since we are still going to get there? Or stay at the ANI - Artificial Narrow intelligence level? In all, limitations still exist in the practicality of data-driven fields but sooner or later more solutions will be created or enhanced to control their adversities or eliminate their problems.

Cloud Computing

I like the old saying that 'To operate a cloud platform means to keep on changing the engine of a plane while it is still running or flying'. In other words, data intelligence period requires different cloud computing capabilities and powers to be constantly updated and renewed. Cloud computing is that capability which is quite better than data center administration in terms of its elasticity, security and reach. It takes the data collected by a sole company or user, computes and analyzes the data for clients to benefit from some capabilities, products and services.

Self-Aware AI

Self-aware AI is more related to ASI - Artificial Super Intelligence. It is more alike with those expressed in movies. Knowledge graph is one of the most revolutionary applications of AI that indeed helps us to advance from ANI (Artificial Narrow Intelligence) to AGI (Artificial General Intelligence) in the short run.

Human Creativity

In addition, the most important mental addition to the requirements of digital transformation in business is human creativity which will always help Tech for Financial Inclusion. The more a tech leader or technologist is creative in providing tech solutions for innate problems faced by organizations, the more the companies can transform digitally in the industry. As of now, a creative IT specialist would use the Web 3.0 and open finance to create more enabling atmosphere for easier, trustworthy and faster fintech products, revolutionary financial transactions and sustainable tech inclusion initiatives for growth businesses, SMEs and even their customers. Indeed, there is a need for systems that could out-live teams in an organization which will help the company and its people to achieve their sole WHY and not different immutable rigorous tech strategy.

Market Add not Market Share

Lastly, I like the idea of market add in place of market share. The digital transformation attempts of one company should be embraced by other competitors in the industry and leveraged when the condition arises. It is all about the customer value proposition rather than our own business profitability ratio or margin. To sum up, to get a business to succeed in this technology era and join the unicorns or just reach the IP exit level, companies must focus on their customers' painpoints and how to scale rapidly based on the organisation's objectives.

Conclusion

These ideas of mine are shared here but gotten from the two recently 10x1000 Tech for Inclusion programs: Fintech Foundation Program Flex and Fintech Expert Program Flex I took in order to learn about FinTech and top companies in the field. Kindly share with me your own thoughts in the comment section. I would love to read about them!