The Advancement of Fintech

The Advancemenent of Fintech

Many people are losing their faith in the existing financial services, the assumption that traditional institutions such as banks and governments are the root cause of many financial and economic crises has increasingly become more salient. Critical awareness about the long-term failures of the banks in providing a trustful financial transactions and remittances greatly increased after the global economic crisis of 2008. People who lost their trust in the banks and governments during the 2008 financial crisis started to look for alternatives for an answer to their trust crisis. For a long time, the question in the minds of many people has been that: If the banks themselves are incapable of managing the risks they take, how should anyone make their savings and investments to avoid the damage caused by such institutions?

One powerful way to meet these challenges resulting from the regulations of the traditional financial institutions was boosting digital transformation. Recent developments in the information and communication technology (ICT) sector have enabled robust solutions for the management of databases and provided new opportunities for a better customer experience. Therefore, the marriage of financial services and advanced technology sector under the name of FinTech started to be considered in the context of the long run evolution of finance industry and its regulations supported by the technological advancements.

The term fintech refers to the financial services reinforced by computer programs and other technologies in the line of innovation and technology-enabled business model, which leverage ICTs in the area of financial services. In today’s world, many fintech companies have been replacing their incumbent financial systems and corporations that rely less on software with more advanced and automated methods, while revolutionizing how existing firms create and deliver products and services, providing new gateways for entrepreneurship and more importantly, democratizing access to financial services.

Generally speaking, the tools of establishing more decentralized and thus, democratic financial systems would be mobile payment systems, peer-to-peer lending, utilization of alternative data sources through the artificial intelligence algorithms and machine learning technology and lastly, cryptocurrencies and blockchain technology.

Miracle formula of fintech: Machine learning

More and more democratic financial systems refer to the dependence of financially enabled people in the economic systems. However, today, upon considering approximately 1.7 billion unbanked people all around the world, it can be noted that providing financial inclusion for the people in a global scale has become a highly challenging task. An innovative solution replacing the good old credit scoring mechanisms of traditional financial institutions is imminent. Banks only are capable of building their financial scores based on the track records of their customers, which hinders the process of discovering the real characteristics of potential borrowers. Since they cannot freely access the necessary private data of these potential customers, their degree of freedom is limited to their databases.

Therefore, recently, some fintech companies started utilizing alternative data sources through the proprietary complex machine learning algorithms to evaluate the borrowers’ credit risk more “fairly”. The concept of “alternative data” we mentioned mainly refers to the comparably new sources of information such as consumers’ payment history, cash-flow data from their bank accounts, credit card transactions, shopping habits, and other personal information obtained from people’s smartphones, social media accounts e.g. With the help of this new approach in credit risk evaluation, most of the un/underbanked people who previously could not satisfy a bank’s traditional lending requirements due to their insufficient credit scores can begin to apply for a loan and participate in microcredit cycles. Therefore, the utilization of alternative data by fintech companies can improve consumers’ ability to access credits with the help of lenders who can better assess the creditworthiness of their potential borrowers.

Therefore, it can be concluded that machine learning technology is becoming one of the pillars of the fintech industry thanks to the new businesses and organizations that aim to create more decentralized, and thus democratic alternative financial system. Besides providing a new path to access the alternative data sources to develop a more trustful user financial score, machine learning algorithms contribute to the development of the fintech sector in other aspects. For example, ML technologies can help identify and prevent fraudulent financial transaction activities by analyzing high volume data, and contribute to the risk management of businesses by enabling the identification of market changes earlier than the methods of traditional investment models.

Colendi

As a credit scoring and micro-credit platform, Colendi also utilize alternative data sources in its indigenous machine learning algorithms based on the disruptive attribute of blockchain technology. In order to provide sources to be used as alternative data, it is necessary for users to create their private Colendi ID by providing identity parameters and complementary information related to themselves. User-owned data in smartphones and social media accounts, and tertiary data sourced from the data partners are positioned anonymously on decentralized data storage systems, which means that the hashed signature of the data is kept intact on Ethereum blockchain. Private data based on end-to-end encrypted data storage systems are scored by Colendi’s proprietary algorithms. The weight of each data segment change dynamically depending on the way it affects the credit score of individuals, as Colendi consolidates more credit history data. Transaction logs, smartphone and social media data, user inputs and Colendi credit history are evaluated to update the credit score of each user. Gathering credit history data includes all details related to their transaction activities and repayment performances. Therefore, it can be said that Colendi’s algorithms is the “linchpin” of measuring the creditworthiness of each of its users in a more fair and transparent way.

Sources:

https://www.philadelphiafed.org/-/media/research-and-data/publications/working-papers/2018/wp18-15r.pdf

https://igniteoutsourcing.com/fintech/machine-learning-in-finance/

https://globalfindex.worldbank.org/sites/globalfindex/files/2018-04/2017%20Findex%20full%20report0.pdf