Earlier this year, news of the launch of the revolutionary Apple Card drove the over-crowded credit card market into a frenzy. The new kind of credit card reimagines the concept of credit cards using the iPhone. This is just one example of how fintech is transforming the world of financial services. Fintech organizations that are able to quickly draw relevant insights and successfully monetize the data will achieve an unbeatable advantage in the marketplace.
Here we explore three use cases on how data science empowers fintech to provide superior solutions.
Traditionally, lending has been a tedious and time-consuming process, as lenders take days and sometimes even weeks to approve loan applications. A majority of the time is spent on verifying applicant information and income statements, bank accounts and credit ratings of the individual. This process is biased against individuals without adequate credit histories such as young adults, self-employed individuals, and the underbanked.
Fintechs are transforming lending by increasingly relying on data science to analyze and classify loan applicants without human intervention while eliminating the biases of traditional models. Take the case of Credolab, a Singapore-based fintech that uses smartphone metadata to generate credit scores for people with thin credit histories. It applies an algorithmic credit scoring engine that emulates the human intelligence of underwriters. As a result, they have seen credit approvals grow without increasing risk exposure.
For financial institutions (FIs), customer acquisition and retention are major challenges. FIs are therefore constantly seeking ways to make their acquisition and retention efforts more efficient and effective. Data science is coming to their rescue through predictive models that help organizations better understand consumer behaviour, drive sales and cultivate loyalty. A good example is Cardlytics, an Atlanta-based fintech, that applies data science to provide FIs with relevant insights to drive client acquisition and retention. The company uses insights from historical purchase data across stores, products, categories and geographies to identify, reach and influence likely buyers and drive loyalty programs.
Offering data science-backed investment management services not only increases user engagement but greatly improves the overall experience of the user with the financial product they are interacting with. Algorithmic trading, for instance, is gaining ground as algorithms can analyze humungous volumes of data to spot patterns that can go undetected by humans, react faster than human traders, and trade automatically based on available insights. All that is needed is enough data to train the model. Sarwa, a Dubai-based fintech combines technology and human advice to make expert investing “available for everyone.” This helps clients: open an account in minutes via facial recognition, track their goals via an intuitive dashboard, and rebalance their portfolios by tracking market movements.
The fintech world is rapidly taking advantage of the power that data science can potentially unleash in combination with Artificial Intelligence, machine learning and predictive analytics. Fintech enterprises are employing armies of data scientists in addition to financial specialists and machine learning experts to develop disruptive financial solutions. Given the data science talent crunch, many forward-looking fintech are providing access to data training, analytical tools and reputed data science courses to create a healthy pipeline of data scientists and ensure sustained competitive advantage.