Big Data challenges and trends for the financial services industry

Big Data is one of the biggest trends happening right now in the financial services industry, with nearly every player understanding the need to leverage analytics garnered from an ever-increasing number of data sources to survive. That being said, let’s look at 10 of the biggest Big Data challenges and trends in the financial services industry:

  • Quality of data – in today’s world, data comes for a myriad of sources, which can make it difficult to determine which data is valuable and which is not. At the same time, it is important as real-time analytics tools help companies extract real-time quality insights.
  • Regulatory requirements – many businesses today face strict regulatory requirements. Some Big Data technology allows financial institutions to scale up risk management cost-effectively, while improved metrics and reporting help transform data into insights required for risk management.
  • Data silos – financial data comes from a variety of sources. The combination and reconciliation of Big Data requires data integration tools that simplify the storage and access processes.
  • Cybersecurity – data is an increasingly-sensitive subject and that must be gathered, handled and stored responsibly and with respect to the subjects of that data.
  • Robo-advisors – robots aren’t just replacing truck drivers; they’re making their way into the financial services sector. Robo-advisors are being deployed to offer low-cost, real-time personalized financial portfolio advice to customers. And Big Data analytics is now being used to manage portfolios without human intervention.
  • Social credit scoring – an online presence is now being used to determine creditworthiness, with a growing number of credit businesses using personal data mined from social networks like Facebook, Twitter, and LinkedIn to analyze consumers’ credit risk.
  • Mortgage lending – mortgage applications are going to go beyond traditional data analysis methods and will begin incorporating social media data as well. Big Data will also be utilized in the application process to mine important inputs from public databases, bank records and other websites to gather as much information on applicants as possible.
  • Optimizing protection and mitigating risk – advanced customer data, transaction data and geospatial data combined with advanced data analysis that looks at transaction anomalies will allow for easy risk detection and fraud prevention.
  • Unified data analytics – in times past, large financial services organizations that span their wings throughout different departments had to set up individual Big Data analytics platforms. However, in 2020, unified data analytics platforms will make it possible for large financial institutions to benefit from an easy-to-use data analysis system.
  • Big Data and the hybrid cloud – Leveraging Big Data solutions is all well-and-good, but what will happen when you meet hardware limitations. A hybrid cloud is an approach where a company can extend its internal capabilities with on-demand cloud infrastructure.