The data science field is fast-paced, constantly evolving, and above all, extremely lucrative. Companies of all sizes are investing more resources into hiring data scientists and developing in-house data analytics programs. So what does this mean for the financial sector? Do non-traditional industries like fintech also need data scientists to succeed? This post will discuss why data science is becoming a stronger force in the finance industry.
What is Data Science?
Data science is the process of acquiring, cleaning, transforming, and analyzing datasets to uncover hidden insights. A data scientist examines information from different angles, identifies trends, and recommends new strategies based on the findings. Data science is expanding rapidly as more and more industries embrace data-driven decision-making. Successful consulting firms like Cane Bay Partners prioritize data science as a key part of their services.
Data Science in Finance
Alongside cybersecurity and IT, data science is one of the biggest pillars within the financial sector. For one, financial institutions are collecting and analyzing more data than ever before. Trends like fintech are leading to an increased reliance on data and this will likely continue to grow. Also, the financial sector is adopting data visualization tools to improve communication and collaboration. Lastly, AI technologies (like machine learning) are becoming more and more prevalent in business software.
Lending
Lending has been part of the financial sector for centuries, but in recent years, it has undergone significant changes. One of the biggest developments in lending is the growth of peer-to-peer (P2P) lending. A P2P lending platform allows individuals to make small loans to other individuals with low credit scores. These platforms charge higher interest rates in exchange for the higher risk of default. With the help of data science, P2P lending platforms are able to better evaluate the risk that borrowers pose to lenders. A data science consultant can help a company collect and analyze information on borrowers, including their payment history and employment status, to figure out how likely they are to default on their loans.
Robo-Advising
Robo-advising is automated wealth management that is mainly conducted online. This type of financial management is based on algorithms and is primarily used by younger investors who prefer to manage their money digitally. Robo-advisors use algorithms to create customized investment plans for clients based on their risk tolerance, current financial situation, and long-term goals. However, financial algorithms are based on the assumption that the data collected is accurate and complete. This is where data scientists are needed to verify the information.
The future of data science is bright and new job opportunities will continue to rise as companies expand their data analytics programs. But as more and more college graduates enter this sector, competition will continue to go up. This is why people who are interested in data science should start developing their skills as early as possible. Also, it’s essential that workers in the field keep current on the latest industry trends. Data science is a highly specialized skill set that requires constant training and upgrading to remain relevant.
