Evolution of FinTech

Mobile infrastructure and shifting consumer preferences

With the advancements in mobile network infrastructure (2G, 3G, 4G LTE), consumers have become more connected to the internet—as well as each other—than ever before.

As a result, consumers not only now have a greater resource pool to cross-check and validate information (e.g., checking prices), but also have become a resource pool themselves, in which companies look to target for business.

Today, consumers demand quick, reliable, and quality channels of engagement. They are inclined to place their trust in a company that boasts a dynamic and beautiful website, a well-designed and efficient mobile application, and (if possible) a social user platform for connecting with others using the similar product.

Companies have been forced to make large investments in technology in order to stay competitive among their industry peers.

Consumers have more product choices and are loyal to companies they trust. 

Thus, the technological channels affecting consumer engagement have a direct impact on a company’s ability to market themselves and, ultimately, capitalize on demand.

1. Channels of Engagement

Today, consumers demand quick, reliable, and quality channels of engagement. They are inclined to place their trust in a company that boasts a dynamic and beautiful website, a well-designed and efficient mobile application, and (if possible) a social user platform for connecting with others using the similar product.

Companies have been forced to make large investments in technology in order to stay competitive among their industry peers.

Consumers have more product choices and are loyal to companies they trust. Thus, the technological channels affecting consumer engagement have a direct impact on a company’s ability to market themselves and, ultimately, capitalize on demand.

2. Big data

Over the years, computer processing units (CPUs), random access memory (RAM), and hard drive storage devices have become both more powerful and less expensive. 

Therefore, more companies have been able to purchase and utilize large clusters of computers working in parallel.

To enable machines to work in parallel, the concept of MapReduce was born. With MapReduce, data workloads were split among multiple machines for disk-based processing and reaggregated at the end to produce the result.

With the advent of Spark, that same process has been refined for in-memory processing, in which data workloads utilize RAM that is much faster at processing data (though more costly).

Because big data processing has become more efficient, the time needed to curate and analyse data has also decreased.

Companies have placed an enormous emphasis on technological investment due to the growing feasibility and allure of housing large clusters of machines to drive real-time, data-driven analysis.

3. Cloud infrastructure

Traditionally, server farms—large clusters of machines—required large up-front costs and overhead related to server maintenance. 

But with the inception of cloud computing, companies no longer had to purchase their own servers for their data processing needs; they instead could “rent” servers from another vendor on an as-needed (and, therefore, much cheaper) basis.

The business landscape has become increasingly competitive, as smaller companies now have the capabilities to disrupt markets with applications and services that previously would have required large up-front costs.

Fintech domains

Some areas in the financial industry in which technology has disrupted traditional finance activities include blockchain and financial transactions; robo-advisors and investment management; and payment applications and money transfers.

Blockchain allows for cheaper and more secure transactional validation; robo-advisors utilise machine learning algorithms for portfolio management, thereby reducing overhead costs; and payment applications utilise modern infrastructure such as mobile and cloud-based networking.

With cloud-based networking such as AWS, small start-ups and even individuals can quickly spin up servers faster and on an as-needed basis, minimizing time to deployment and reducing up-front costs. This allows smaller companies to compete more efficiently with larger firms that have existing infrastructures.

Machine learning can be used in lending to more efficiently target customers who have a higher likelihood of paying back their loans, while avoiding those who have a higher likelihood of not paying back their loans.

1. Payments and remittances

Currently representing the largest segment of the FinTech space, digital payments have become increasingly widespread with the growth of e-commerce and mobile device infrastructure. Examples include Venmo, Stripe, PayPal, Square, Apple Pay, Android Pay, Zelle and cryptocurrencies.

Distributing credit card numbers over the internet proved to be insecure (and costly) in the past. Thus, digital payment technologies were designed for not only security, but overall speed and convenience as well.

2. Robo advisors and personal finance

Robo advisors and personal finance companies provide wealth management, investment, and budgetary services that seek to help customers with their overall capital management and investments. Examples include Betterment, Acorns, Robinhood, Personal Capital.

Wealth management solutions are often driven by machine learning with automated trading and portfolio rebalancing. Budgetary services utilize machine learning to scan a customer’s purchase history and identify buying habits to suggest areas in which they can save.

3. Regtech

RegTech companies manage the regulatory/compliance processes within the financial industry through technology. These types of companies use machine learning to identify and prevent instances of fraud, money laundering, and breaches in data. Examples include Apiax, Finform, Trulioo, ClauseMatch.

4. Digital banking

Digital banking consists of online banks that seek to provide higher account interest rates by reducing
the capital overhead associated with physical branches/bank locations. Examples include Ally Bank, ING Direct.

5. Insurtech

InsurTechs utilise machine learning to more efficiently group customers into respective risk profiles and, therefore, provide the right type of insurance product.

Fine-tuning the determination of customer risk profiles minimizes costs to those who would have been lumped together in a broader customer risk profile. Examples include Lemonade, Slice, Ladder.

6. Alternative finance

“Alternative finance” refers to the financial channels outside the realm of traditional finance, such as regulated banks and capital markets, that facilitate capital borrowing and lending. Popular crowdfunding and peer-to-peer lending channels have emerged in this domain. Examples include Indiegogo, Kiva, LendingClub.

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