Broadly speaking, the notion of data refers to the collection of information in the form of numbers, words, measurements, observations, e.g., that are processed by human beings and computers. In simple terms, data is the information of the facts. However, considering the scope of big data, one of the buzzwords we have faced in recent times, it can be said that it refers to the machine-readable information rather than the human-readable one. Nevertheless, whichever we are engaged in the form of data, or whatever our interests are, we have mostly heard a story talking about the capability of data to change the face of our world we live in.
More precisely, while human-readable data means to the information in which only human brains can receive and guess what, such as an image or the meaning of the texts, the data that can be worked and processed by the computer programs is mostly called as machine-readable, also known as structured data.
Data is everywhere!
With the growing network of people and businesses all around the world through information, communication, and transformation technologies, people have a tendency to generate a lot of data each day. At the same time, the data is found in each drawer of various businesses and mostly used by businesses with the aims of improving marketing strategies, customer experiences, and understanding business trends.
Until today, successful business leaders have made their decisions through manual data collection methods such as talking with customers face-to-face or taking surveys via phone, mail, or in-person. Via such conventional methods, many businesses in a wide range of sectors have manually collected the data to meet the needs and expectations of their customers and market better. However, due to some limitations such as financial cost, time, difficulty of execution, many companies had to operate with limited data.
However, today, with the rise of innovative technologies, it is both easier and exciting for businesses to make an analysis of the increasing amount of data.
As previously mentioned, big data can be used through certain algorithms and analysis methods to manage valuable information about the user. One of the popular sectors the big data is mostly harnessed by the companies and businesses can be regarded as FinTech in today’s world. The main reason why big data takes an important place in this sector is that with the help of Big Data, it is easier and less costly to anticipate customer behavior and to emerge protective strategies and policies for alternative banks and financial institutions all around the world. So, the question is as follows; how can big data be used in the banking and Fintech industry?
Big Data in Fintech and Banking Sector
Financial technology, better known as FinTech, rests on the developments in innovative technologies. Therefore, it is one of the favorite sectors for venture capitalists. As mentioned in our previous articles, the Fintech sector is revolutionized by the game-changing technologies focusing on new tools of delivering banking and financial services to the consumers. That is to say, the ways in which banks and fintech organizations operate today have been highly transformed by technological advancements.
Considering the interplay of technology and banking industries, the first issues showing the collaboration between these two sectors are online transactions and mobile banking applications. However, when we look at the evolution of the FinTech from a broader perspective, the transformative impact of technology on the banking sector goes beyond these innovations, since there are evolution and disruption internally started in almost all aspects of financial institution and services, or even banks, through third-party services in a global scale. These aspects can be summarized as payments, investments, consumer finance, insurance, securities settlement e.g.
In the age of digitalization, Fintech companies heavily rest on innovative technologies, including machine learning, artificial intelligence, predictive analytics, and data science. Let’s look at the ways in which Big Data is harnessed in the FinTech sector.
First, through the Robo Advisors that can be regarded as digital platforms, investors can get access to algorithm-driven, automated financial planning, and investment services in the Fintech sector. And then, there are investment decisions that are shaped by the technology-driven processes based on the working of algorithms without human intervention in the entire process. First, an online survey is conducted to collect information about the clients’ profiles, including their financial status, risk capacity, e.g., and then, the user data is used to make investors’ decision making processes more clear and less costly by providing the financial advice.
Secondly, big data has started to be used by credit rating agencies and credit scoring companies like FICO through innovative technologies such as artificial intelligence and machine learning to provide instant data on borrowers. For example, they use logistic regression to predict the risk of customers and separate good borrowers from bad ones based on a wide range of data sources such as social media, the reports of credit bureaus showing the financial history of the customers e.g.
Another advantage of harnessing Big Data in the finance sector is the new opportunities it provides to detect the fraud. In other words, with the help of data science techniques, the threat of fraud in financial transactions can be more easily detected. Traditionally, identification of fraud has been ruling based, and the rules for flagging a transaction had to be set manually. However, today, with the rise of online banking and internet transactions, finance businesses, and their customers are more susceptible to fall victim to fraud. However, through data science and machine learning techniques such as Deep Neural Networks (DNNs), the unusual activity can be identified by the business, and then the holder of the account can be easily contacted and ask or informed about a transaction that seems suspicious.