How predictive analytics is shaping fintech

The banking and fintech sectors have seen major technological innovations over the past few years, with concepts such as big data or blockchain claiming that they can revolutionize the way institutions and consumers handle money. But data is useless without analytics to draw conclusions from it, and the rise of predictive analytics portends to change how fintech does business. Fintech with new predictive analytics technology can give customers an individualized experience which caters to their unique interests and keeps them safe.

Before discussing the benefits of predictive analytics, we should understand how it does things differently. Banks and businesses have always been using data to predict future trends, and so predictive analytics is hardly a completely revolutionary idea.

Predictive analytics are better, not because they are completely new, but because they promise more accurate results. As CIO notes, new technologies such as data mining, big data, and machine learning lets organizations more easily discover and exploit patterns to detect new risks and opportunities. Organizations can create models which rely on data in the present, and extrapolate the data with modeling techniques to have a better idea of what will happen in the future. More data and better analysis leads to better predictions.

There is perhaps no field which stands to benefit more from predictive analytics than fraud prevention. Insurance Journal reports that the White House said last February that “malicious cyber activity cost the US economy between $57bn and $109bn in 2016.” And while fraud can be limited with proper security procedures, fraudsters always seem to be one step ahead. Criminals come up with new means of deceiving institutions and consumers, and fintech is forced to react after the damage is done.

But with predictive analytics, fintech companies can more easily detect fraud before it causes damage. A basic example is how credit card companies will often deny a charge if you attempt to use the card in a different country or state, because they know that you normally make purchases from one state. But over time, fintech institutions can use more data to detect if a transaction does not fit in with a user’s normal purchase history, and then assess whether it is potentially fraudulent.

Furthermore, fighting cyber fraud is an example of how predictive analytics pairs up with machine learning algorithms.