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.
Defining predictive analytics
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.
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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. The algorithms can go over each and every transaction, and instantly stop transactions without the user having to take the time to file a report after the damage has been done. While cyber fraud will never be entirely eliminated, predictive analytics combined with other technology will keep fintech institutions on pace with scammers.
Predictive analytics and HR
A major challenge with the rise of predictive technology is that fintech will need to recruit qualified data scientists and engineers to manage and improve algorithms. When human resources profile resumes and decides who to invite to interviews, there are a lot of judgment calls. These judgment calls can be filled with human errors. HR workers may be biased consciously or subconsciously against certain groups. They can get tired and miss details.
For those reasons, companies often already use automated software to screen out resumes, but much of this software consists of looking for certain keywords rather than truly analyzing the resume. Predictive analytics can take this next step. By looking at past successful resumes and applying machine learning, algorithms can determine what kind of candidate will be a solid cultural fit. The end result will be a better hiring process.
Predictive analytics and customer retention
Fintech companies and banks face greater competition as they chase the same customers, which means that customer retention and acquisition are more important than ever. Fortunately, predictive analytics and machine learning can improve the customer experience in countless ways.
Chatbots are one example. As Techburst reports, US Bank is working on developing conversational interfaces and chatbots to augment customer service. A machine learning algorithm is used to help the bank’s associates answer rarely asked questions more quickly.
But the true key is that these chatbots can create unique data in the form of customer conversations which can reveal why they like or dislike a service. Unique data leads to additional learning, which leads to better analytics to create superior chatbots, which collects better data. The end result is a continuous loop of improving technology that can better help customers.
The above example should make clear that predictive analytics is not a solution in and of themselves. They are part of a larger technological revolution which promises to help fintech draw stronger conclusions from data, predict what trends will continue, and reach better decisions. Creative fintech institutions can find additional ways to use analytics, which will help them gain a step over their competitors.