How Data Science Connects To Fintect

Big data, data analytics, and machine learning are leading the way in many different industries around the world. The way big data is used in the modern era has completely changed how the world engages with information, especially in large-scale industries and companies that prioritize data. The tech industry has been seen consistently quoting the sentiment that data is now more valuable than gold, and nowhere is that more obvious than fintech. Fintech, or financial technology, is the industry made up of technical programmers, software engineers, data analysts, and coders defining the way company accesses and uses big data for use in their financial sector. Here are a few of the main elements of fintech and how companies are using it to give themselves an edge in the industry.

Data Science 

Data science, the backbone of the infrastructure that supports big data is the main element of any fintech industry. It collects, stores, codifies and processes large volumes of information through analytical tools. Companies like Cane Bay Partners use data science to construct meaningful changes within a company by using consultants who analyze and illuminate issues about the financial sector of many different companies. This analysis can drive a competitive edge as it constructs meaningful projections that can drive results through the analysis of financial consulting professionals.

Big Data & The Three V’s

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Big data has exploded within the last ten years and with it are many different evaluations of the structures that lead to analytics changing the way companies are interacting with information. The three V’s define big data and are Volume, Velocity, and Variety. Volume is the expansive amount of data that is collected. Some studies have projected that the amount of data collected will explode within the next five years, doubling what is currently collected. Velocity is the speed at which that data is collected, transferred, and analyzed. Traditionally, data has always been a time-consuming effort. But with the advent of machine learning, large-scale analytics, and teams dedicated to just analyzing large-scale datasets, the velocity at which data is transferred, collected, and analyzed is much faster than ever before. The last V, variety, stands for the many different variables of a specific dataset that can be defined and consumed. The different data points on just one piece of data that can be analyzed are much more expansive, with data points for just one piece of the collection having the potential to be analyzed in many different ways.

Uses Of Data Science in Fintech

Fintech, or financial technology, can be defined as the industry that has been created in recent years that help companies codify, analyze, and project outcomes for data that is collected and used for a business’s financial uses. This can be used to protect returns on investments, create more targeted outcomes, prevent fraud, risk analysis, and analyze the relationships amongst customers. This is generally done through building large-scale dataset machine learning programs. Not only can this information improve the customer’s experience, but it can help a company understand the nuances of their company and make changes to protect their company from failure.

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