How data science is set to revolutionize the fintech landscape

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The availability of massive data is driving the FinTech industry to harness the power of the hidden gems that only data analytics can deliver.

The FinTech industry has witnessed a massive shift owing to digital transformation. From banks to e-commerce platforms, astronomical amounts of data are being generated in the form of transactional and non-transactional data.

Ruled by the power of algorithms and data science, it is enabling businesses to spot consumer trends, and empowering them to create real-time growth opportunities. In a fiercely competitive environment like the payments industry, data science approaches have already matured.

Despite the industry being highly regulated, businesses can attain an edge over their competition by leveraging powerful insights unearthed through data science. The availability of massive data is driving the FinTech industry to harness the power of the hidden gems that only data analytics can deliver.

Here are the top three ways in which data science is being leveraged by the FinTech industry:

  1. Fraud detection and prevention– The number of frauds, as well as their new mechanisms, make it difficult for traditional rule-based approaches to detect them. A scalable way to keep track of fraud is to use data science. Data science techniques are widely utilized to identify and predict fraudulent financial transactions. Gradient boosting models are a popular choice. If interpretability is an important factor, more simple models like logistic regression could be used, or advanced techniques like Local Interpretable Model-agnostic Explanations (LIME) and Shapley Additive exPlanation (SHAP) could be tapped to explain more complex models. Owing to an exponential rise in the number of daily online transactions, there is a need for FinTech players to place fraud prevention on the top of their agenda. Employing the right mix of predictive analysis, behavioral profiling, and real-time detection, data science can enable financial organizations to keep abreast of new ways of committing fraud with low to no manual intervention in an automated fashion using algorithmic approaches. While fraud detection and prevention are critical aspects that data science can aid, their true potential and capability extend far beyond these functions.
  2. Credit scoring models- Assigning a credit score to people who quantify the likelihood of default is an extremely important part of FinTech companies dealing with providing loans. In some emerging economies, people prefer not to have bank accounts, leading to discrepancies in accounting transactional details holistically. This has posed a significant challenge to the FinTech industry to assign them a credit score. Businesses are harnessing the power of data science techniques like profiling based on psychographic surveys to go beyond the traditional credit scoring methods which require a banking history. From geocoding, analyzing SMS messages to psychographic surveys, these data points could serve as a substitute for traditional banking history and might predict likely defaulters. Technologies like machine learning are playing a key role in providing loans to people who are not yet in the formal banking sector.
  3. Customer lifetime value models- To grow more, businesses need to sell more, which can be achieved by acquiring new customers. A recent Gartner survey revealed that 44% of CMOs expect marketing budgets to decrease because of COVID-19[i]. This will mean an increased focus on ensuring that customer acquisition costs (CAC) are reduced. With the dynamics of business changing rapidly and revolving around its customers, it is very critical to get to know a customer’s lifetime value (CLV). CLV enables businesses to concentrate their efforts on their best clients. Better their understanding of CLV, the better they can employ their strategies to retain their most profitable customers. Another efficient way to apply this would be to use machine learning models to calculate customer lifetime value (CLTV models). The CLTV can ensure that customers identical to existing customers with a higher CAC than their CLTV are not acquired again.

Today consumers have numerous payment methods at their disposal, there is not a single value-based ecosystem that effectively connects cash, digital, and loyalty rewards today. The FinTech Industry is enormous in its own right, and by employing the advanced methods offered by data science it can scale hitherto unknown heights of growth and profit.

Herein lies a crucial opportunity for businesses to drive engagement, higher customer satisfaction, and elevated experiences.

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