The financial industry is experiencing a technological transformation like it has not seen since the emergence of the Internet. Mobile banking and payments, instant peer-to-peer money transfers via SMS, online peer-to-peer lending, robo-advice, AI-driven chatbots, and blockchain-based interbank payments – to name a few – are all financial innovations that have emerged in the last decade.
One of the key elements that has been enabling this technological disruption is the incorporation of big data analytics by financial services companies.
What is Big Data?
Big data refers to sets of structured and unstructured data that are too large and complex to be processed using traditional database management applications. However, more recently the meaning of the term has changed to also incorporate new predictive data analytics tools that are able to make use of these large data sets to produce valuable insight into user behavior.
In other words, big data allows companies to get a better understanding of their customers’ behavior but also predict their future behavior with more accuracy. Big data, therefore, can be used for a range of applications in the financial industry given how data-intensive the financial sector is.
In this article, you will discover three areas where big data is being used in the financial industry; namely sentiment analysis, fraud prevention, and to develop in-depth customer knowledge that can be used to tailor products and services directly to each customer.
Individuals are expressing their views and opinions on multiple social media networks and platforms online. Whether they are complaining about the food at a restaurant on TripAdvisor, voicing their political views on Twitter or sharing their thoughts on a new movie in forum, they are providing sentiment data.
These sentiments can be collated and used to improve services and product offerings for a bank’s customers by deploying big data analytics programs such as Apache Hadoop, for example. Hadoop uses natural language processing and machine learning to extract key data points out of large sentiment-based data sets that can be useful to businesses.
Using Hadoop, a bank can discover how many of its customers are unhappy with the bank’s customer service and are voicing those opinions online. For example, the analysis might discover that three out of ten customers are not satisfied with the service they are receiving but only one in ten customers of the bank’s leading competitors’ customers are unsatisfied with their bank’s services. This would provide a bank will important insight that it could then act on to improve its services.
Sentiment analysis is also being used by hedge funds to predict stock price movements. For example, big data analytics can be used to gauge sentiment on a stock by the amount of positive or negative tweets about the company.
By analyzing thousands of tweets about a particular stock, analytics tools can show whether the sentiment for stock is very positive or very negative in real-time. This information can then be used by high-frequency trading algorithms to make short-term trading decisions. A lot of research has gone into this area by leading institutions such as Stanford University, and The University of Texas, among many others.
Fraud Detection and Prevention
Fraud costs the financial industry billions every year. Moreover, in today’s world of full disclosures of security breaches, severe cases of fraud can also hurt a financial institution’s brand and reputation. For those reasons, fraud detection and prevention are high on the agenda of global financial institutions.
In the battle against fraud, big data is extremely useful to have in one’s arsenal. Through the combination of big data with artificial intelligence and machine learning technology, fraud patterns can be recognized and prevented much easier than ever before.
By analyzing and tracking the behavior of a new customer from the initial client onboarding process all the way to transaction monitoring and day-to-day customer engagement, financial services can use big data analytics to detect unusual customer activity.
Through a combination of predictive analysis, behavioral profiling, and real-time detection, big data analytics can enable financial institutions to conduct fraud detection at a rate that was not possible before the big data revolution.
Furthermore, as machine learning is applied to customer behavioral data, the predictive ability needed for future fraud prevention receives a substantial boost thanks to the incorporation of big data in the fraud detection and prevention process.
In-Depth Customer Knowledge
Providing a customized user experience has become more important than ever in an industry that is becoming increasingly more competitive through the influx of new startups looking to take market share away from established institutions.
Through the use of big data, financial services companies can provide customized financial services to its customers. By incorporating a wide range of data points ranging from a customer’s emails, complaints, customer support enquiries, statements on social media, etc., financial services companies are able to construct a 360 degree view of their customer, which can then be used to tailor services and cross-sell products that suit the customer’s needs.
Furthermore, customized financial advice can be delivered to customers by combining big data with machine learning. Machine learning software can be applied to learn a customer’s behaviors and needs by analyzing the customer’s big data. These findings can then be used to deliver financial advice tailored specifically to that customer including services and products that would suit them. This new ability to gain in-depth customer knowledge could turn into a very lucrative business for financial institutions thanks to big data.
Big data is disrupting the financial industry in ways that are beneficial to both consumers and financial institutions. Consumers can benefit from much more personalized interactions with financial institutions while financial services companies will be able to increase revenue and reduce loss from fraud and other malicious activity.