5 Ways Big Data Is Transforming Finance

August 1, 2017
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A lot of financial companies are sick about hearing of Big Data, and I frankly do not blame them. To many businesses, Big Data means hoarding reams of useless facts which are collected just because, and are just stored on some cloud server and then forgotten.

But while Big Data can be annoying to deal with, financial companies cannot ignore its transformative effects. Big Data is not just collecting data for data’s sake. Big Data is fundamentally about the analytics which come with the data as well as what banks and financial service companies are doing with them.

As more companies embrace a Big Data approach, new trends and changes are in motion at this very moment and transforming finance. Here are some of the most important trends, and what companies have to consider to get the most out of Big Data.

  1. Fraud Protection

Many companies shy away from Big Data because they are concerned about protecting said data and the harm that can be caused by a data breach. But while it is true that companies which embrace Big Data must implement policies which protect customers’ integrity and privacy, Big Data can actually keep customers safer with improved fraud detection services which can more quickly detect malicious transactions.

With big data and predictive analytics, a bank can notice small deviations in a customer’s financial habits which could indicate credit card fraud as Dell points out or that a transaction is happening in a strange geographical location. Most importantly, Big Data can detect such deviations faster, preventing credit card fraud from becoming truly damaging.

Big Data carries unique security risks, and companies should understand how to protect said data from a breach. But it can be a security protector as well on top of its other benefits.

  1. The Rise of the Public Cloud

Big Data and cloud technology are inexorably linked, as it is the only realistic way for banks or other companies to store all of that data. But up until 2016, most banks preferred to rely on private clouds where they could keep credit scores and other data to themselves, largely due to security concerns.

That trend has begun changing, as a June 2016 report from Deutsche Bank reported that they expect 30 percent of banks to adopt a public cloud like Amazon Web Services by 2019. Private clouds are not that much safer, and they lack the flexibility needed to handle ever increasing amounts of data as well as sudden shifts in workload. Banks are not just moving to the cloud, but the public cloud where data is shared.

  1. Algorithmic Analysis

Every financial analyst knows that the traditional stock broker has been going the way of the dodo for years. There are plenty of reasons for this change, but perhaps the biggest one is simply because that broker can no longer digest and analyze big data better than predicative analysis software bolstered by Big Data.

But it is not enough for trading algorithms to analyze a stock’s performance over the last day or five years. Algorithms must be able to see holistically, taking account factors such as other customers’ behavior and trading patterns. All of this requires data stored on a public cloud, but constant improvement of algorithms will mean that those who understand how to analyze data and algorithms can gain a decisive, if temporary advantage.

  1. Out of Work?

While algorithms may help investors and business leaders, the aforementioned decline of the ordinary stock broker may cause people to worry about the age-old fear that big data and technology will drive humans out of work. And it is true that algorithms and artificial intelligence will continue to improve and make certain professions obsolete.

But those people who panic about how artificial intelligence will put humans out of work in favor of robots are well, panicking. AIs can do incredible things, but AI researcher Oren Etzinoi observes that his 6-year old son has more autonomy than any AI. There will always be new fields of activity for the innovative financial entrepreneur to explore even as algorithms and big data may drive certain individuals out of a job.

  1. A True Customer Experience

Customer service is the field around which Big Data revolves around, as the data can be used to find new ways to help customers. Big data can both group customers into segments to uncover important trends and look into individual data to personalize their services for each customer.

This does assume that any financial institution successfully merges its datasets together. Unfortunately, what often happens is that separate divisions within the firm keep separate datasets. This prevent insights from being discovered compared to if there was an algorithm or department which had access to the entire data. Problems like these are an example of how companies must readjust their mentality in the face of Big Data. It requires a commitment not just towards having the right technology, but towards unity within the company, and a strong commitment towards customer service and safety.