10 Jul 2020
How can data analytics help you uncover the valuable business intelligence needed to surpass your Financial Services competitors?
Along with Retail, the Banking and Finance industry has earned a reputation as a leader when it comes to using big data and data analytics to its advantage. According to an EY study in 2016, more than half (57%) of Financial Services organizations were already investing in building skills in predictive and prescriptive analytics. But Financial Services organizations can’t afford to relax their efforts, particularly in view of the economic challenges we face today. As Vivian Zhang, founder and CTO of NYC Data Science Academy, writes on the NASDAQ blog, “The future of finance relies heavily on data.” What kind of data? Certainly, internal data—on customers or on transactions—is a critical component of any data analytics program, but Financial Services organizations can unlock even more value by complementing internal data sets with third-party data to provide context and next-level insights.
4 ways Financial Services benefit from big data
The business intelligence that can be discovered using big data and analytics has the potential to transform how Financial Services organizations approach numerous challenges across the enterprise, enabling growth, even in difficult economic times.
- Research for financial professionals enables customer satisfaction and retention.
Banks are on the forefront of leveraging customer transaction data to understand customers and deliver better experiences. That being said, the Financial Services industry still struggles with customer churn. According to Statista, the industry had a customer churn rate of 25% in 2018—the third highest rate behind Cable (28%) and Retail (27%) industries. Behavioural analytics can help these organizations spot attrition triggers sooner and respond proactively to engage at-risk customers. In addition, trend analysis leveraging current and historical news data and social commentary can provide guidance for product and service innovations that attract new customers while driving loyalty among existing clients.
- Predictive analytics enhance investment decisions.
"Over the last couple of years,” says consulting firm McKinsey, “the application of advanced analytics to specific business problems has started to deliver value for traditional asset managers—not by replacing humans but by enabling them to make better decisions quickly and consistently." McKinsey also notes that data analytics are being used across the entire value chain of asset management firms, hedge funds and other investment businesses. Investment portfolio performance has seen "meaningful improvement" by using alternative data and quant modelling to find new sources of alpha. Organizations can also automate ingestion of relevant third-party data through flexible APIs to facilitate research for financial professionals. For example, Critical Mentions data enables investment firms to identify market signals—days before market analysts’ weigh in—from full-context executive interviews rather than the selective soundbites that are more widely reported.
- Transaction data analytics help identify potential fraud.
Fraud ranks high on the list of concerns Financial Services organizations face. The Motley Fool reports that “Credit card fraud has been steadily increasing over the years, but it exploded in 2019, with the number of reports increasing by 72.4% from 2018.” This is bad news on multiple fronts. On top of billions in losses related to credit card fraud, Financial Services organizations can also experience a serious loss of trust when they fail to protect customers from fraud. Machine learning algorithms can be trained to identify unusual activities that signal possible fraud, allowing them to alert customers to danger.
- Risk analytics protect against regulatory fines and loss of trust.
Fraud isn’t the only risk consideration that organizations across the Financial Services industry face. Organizations in this highly-regulated industry must also comply with a complex array of laws, from Know Your Customer (KYC) and Anti-Money Laundering (AML) requirements of the Financial Crimes Enforcement Network (FinCEN) to Anti-Bribery and Corruption laws like the Foreign Corrupt Practices Act (FCPA). Machine learning algorithms and robotic process automation can help financial institutions automate the due diligence process, conserving valuable human resources for tasks that require emotional intelligence.
Data analytics holds the key finding the business intelligence needed to accelerate growth and increase profits—whether your Financial Services organizations wants to enhance customer experiences or improve business operations. Do you have the data you need to power your AI applications?