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Figure 1. Financial Technology Cover [1]



Financial technology (Fintech) is the term used to describe information technologies that assist the improvement of the delivery and use of financial services. ​Fintech aids companies and their consumers to smoothen their financial operations by utilizing specialized hardware and software. Fintech, the word, is a combination of "financial technology". [2]

Many believe that fintech emerged in the 21st Century. The term was initially used to the technology employed at the back-end systems of established financial institutions in the 20th Century. [3] ​Since then, the industry shifted to more consumer-oriented services.[4] Fintech now includes different sectors and industries such as education, retail banking, fundraising and nonprofit, and investment management, just to name a few.[4] The development and use of cryptocurrencies are also part of Fintech.[4] Although cryptocurrencies see the most headlines, most of the money continues to move across the traditional global banking industry with a multi-trillion-dollar market capitalization.[4] For a comprehensive video explanation, click on the image below.

Background and Information of Fintech

As mentioned before, FinTech is often seen today as the new merger of financial services and information technology. However, this symbiotic relationship has a long history and has evolved over three distinct time periods known as financial globalization, digitization of finance, and the emergence of new players.[5][6]

Financial Globalization

Diners Club Card [7]

The first transatlantic cable was finally installed in (1866) in cooperation between the United Kingdom and the United States of America,[5][3] and the Fedwire followed in 1918 in the USA.[5][3]As of today, the Fedwire is used by banks, businesses, and government agencies for large, same-day transactions.[5] This era ended with the introduction of charge cards such as the diner’s club and Mastercharge, better known today as Mastercard[8] in the 1950s and 60s. The introduction of the Diners Club was possibly the first Fintech product aimed to improve the customer's journey. The story has it that the Diners Club was conceived after its founder "Frank McNamara" forgot his wallet during an important diner with his clients.[8].[7] After the embarrassing moment passed, he imagined a product that would prevent such a situation from happening again.[8] It is in this moment that we can see the potential for every day people to imagine a service that would prevent an everyday problem with the help of information technology. In addition, scalability was another factor that made charge cards so appealing. In just a year after its release, the Diners Club card had approximately 20,000 cardholders.[8] The difference between a charge card and a credit card is the payment requirement at the end of every month. Although purchases on a charge card are made on credit, the customer must pay his balance in full at the end of the month.[8] [9] The first credit cards as we know them were introduced by Bank of America in 1958.[8] Credit cards brought great convenience and for their users and a profitable product for the bank. However, they also introduced new problems for the bank and its customers. Credit card fraud became a common term in 1959 when over two million cards were dropped on the state of California with a result of 22% of the accounts to be deemed delinquent. [10] This was an expensive mistake, with an estimate of 10 million dollars. [10] After learning from their mistakes, the Bank of America cleaned up their operations by creating financial controls on their products. This story illustrates the importance of thinking ahead of time to prevent problems like this. A mistake like this would mean the end of any modern-day startup. To learn more about the transformation and evolution of VISA credit cards, click on the image below to see the video.

Professor Douglas Arner classifies the first era as Fintech 1.0.[5] As mentioned earlier, the construction of the first transatlantic cable marked the first time information technology was leveraged to empower financial transactions and helped make financial transactions more efficiently. With this technology available, businessmen in the UK were able to request quotes and pricing in the USA from their offices in London, thus cutting the middlemen, who would benefit solely from the arbitrage commissions.[5] Therefore, businessmen increased their margins, and the barriers to entry to some markets were lowered slightly.[5] It is common to hear advertising materials from new startups talking about getting rid of the middleman to make goods more accessible. While using IT to make their operations more efficient may sound like a new idea, businesses have tried to do it for over a hundred years. Click on the image below to watch a comprehensive video on the first transatlantic cable.

Digitization of Finance

First Automated Teller Machine in the world, developed by Barclays in the United Kingdom [11]

This era began with the introduction of the first Automated Teller Machine by Barclays.[5][11] However, it is important to highlight that the adoption of these technologies is often not swift. The invention of the handheld calculator by Texas Instruments was also an important development in 67.[5] In this era, traditional financial services as we know them were developed. A few highlights of these era are:

Professor Douglas Arner then classifies the Digitization of finance as Fintech 2.0.[5] The technologies developed in this era were not swiftly adopted and took a long time for these technologies to come to Canada and the rest of the developed world. For instance, the first ATM was announced by Barclays in the UK in 1966 but was only brought to Canada by Sherwood Credit Union in 1976, and it was first located in Regina Saskatchewan.[15] Although adoption of technologies was slow in this era, we started to see a faster development of technologies that became the foundation for the exponential growth of Fintech.

The internet, alone, created the groundwork needed for most of the technologies we have today to flourish. For example, the first time online banking was offered to customers, in 1981, neither the technology nor the market were ready for adoption. [16] It was not until 1994 that the Stanford Federal Credit Union offer online banking to all of its customers that the technology was ready to be adopted and most importantly, the market was ready to receive the tech. A year after, most banks in America adopted the technology, which demonstrates that the development of the internet allowed for quick and nimble operations in the financial sector.[16] Even in the 90s when people were more comfortable using the internet, some customers refused to use online banking for reasons such as not feeling safe, and poor user experience. [16]

Another piece of technology that began to develop in Fintech 2.0 is the cell phone. Although the first few models did not have impressive specs by today's standards, the technology did not take long to be used to improve financial services. In 1999, BMO became the first bank in North America to introduce mobile banking via a cell phone. This era culminated with the introduction of the smartphone, particularly the iPhone by Apple.

The Emergence of New Players

iPhone featuring Moka's mobile app[17]

This era is mainly identified by the creation of new startups that successfully leverage the availability of information technologies with the need for reliable financial services. In 2007, the iPhone was announced and shipped across the USA.[18] [17] By 2008, the iPhone was available in all the developed countries in the world. With smartphones came the ability to create apps, which lowered the barriers to entry and the costs related to marketing, operations, and distribution. In doing this, the technology increasingly incentivized innovators to create solutions to their problems, which they later transformed into consumer products, effectively democratizing the financial sector.[19] A few highlights of this era are:

Lastly, Professor Douglass Arner classifies our current era as Fintech 3.0.[1]It is characterized by the introduction of new products and technologies that began to disrupt the market.[1] The reasons that incentivized people to get in on the Fintech game were:

  • The Operational Cost Reduction In this era, the downsizing of teams and the increased use of technology to reduce costs such as straight-through processing were key to the development of Fintech 3.0.[1]
  • The Great Financial Crisis: After the 2008 Great Financial Crisis, the lack of trust of traditional financial institutions allowed new entrants to enter the market.[1] Take Transferwise as an example, it continuously demonstrates how regular financial institutions are taking advantage of their customers and offers a much better service for cheaper.
  • Introduction of Smartphones: Smartphone penetration evolved into a whole new financial ecosystem that allows users to buy almost anything from their mobile device.[1]

Innovation in Traditional Banking

Many traditional banks are aiming at catching up with Fintech 3.0 technology. They are doing so by providing mobile apps and online banking to their customers.[2] In doing so, traditional banks are more accessible to users who need to check their account balance, cash in checks, or make a transaction remotely. In addition, mobile solutions mean that there will be easier access to banking services for marginalized people who do not live near a bank.[2] Ultimately, providing a democratized service that lowers the importance of living close to a bank.[2] Finally, banks have become more personal, allowing customers to alter the type of services based on the individual.[2] Nevertheless, it is important to remember that competition and innovation by new startups are the driving force that keeps pushing banks towards innovation in Fintech.[2]

Fintech Apps

The more people use their mobile phones, the faster the growth in mobile phone applications.[2] Fintech companies understand this basic fact, and they are aggressively entering the industry. Fintech firms need to focus on developing apps with a fast and easy to use user experience.[2] Investments in the industry are also aggressive, in the first two quarters of 2015, $1.1 billion were raised for nearly 60 financings.[2] The competition is strong and the fact that a group of startups, with 10 to 50 employees, have changed the way people bank around the world says it all.[2]

In 2019, it was expected that there were over five billion mobile phone users.[3] Back in 2016, 63% of the people in the world were mobile phone users, and the number is expected to keep growing.[3] Both China and India could be the largest contributors to mobile phone usage as an estimate calculated them to reach 1.5 billion and 1.1 billion users respectively in 2019.[3] As such there is plenty of space for Fintech to grow, and offer services to an increasingly more diverse group of people.

Business Side of Fintech

Fintech 2.0 vs. Fintech 3.0

Fintech 2.0 could be then represented by traditional banks. For example, the most valuable bank in Canada has a market capitalization of 143 billion Canadian dollars. [4] Fidelity National Information Services, Inc., one of the most influential IT firms in the world, with a vast clientele list including many banks across the world has a market cap of 90 billion dollars. [5]

If we look at Fintech 3.0, we can see startups like Shopify, which was founded in 2006 in Canada and is now worth 3 billion dollars more than the biggest bank in Canada. [6] And Moka, another Canadian Startup founded in 2017 to help millennials manage their money, and invest their money in socially responsible companies. [7]

From a marketing point of view, a lot of the time it feels like firms that gained most of their worth in the Fintech 2.0 era stopped focusing on the customer. Whereas, Fintech 3.0 startups are better at creating products inspired by the needs and wants of their customers. One can possibly attribute this mismatch to the fact that these startups are created by people who have those needs themselves. Which leads them to create a solution that they will then share with other people like them. Effectively democratizing financial services.

SWOT Analysis


There are many advantages to coming with Fintech. By creating a simpler streamlined interaction between consumers and financial services, Fintech offers significant potential to: Reduce costs, Revamp financial security infrastructure, Introduce powerful risk management systems, Allow more players to enter the market. [8] Lastly, Fintech has allowed regular people to provide and access several financial services including P2P lending, payments, personal finance, money transfer, and insurance.[8]


The standards for the privacy protection provided by businesses online have been going up.[8] Should privacy not be properly protected in a Fintech startup, it could mean the end of that firm since the risk of fraud or financial risks for consumers would be dire.[8]


With the decrease in the cost of internet services and the ever-growing mobile device adoption worldwide, financial services are more likely to be democratized as more people are solving problems that personally afflict them and offering those solutions to their peers.[8] For instance, Moka was created by millennials for millennials.


As Fintech becomes more popular, cybercriminals become more focused on hacking into them.[8] Cybercrime can cripple firms who are not prepared for an attack, which can undermine the credibility of the entire sector.[8] Security may be one of the main reasons it takes a long time for Fintech startups to gain customers.[8] Lastly, Fintec may be abused by users without proper regulation.[8] Easy access to financial services may incentivize irresponsible behaviors like excessive borrowing and high personal debt accumulation.[8]

Innovation of Fintech


Innovation is constantly growing in the financial industry with the most adoption surrounding Fintech. We see disruption happening in the banking sector as it is the most traditional sector of financial services. The banking section is one of many sectors within the financial industry that will be heavily impacted by data-driven innovation. The highest disruption factor for the banking sector is consumer expectations (71%).[9] The consumer demand is constantly increasing with more technological demands that are hard to keep up with. Other factors are impacting the banking sector like regulatory pressure, the increasing demand for digital channels, and the emergence of new technology. These are some of the issues that the development of fintech will be able to resolve soon.

Factors Disrupting the Banking Sector (2018)[9]

Worldwide Adoption

There is a growing development of financial technologies worldwide. China has the most fintech adoption with the highest adoption rate worldwide of 89%, the United States comes in second with 41%. Countries such as South Africa and the United Kingdom have an adoption rate of 34% for fintech. [10] We also have a growth in the fintech sector with a total amount of USD 135.7 billion invested in fintech in 2019. [11] We also see the emergence of more fintech companies overtime with 8,775 companies within North America in 2020.[12]

  • Fintech Startup by Region (2018-2020)[12]
  • Total Worldwide Investments in Fintech (2010-2019)[13]

Big Data

5V Characteristics

Big data is used to describe the vast amount of data and information that is made available. However, the amount of data is too vast for a person to analyze alone, we need the support of the technology to handle the enormous amount of data. The amount of information worldwide has grown exponentially within the years. In 2020, the volume of data and information worldwide is approximately 59 zettabytes (59 sextillion bytes) and expected to reach 159 zettabytes by 2024.[14] It is estimated that a person produces 1.7 megabytes per second and 2.5 quintillions[15] bytes of data every day. The characterization of big data can be identified through 5Vs.[16]

Volume of Data Worldwide (2010-2024)[17]
  • Volume

The volume for big data can be defined as having vast amounts of data that is impossible for a person to manage alone. It is the foundation for utilizing big data as there will be more potential to analyze information.[16]

  • Velocity

The speed is one important key factor for the management of big data.[16] The value of the data will decline if we cannot receive the necessary data for decision making. The ideal velocity would be to receive data at real-time to use the information as quickly as possible.

  • Variety

There can be many different sources where we can gather data which makes it more diverse.[16] With more sources and social media channels, the variety of big data acquisition has grown significantly as we can gather data from social media platforms and smartphone devices.

  • Veracity

We also need to look at the quality of the data when doing an analysis.[16] The information needs to be clean and accurate to appropriately transform and convert the data.

  • Value

The data needs to have some sort of importance to the business or whoever collects the data.[16] If the data that is collected cannot be utilized, then it would have been all for nothing as there is no valuable insight to be found. The value may be found with thorough analysis, however, there still needs to be a clear goal as to why this data was gathered in the first place.

Business Value

We find that information has become more diverse over time with the increasing demand for alternative information. Alternative data refers to any information outside of traditional methods like financial statements from credit reporting agencies.[18] They may look at bills, app usage, social media activity, and geolocation to better identify consumers. The worldwide spending to buy alternative information in 2016 was $232 million US and it grew to $1.7 billion US for 2020.[19] The use of big data in FinTech can be implemented to a greater level of granularity with customer focus as they play a major role in fintech enhancing customer segmentation, personalized services, and risk management.

Worldwide Purchase of Alternative Data (2016-2020)[19]
  • Customer Segmentation[20]

Big data can be used to accurately identify consumer characteristics with finer granularity. Traditional customer segmentation relies on demographic factors such as age, gender, and geographics. However, with the use of big data, there can more aspects we can capture through different information channels. Fintech organizations can gather information through their multiple channels to analyze and depict trends that appear over time. We may find better customer interaction and consumer demand by looking at online behaviour, app usage, and other interactions with social media.

  • Personalized Services[20]

With the assistance of more accurate customer segmentation, there can be more accurate services that meet the demand of the consumers. Fintech companies are able to be more flexible and personalized in their services and offerings based on consumer preferences. Using spending patterns, fintech organizations can offer consumers financial services better adjusted to their daily life.

  • Risk Management[20]

The use of big data can improve the accuracy when identifying its users. By analyzing information such as habits and consumer behaviour, fintech organizations can use big data to prevent the risk of fraud. Aside from fraud detection, big data can be used by fintech to predict risks from historical data. We can look at potential outcomes and ways to minimize risks before they happen.

Ant Financial

One of the best examples of a fintech business utilizing big data is Ant Financial, now known as Ant Group. Ant Financial is owned by the Alibaba group has the capacity from its many firms to collect and store data to have a large set of available information. Their many affiliations with Alibaba and other subsidiaries allows them to collect more information and create further online infrastructure.[21] With subsidiaries like:[22]

  • Alipay: An online payment platform claimed to be the supercharged version of mobile payment giants such as Venmo and Paypal.[23] However, it is restricted to China as it requires the ownership of a Chinese bank account.
  • Yu’E bao: Spare cash management platform managing the savings of $251 billion as of 2018.[24] They manage cash transfer and withdraws free of charge with prime monetization on interest fees from its borrowers.[25]
  • Huabei: Virtual credit card service, now known as Ant Credit Pay. They offer loans and track financial records as well as flexible payment installments, Ant Instalments. Consumers are given the option to repay their loans in instalments of 3, 6, 9, or 12 months.
  • Jiebei: Consumer loan service, now known as Ant Cash. It is one of the most widely used among customers in China along Huabei.[26]
  • Ant Fortune: A wealth management platform teamed with 80 asset management firms to offer personalized wealth services to potential investors.

They use the network effect to create a bigger customer database and handle more consumer information. As per the network effect, consumers with positive reviews are able to refer their peers to the service which will multiply as more subsidiaries are applied. According to the wall street journal, in 2018, more than a third of China, 588 million users, had accounts with Yu’E bao. With this big data, they are able to predict and adjust their offerings based on the consumer habits explored through different subsidiaries. Ant Financial is expected to continue its growth as they handled $17 trillions worth of transactions in 2019 and expect to grow to $61 trillion by 2025.

Artificial Intelligence

Artificial intelligence is referred to as computing technology that imitates brains activity regarding problem-solving and decision making. The use of big data is rendered useless without the application of artificial intelligence. Using machine learning and artificial intelligence, we have the potential to automate tasks at a higher efficiency, lower costs, and no human error. The financial and insurance industry have the second-best automation potential, after manufacturing, with a rate of 44%.[27] In 2019, banks have been expected to spend $5.6 billion USD on artificial intelligence and machine learning.[28] With the top three strategic priorities for financial services worldwide are to improve the digital customer experience, enhance data analytics capabilities, and reduce operating costs. The application of artificial intelligence will meet the demands and create growth for fintech.

Automation Potential by Industry[27]

The three major areas[27] where automation has the most potential are:

  • Applying Expertise

With artificial intelligence and machine learning, there will be easier transferability of information that originates from big data. We can use artificial intelligence to improve account management with better scalability and less human error. Aside from reducing menial labour, artificial intelligence can analyze more factors that are not available to financial advisors. Artificial intelligence can directly analyze and study consumer behaviour in order to provide better financial services and models to effectively meet goals and provide consumer satisfaction.

  • Consumer Interface

With higher usage of mobile devices and increasing development of technology, there is a growing demand for automation of consumers interact with their financial details. Artificial intelligence makes this possible with the quick summarization of data to be presented through a mobile platform. With the application of chatbots, artificial intelligence can reduce the customer support workforce and handle larger capacities of consumer interactions. Alipay’s own customer smart customer service can handle 2-3 million queries per day.[29]

  • Data Management

As information is too vast for individuals to digest, artificial intelligence becomes valuable for the development of fintech. With the constant growth of data volume worldwide, machine learning will provide a solution to manage data in a fast manner. Using big data to create new values, such as client risk profile, and provide business value with predictive analytics and accurate decision-making.

Applications of Artificial Intelligence

Wealth Management

There are many ways in which wealth management is applicable to machine learning. We can use artificial intelligence to handle vast volumes of data and remove volatility from human-based investing. Using more available measures gathered through machine learning, artificial intelligence can make accurate decisions to make financial investments without applying human emotions and bias.[30]

Goldman Sachs and Kensho have partnered to use artificial learning to run a correlation analysis to predict changes in stock and currency prices. They look at factors such as recent news to observe stock price fluctuation and predict similar outcomes in the foreseeable future.[30] Using a simple model like the google search engine, finance analysts can ask questions and be given results regarding the surrounding topic. Kensho has applied its technology with Goldman Sachs’ asset management division to create a cross-correlation engine able to look at various information such as news and statements to understand the movement of said assets.[31]

Wells Fargo and Sqreem are partnered to use deep learning technology to analyze business operations and overall transaction. By investigating internal affairs, they are able to detect any anomalies caused by unethical and criminally liable actions such as insider trading. Sqreem has been named as one of the top growing companies in the Asia Pacific by Financial times.[32] Sqreem’s A.I. algorithms are capable of being applied to different problems and situations from tracking government activity to media campaign management.

Fraud Detection & Insurance

The vast amount of data also includes consumer information from sources as applications, mobile devices, payments. However, it is also a very personal dataset of information of customers which can be exploited if it was acquired by someone with ill intentions. As stated by Statista, 69% of CEOs from firms that offered financial services were concerned about cyber threats and 61% for other industries.[30] But, with the use of alternative data, we can create more accurate predictions of when there might be an economic crisis by looking at consumer patterns from a macroeconomic perspective. Although we cannot reduce these risks, we are able to identify it and take early action.

Mastercard launched decision intelligence to use information like consumer segments, user data, and purchasing habits to identify whether legitimate purchases were made. They have had a 40% increase in accuracy when detecting frauds.[33] In comparison to their old model, they reduced false positives (legitimate claims identified as fraud) by 50%.[33]

Lloyds Banking Group, from the UK, partnered with Pindrop to create audio profiles and identify 147 features from a human voice to identify fraudulent claims.[30] They use phoneprinting and were the first to do so in the UK, to identify different factors within the call for any suspicious activity.[34] Phoneprinting has been effective against ID spoofing, voice distortions, and social engineering. During the trial of the technology, Lloyds Banking group had an 85% success rate of identifying fraudulent calls and has reached up to 95% after it was entirely implemented.[34]

Banking & Personal Finance

Artificial intelligence can be applied to a consumer’s daily lifestyle.[30] They can use algorithms and machine learning to track spending patterns and apply them to their monthly budget. This is more favourable for millennials as they have lower trust with banks and are more accustomed to mobile-based services.

Simple uses machine learning and behavioural economics to produce a better understanding of consumer spending habits. With the available information, they can provide personal advice to its consumers on their spending habits. They are a branchless bank that offers budgeting tools and smart spending support.

Wallet.AI, similar to simple, uses machine learning to track spending habits for the sake of restraining unnecessary spending and offer better financial management to improve their savings. [35] They are similar to a watchdog as the consumer spends more, artificial intelligence will learn more. It creates an understanding of the user’s environment and offers advice for a small decision regarding daily and casual expenditures.


A Robo-advisor is an account that automated the process of investing to align with the financial goals and portfolio strategy of its user. The use of a Robo-advisors is a front-end process driven by artificial intelligence and big data to increase and improve consumer expectations and interactions. The presence of Robo-advisors creates variety for consumer expectations regarding the investment process. Consumers are able to select from three different choices:

  • Human advisor: The most traditional advising where you have a personal financial advisor to consult and discuss spending and earnings for the next steps to achieve financial goals.[36]
  • Robot advisor: An automated account that makes diversified investments by passively investing in order to match the market rather than beat it.[36]
  • Hybrid model: A combination of a personal financial advisor using a Robo-advisor to provide the best solution towards financial goals and investments.[36]

Robo-advisors are able to handle wealth management at a better degree and flexibility to their investors while making fewer mistakes. They are able to comply with investors and mimic hedge funds to manage portfolios flexibility with factors regarding social responsibilities and tactical strategies in regard to the wishes of the investor. Robo advisors are actually digital platforms to manage assets with data-driven algorithms. The model for investing is a passive investment model meant to imitate the market rather than beat it.

Efficient Management

The traditional target segment for investment firms was rich and wealthy people as human advisors did not take anyone under $100,000 in investable assets.[37] However, with better automation, financial services such as investing have become very accessible for all kinds of people. Robo-advisors are able to handle more accounts with fewer errors regardless of the financial amount. With fewer restrictions on the minimum deposit for investing, more individuals are able to participate and invest in future ventures.

Demand for Digitization

The growing population that is adept to technology continues to grow with millennials and generation z entering the labour force.[38] We see a higher number of mobile devices worldwide as we forecast it to be nearly $15 billion in the year 2021.[39] The increasing population of technology adept consumers creates the need for convenient services available through their mobile devices.

Forecast for Mobile Devices Worldwide(2020-2024)[39]

Flexibility and Low Costs

Robo-advisors provides a level of flexibility that will align with the investor according to their level of risk and return expectations. Investors are asked details regarding their financial situation such as spending and income as well as their financial goals. Aside from risk and returns, users are also able to specify their investment preferences as the can abstain from supporting an organization that includes animal abuse or other unethical actions.

According to wealth simple, the cost for a Robo-advisor is typically 0.5% or less while a human investment is 1% or higher.[36]

Transactional Value

Transactional value refers to the number of assets under management. The transaction value by Robo-advisors is set to increase with a compound annual growth rate of about 24%.[40] The expected transactional value by 2024 is approximately $2.5 trillion based on the higher level of adoption for fintech. Based on the transaction value, we expect to continue to see positive growth for Robo-advisors.

Forecast of Global Transaction Value (2020)[40]

Top Robo-Advisors

The top Robo-advisors all come from the US with Merrill Edge and Vanguard having a combined $340 billion worth of assets under management.[40] Overall, the Robo-advisors within the US manage 73% of all Robo-advisor managed assets. [40]

Top Robo-Advisors (2020)[40]

Crowdfunding and P2P Lending

According to the U.S. Securities and Exchange Commission, “Crowdfunding is an evolving method of raising money via the Internet to fund a variety of projects.” Several definitions slightly differ based on their respective regulatory bodies. The majority of these regulatory bodies do, however, agree on the elements that crowdfunding has [41]. These elements consist of the following:

  • More than one lender - there is generally going to be numerous lenders or funders in the crowdfunding process
  • An online platform - the platform is the key to facilitating the entire process between funders and borrowers
  • An open call to participate - there is always an open call for more lenders to help with financing [41]

Terms such as P2P lending, crowd-investing can be misinterpreted as referring to a completely different model. All these terms actually refer to identical or similar business models. Some of them are simply subcategories of one another and are only slightly different [41].

There are 4 main types of crowdfunding: debt-based crowdfunding, equity-based crowdfunding, reward-based crowdfunding, and donation-based crowdfunding.

Debt-Based Crowdfunding

Investors provide money in exchange for the right to be paid back with principal and interest. Based on data, this crowdfunding model is the most common one by a large margin, accounting for 99.6% of funds raised through crowdfunding as of 2018. P2P (peer-to-peer) lending and P2B (peer-to-business) are both in this category. Other versions of standard P2P lending are in this category and these involve student loans, real estate, and block-chain enhancement [41].

Equity-Based Crowdfunding

This model involves investors providing funds in exchange for private shares in a company or project. The expectation is that investors will profit from this investment once the stock price goes up or they receive future cash flows [41]. This form of crowdfunding opens up new opportunities for investors who aren’t venture capitalists or angel investors. In comparison to traditional forms of equity funding, this model especially benefits from the elements that characterize crowdfunding in general. The main advantages are the large number of funders and the open call to participate through the online platform. Real estate equity crowdfunding and startup equity crowdfunding are the two main types of equity-based models [41].

Real estate equity funding allows landlords or developers to raise funds in a way that takes advantage of having a crowdfunding platform and being able to do marketing online on a larger scale. It also involves obtaining funds from many smaller investors rather than one investor who provides all the funding [41]. This model benefits developers or landlords because it allows them to obtain funds faster, save overall time, and get feedback from the online marketplace. It also benefits investors because some of these potentially favourable real estate investments wouldn’t be possible without a crowdfunding platform. There is also less risk due to the large number of investors as opposed to a traditional model where only one investor contributes a large amount to support the project.

Startup equity funding also allows for individuals to participate in investing that would not have been possible without a crowdfunding platform. It allows a startup company to receive funding from a large pool of small investors in exchange for shares in the business. In this way, the company receives many different investors, most of which do not have large relative ownership. This model increases the number of options for both investors and startup companies [41].

Reward-Based Crowdfunding

Reward-based crowdfunding involves funders supporting a project in exchange for being an early recipient of a product or service that is still in production [41]. This allows businesses to receive customer payments before delivering their initial product or service. To incentivize backers (funders), the product’s or service’s pre-order price is advertised as being discounted from its official release price [41]. This model of crowdfunding is considered an effective way of testing the potential demand for a new company’s product or service. Even though the reward-based model may be a well-known method of crowdfunding, it only represents a marginal portion of the funds raised by overall crowdfunding [41].

Donation-Based Crowdfunding

Donation-based crowdfunding involves donors (funders) donating money for a cause that they support. The cause is usually philanthropic or sponsorship-related in nature [41]. The donation is considered pure if donors do not receive a reward or it is considered a reward donation when donors receive a small reward. The rewards can either be intangible such as tokens of appreciation or tangible items such as pens. Similar to the reward-based crowdfunding model, crowdfunding of this form contributes only a small portion to the global crowdfunding market [41].

Peer-to-Peer Lending

P2P Lending is a form of debt-based crowdfunding where lenders and borrowers connect through an online platform [42]. P2P lending rose in popularity following the economic crisis in 2008. Individuals began trusting banks less and looking at alternative or secondary markets for their financing needs [43]. It involves hosting an online platform that acts as an intermediary for lenders and borrowers [42]. Lenders are typically either private individuals or institutional investors while borrowers are individuals or businesses. In the case of a business seeking a loan, the practice is known as P2B lending. In exchange for the loan, the lenders receive the loan’s principal as well as interest payments that are calculated based on the borrower’s risk of default. Before participating in the P2P system, potential borrowers must pass a credit rating system to determine their legibility and what terms they can borrow on [43]. The platforms that host P2P lending receive revenue based on the business model that is being followed.

For some borrowers, P2P Lending can allow for more flexible payment terms and lower interest rates. However, the size of P2P loans also tends to be capped at a lower amount than traditional loans [43]. Investors can vary in their level of leniency regarding their standard for the borrower and their credit score and source of income [42].

Peer-to-Peer Lending Business Models

Standard P2P Lending

The standard P2P model consists of the borrower, lender, and the platform. The platform’s goal is to attract both lenders and borrowers [42]. The platform receives revenue from the fees that it collects from its users for performing duties such as creating contracts, dealing with payment adherence from the borrowers, and legal issues. If a borrower defaults on a loan, the platform sells the loan to a collection agency.

Lending with Loan Originators

This loan originator is a third party that is not present in the standard P2P model. The loan originator is usually a company and is meant to look for borrowers that are looking for a loan and bring them to the platform [42]. The loan originator mainly performs sales-based duties where the goal is to show the borrower the attractiveness of the loan and its terms. This is why the loan originators are referred to in this manner. Loans originate from the loan originator in this model. In this case, the platform's full attention is on attracting only the lenders to contribute to the platform [42].

Sometimes, investors are wary of this model because it adds more complexity. The loan originator is considered to be fully responsible for the quality of loans and returns that it generates for investors [42]. If the loan originator isn’t performing well in that sense, their replacement is an option. In this model, platforms may sometimes include a buyback guarantee where the loan originator will pay back a loan after several days after the payback period has passed. This is only an option if the loan originator company has sufficient funds to cover the loan amount and pay back the investors [42].

Bank-funded P2P Lending

Bank-funded lending is the P2P lending model that is closer to traditional bank lending [42]. This model adds an extra step involving the bank. The bank originates the loan and sells it to a platform where the borrower signs off on the loan terms that are provided by the bank. The platform buys the loan using the money from its investors. After this stage, the process is the same as the standard one. Borrowers repay investors for the loan that they bought off the bank.

Balance Sheet Lending

Balance sheet lending is also very similar to traditional bank lending in how the platform’s role closely resembles that of the bank in traditional settings [42]. The platform originates the loan and keeps it on its balance sheet as an asset. With this model, the platform assumes credit risk but also earns more revenue because it receives interest payments along with the fee payments it usually receives from the platform’s users. This is the only model in which the loan originates from the platform itself [42].

Global Market for P2P Lending

China is highly dominant in terms of global market share for P2P lending. Overall, because of the number of scandals surrounding P2P companies in China, the amount of P2P lending companies has decreased over the years. In previous years, China was even more dominant in the global landscape. This is evident in the difference of the distribution from 2017 to 2018 [42].

  • P2P Lending in 2017[42]
  • P2P Lending in 2018[42]

Issues in Fintech

Regulatory Issues

Due to a number of legal issues, a large number of P2P companies have been shut down in recent years. This is a large part of why China’s domination of the global crowdfunding market has diminished over time. Many of the companies were shut down because of questionable legal practices and conducting widespread scams [44]. American companies such as Lending Club have been the subject of scrutiny for their own errors. In the UK in 2014, for example, officials began limiting investors to investing only 10 percent of their existing investable assets on crowdfunding platforms [44]. These regulations stem from the risk that governments believe crowdfunding platforms pose to some unprepared investors. Officials were also concerned regarding the marketing of these platforms to certain investors because they were concerned of the higher risks [44].

Low Barrier to Use

The gamified nature of some fintech products and services incentivizes anyone to join. This can be seen as an advantage but also a disadvantage. People that have previously been less involved in investing are now becoming more familiar with it. This includes younger individuals that are sometimes not quite ready for the consequences of losing. For example, there was a tragic incident involving a young man that was using the stock investing app, Robinhood [45]. A glitch on his phone took place where his account mistakenly showed his account as having a $730, 000 negative balance. This caused the young man to panic and take his own life [45]. This problem also relates to rules and regulations surrounding the types of investors that are allowed to use unfamiliar or new fintech technologies. There are many different startups and many have new business models. For most of them, almost anybody can start an account and begin trading stocks or participating in other investments. Unsophisticated investors are also able to invest at a far greater risk than they are recommended to or were able to in the past. Allowing unprepared investors to expose themselves to larger risks can be a problem as they have relatively low amounts of investable assets to begin with. Younger investors tend to be very confident which can allow them to participate in ill-advised investments [45]. Robinhood, for example, is seen as a way to make “impulsive decisions” easier for millenial investors [45].

Data Security and Privacy Issues

Once released, new technologies are riskier to use because they have been tested by fewer users. Some technologies are also more difficult to adequately test. This causes security issues such as the ones that occurred with NFC capabilities and payments on Android phones in 2014. It was discovered that hackers could easily hack into an Android device through its NFC payment system and retrieve user data [46]. Not all fintech mobile applications may have the required security measures to protect user data. This is due to a lack of standards surrounding these mobile apps but also due to a potential lack of resources or expertise that is present at some fintech companies. A lack of security or privacy on fintech applications can create trust issues between banks and third-party applications, which prevents banks from supporting these applications and preventing users from accessing them. Hackers can also use malware to attack users and these types of attacks are successful even against big banks with better security [47].

The use of more fintech applications also creates the need for more user passwords. The more passwords a user enters, the more prone they become to having these passwords retrieved by hackers. Stolen login credentials can also be an identity theft risk [47].

Legacy banking systems can be out of date and lack compatibility with fintech applications. If these systems are not updated, they can be a great source of security risks where users connect their bank account to a third party fintech application [47].

The cloud is widely used to store data from third-party fintech applications. The cloud, whether private or public, can also be unprepared in its defence against hackers. Many fintech companies are still in the startup stage and may not have the resources to invest in a good cloud provider which amplifies this risk further [47].


Fintech has provided new ways for individuals to begin investing throughout its history. The first disruption marked the first time information technology was leveraged to empower financial transactions and helped made financial transactions more efficient. It marked one of the first steps towards globalization. The era of fintech 2.0 revolutionized Fintech through the creation of the ATM and key fintech companies like Visa, Mastercard, and Interact played a major role. The newest innovations paved the way for fintech 3.0 and beyond. New ways of investing through crowdfunding platforms provided investors with new opportunities to diversify their investments and gave newfound access to certain types of investors. Fintech's issues have been present since fintech began and will likely continue as advancement continues.


Maksim Koljancic Miguel Adolfo Reséndiz Jiménez Jose Huaman
Beedie School of Business
Simon Fraser University
Burnaby, BC, Canada
Beedie School of Business
Simon Fraser University
Burnaby, BC, Canada
Beedie School of Business
Simon Fraser University
Burnaby, BC, Canada


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