The finance industry generates huge amounts of data– more than most segments of a typical economy. From personal bio-data to financial transactions, accounts and savings habits, these institutions yield information on a mind-bogglingly large scale.
This set of data is a treasure trove. FinTechs, banks, and other players in finance could leverage it to utterly transform the way they do business. There’s a great deal of potential with these data that are waiting to be unlocked.
Here, we’ll talk about five ways that Big Data can significantly improve FinTech’s operations.
When FinTechs have more meaningful data concerning their customers, they will be better able to fashion products and services that meet those customers’ needs. They can decipher patterns suggested by the data in their possession, learn about customer behaviour and preferences, and use this information to build products that are better at solving customers’ specific problems.
Big Data can also be analyzed for the purpose of gleaning insights that’ll help FinTechs to improve customer service delivery.
Robotic Process Automation
Robotic Process Automation (RPA) involves letting bots take on tedious and monotonous processes that have previously been managed by humans. Doing this frees up more time and resources for human customer service agents to attend to more unique and personal needs of customers. Ultimately, deploying bots to various FinTech-customer touchpoints raises the quality of customer service that’s available from FinTechs.
RPAs and other bot-driven operations are possible because the technology that undergirds them ‘learns’ patterns of human behaviour and platform operations from the massive amounts of data that it deals with.
Just as is the case with other kinds of businesses, FinTechs want to know what risks they’re faced with, and how they could possibly mitigate those risks. Big data can help them in this respect.
For example, digital lending companies can examine data about their customers to determine what factors indicate creditworthiness or suggest a potential of defaulting on loan repayments. They can leverage this information when evaluating loan requests, and provide loans only to people who are creditworthy. This minimizes the risk associated with lending to potential defaulters.
With more customer data at their disposal, FinTechs will be better able to define and categorize their customers into relevant demographics and target their marketing and advertising accordingly. They can also place customers in segments according to the device with which they access FinTech platforms and the behaviour they exhibit in their interaction with those platforms.
The detailed market segmentation enabled by big data can help FinTechs to develop better products and service delivery channels as well.
FinTechs can use big Data along with Artificial Intelligence (AI) and Machine Learning (ML) to establish what normal transaction and platform access trends look like. This helps FinTechs to discover anomalies that may indicate potential or ongoing fraudulent activity, and tackle such activity before it causes significant losses to customers.
Until fairly recently, players in the finance industry have only been able to make limited use of the large amounts of data that they generate. This is starting to change, thanks to technologies that can extract important insights from big data. We can expect big data to drive rapid, transformative evolution in FinTech in the coming years and decades.Featured Image Source: Workana
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