How data science will revolutionize the fintech landscape

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The FinTech industry has undergone massive change due to digital transformation. From banks to e-commerce platforms, astronomical amounts of data are generated in the form of transactional and non-transactional data.

Driven by the power of algorithms and data science, it empowers businesses to spot consumer trends and empowers them to create real-time growth opportunities. In an extremely competitive environment like that of the payments industry, data science approaches have already matured.

Although the industry is highly regulated, companies can gain an edge over their competition by leveraging powerful insights updated through data science. The availability of big data is pushing the FinTech industry to harness the power of hidden gems that only data analytics can provide.

Here are the top three ways that data science is exploited by the FinTech industry:

  1. Fraud detection and prevention– The number of frauds, as well as their new mechanisms, make it difficult to detect traditional rule-based approaches. One scalable way to track fraud is to use data science. Data science techniques are widely used to identify and predict fraudulent financial transactions. Gradient boost models are a popular choice. If interpretability is an important factor, simpler models like logistic regression could be used, or advanced techniques like model interpretable local explanations (LIME) and Shapley Additive explanation (SHAP) could be used to explain models. more complex. With the exponential increase in the number of daily transactions online, FinTech players must place fraud prevention at the top of their priorities. Using the right mix of predictive analytics, behavioral profiling, and real-time detection, data science can enable financial organizations to stay on top of new ways to commit fraud with little or no manual intervention from automated way using algorithmic approaches. While fraud detection and prevention are critical aspects that data science can help, their true potential and capabilities extend far beyond these functions.
  1. Credit scoring models Assigning a credit score to people who quantify the likelihood of default is an extremely important part of FinTech businesses dealing with lending. In some emerging economies, people prefer not to have bank accounts, resulting in discrepancies in the details of accounting transactions in a holistic way. This posed a significant challenge for the FinTech industry to assign them a credit rating. Businesses are harnessing the power of data science techniques like psychographic survey-based profiling to move beyond traditional credit scoring methods that require bank history. From geocoding, SMS analysis to psychographic surveys, these data points could serve as a substitute for traditional banking history and could predict likely defaults. Technologies like machine learning play a key role in providing loans to people who are not yet part of the formal banking sector.
  1. Customer Lifetime Value Models To grow more, businesses must sell more, which can be achieved by acquiring new customers. Recent Gartner survey found 44% of marketing managers expect marketing budgets to shrink due to COVID-19[i]. This will mean increased attention to reducing customer acquisition costs (CAC). With business dynamics changing rapidly and revolving around its customers, it is very important to know a customer’s Lifetime Value (CLV). CLV allows businesses to focus their efforts on their best customers. The better they understand CLV, the better they can use their strategies to retain their most profitable customers. Another effective way to apply this would be to use machine learning models to calculate customer lifetime value (CLTV models). CLTV can guarantee that customers identical to existing customers with a CAC higher than their CLTV are not re-acquired.

Consumers today have many payment methods, there isn’t a single value-based ecosystem that effectively connects cash, digital and loyalty rewards today. The FinTech industry is huge on its own, and by using the advanced methods offered by data science, it can achieve hitherto unknown levels of growth and profit.

This is a critical opportunity for businesses to drive engagement, greater customer satisfaction, and improved experiences.

The author Varun Vembar is Data Scientist at Blackhawk Network India.

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