The financial services sector has been traditionally a data-rich industry. With the enormous amount of data generated by different sources, Fin-Tech Industry is now in need of Big Data technologies more than ever (who isn’t?). Big data has opened a new passage to fin-tech with cutting-edge technologies such as innovative equity platforms based on crowd funding; data visualization tools to follow customers; new payment systems based on mobile and cloud technologies.
This joining of forces has given fin-tech industry the opportunity to improve customer intelligence, reduce risk and predict fraud while delivering better services with lower costs. In this article, we will outline some of the most common use cases of big data in fin-tech industry;
Customer Retention and Acquisition
One very important area where Big Data analytics has been used successfully is for customer retention and acquisition. This involves analyzing data, and targeting promotional spending using machine learning algorithms.
Big data insights and analysis enables fin-tech companies to correlate customer profile data, purchase history, and customer behavior on social media sites to offer related promotions. For example, when a customer makes a number of transactions at Nike and likes the sports related pages on social networks, the company could send a credit card with a special promotion applicable in sports stores.
For years, credit scores were provided based on basic financial transactions (like repayment history). Big data introduced innovative scoring models and new sources to the fin-tech industry like customer behavior. Based on a richer set of data from social networks, it is now possible to achieve a higher acceptance rate, while sustaining lower default rates.
Gaining Wider Public Opinion About the Industry
Public opinions are becoming very popular in every industry. Aggregating and analyzing all this data generated from various and increasing amount of sources become more complex. Therefore fin-tech companies are using Sentiment Analysis to gain public opinions about their companies, industry or the whole economy. In fact, Finextra.com claims that some hedge funds are basing their entire strategies on trading signals generated by Twitter analytics. While this might be an uncommon example, sentiment analysis are often being used for looking for economic indicators, seeing the relations between other participants, specific market indicators, or sentiments concerning a specific company or its stocks.
Undetected fraud costs the credit companies billions of dollars each year (Fraud costs credit card issuers approximately $10 billion per year and is only detected at a 40% rate). As an example, Citi Group is correlating data from multiple, unrelated and various sources which they believe that it has the potential to catch fraudulent activities earlier than current methods. For instance they are correlating Point of Sale data with web behavior analysis (internally or externally), and cross-examine it with other financial institutions or service providers such as First Data or SWIFT. They claim that this doesn’t only improve fraud detection, but also decreases the number of false positives.
EDW (Enterprise Data Warehouse) Optimization
Traditional EDWs were not cost-effective and were time consuming regarding to data preparation, analysis and cleansing. Big data analytics solutions allow you to scale, integrate, analyze any volume of data and store all data types together cost effectively. In addition, you can clean, match, profile, enrich and aggregate huge amounts of data.
Citi Group also confirms, their Platforming Costs have been driven down due to the move towards Big Data horizontal architecture. “Big Data also offers a price point where we can store as well as analyze the data. We are a global company with an incredible amount of assets that are valuable to our business. And we can now store them at an expense point that makes them analytically beneficial to us at their most granular level” (Michael Simone, the Managing Director of Data Platform Engineering at Citigroup)
Risk Management and Strategy Development
Another common use case is using predictive analytics for risk management and strategy development. A real use case example for predictive analytics in risk management is: A financial services company wanted to comprehend the client behaviors that have the possibility to move/withdraw their funds. They also want to identify specific behaviors of the customers who might consider moving their assets. Using predictive analytics, the company first integrated data of such customer activities such as; a change in address, or power of attorney; or the client had recently been browsing on the company site for forms. They pulled multiple data sources together to build out activity paths for each client. For instance, they tracked clients’ specific activities and identified whether these activities led to a transfer or withdrawal. By correlating this data, they were able to determine the statistical relevance of each activity, or combination of activities that predicted customer churn.
We can see data science and complex machine-learning algorithms leverage big data analytics in fin-tech industry. Detecting and preventing fraud, determining customer behavior, gaining more insights about the industry and reducing costs could lead fin-tech companies to develop innovative data-driven-solutions and services to have a competitive edge.