Data science is full of discovery, innovation and insights. It is the field of study in which knowledge of mathematics and statistics, programming skills and domain expertise are combined to cull insights from a collection of data. It is propelling change and innovation across many industries. When it works in tandem with fintech, to the mutual advantage of business entities and individuals.
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What Exactly Is Data Science?
As Cane Bay Partners VI, LLLP would advise, data science has proven itself to be so useful to fintech that the two must be intertwined into the future. Data science is effectively the mining of vast quantities of data – which flows via online devices at an exponential rate – so that people can make safer and more informed decisions.
What Is Fintech?
Fintech is the shorthand for financial technology. It refers to any and all technological advancements that have been, are and will be employed in the financial industry. It seeks to improve and automate the delivery and use of financial services. Some examples of fintech include online banking and mobile payments to cryptocurrency and blockchain to digital lending and credit.
The tacit purpose of fintech is to help business entities and people to better manage their financial operations. The fintech market is currently valued at about $113 billion and is projected to reach $332.5 billion by 2028. Fintech is a trend that
How Does Data Science Connect to Fintech?
David Johnson Cane Bay utilizes data science in tandem with fintech to provide consultation services for clients, including risk management, portfolio management, service provider analysis and much more. In fact, data science works with fintech to produce and improve many products.
Data science techniques are used in fraud prevention. Data analytics techniques, informed by artificial intelligence like machine learning, are able to examine massive amounts of fraudulent online transactions and predict fraud in future transactions.
The insurance industry employs data science and fintech to minimize risk and keep their businesses in the black. Data science algorithms are used to separate fraudulent from non-fraudulent transactions. Other ways that the two technologies merge to the benefit of insurance agencies include:
- Marketing
- Customer acquisition
- Customer retention
- Credit scoring
- Designing new insurance products
Banks use data science to generate risk analysis. Logistic regression is one example of the powerful tools employed to evaluate borrower risk. It can predict the risk of borrowers, dividing the good from the bad extremely quickly. This benefits folk who’ve worked hard to maintain decent credit scores because the technology is able to focus its insights into the individual, as opposed to the stereotype.
Ideally, data science can predict where markets are headed. By working with AI in fintech to evaluate data and find trends and risks, data science produces a more informed understanding of investment and purchasing risks earlier.
Data science and fintech go hand in hand. As these two complementary technologies continue to grow on their own and together, it stands to reason that the financial industry and every individual who utilizes them (just about everybody) will benefit.