CHICAGO–Companies are amassing volumes of data with the intent of optimizing performance, identifying trends and meeting rising consumer expectations, yet nearly 75% of financial services and insurance executives admit in a new survey they are challenged by the fractured nature and vast amount of data available.
“As a result, it’s difficult for many of them to achieve rich analytics capabilities to further their respective businesses,” said Aite Group in the new study commissioned by TransUnion.
The study further found executives in the financial services and insurance industries plan on continuing to secure more data sources, and that they will look to incorporate more artificial intelligence (AI) and machine learning (ML) technology into their analytic platforms to help them make sense of the information.
What Was Explored
According to Aite Group, the global study explored the existing analytical processes, tools, data sources and operational effectiveness of analytics solutions used by the financial services and insurance industries. The quantitative online survey recorded the feedback of 682 marketing and risk executives at financial institutions located in the U.S., Canada, U.K., Hong Kong and India, many of whom do business across the globe.
Aite Group reported the study found that the proliferation of AI/ML is expected to continue over the next 24 months with three in four global executives considering integrating new analytic technology into their platforms. There’s good reason for this implementation as AI and ML can shorten the traditional analytic lifecycle from months to just weeks or even days, Aite Group said.
Among the other findings in the study:
Help Wanted
- To stay competitive in a data-rich world, companies need access to cutting-edge analytic solutions and data science expertise. However, the study found that inflexible legacy technology, talent shortages and regulatory barriers are among the factors that prevent businesses from harnessing the power of analytics with speed and ease. “Most financial institutions lack a single, cohesive analytics platform,” said Tiffani Montez, senior analyst at Aite Group.
- Enhancing analytic capabilities through AI/ML technology is a top priority globally, but with distinct differences across geographies. The United States lags in AI/ML technology adoption with 22% of U.S. executives indicating they currently do not have any solutions that can implement AI/ML into analytical models. While this capability may be commonly lacking, 66% of U.S. respondents also believe this technology is a major differentiator.
- The data scientist talent shortage is another pressing issue contributing to the global insights gap. As the volume of data has increased, the need for data science and analytics professionals has increased exponentially. Globally, 86% of respondents noted there are challenges with accessing the right data science and analytics talent, compared to 74% of executives in the U.S.
Growing Data Sources
- Financial institutions have placed an increasing amount of influence on the value of expanding data sources. The desire to invest in data includes new sources such as non-traditional, third-party and alternative data among the banking and insurance communities. Over the next 24 months, 89% of institutions have plans to use alternative data.
- More than half of respondents plan to increase spending on most types of data sources with 65% intending to increase spend on newer forms of data such as mobile information about web browsing and app usage. In the U.S. alone, 44% of executives indicated that the integration of new data sources will be very important to their business strategies. Yet the lack of the right tools continues to pose an issue as only 14% of U.S. firms can integrate new data sources across all of their analytic solutions.
- The survey also found that across all regions, 78% of marketing executives and 70% of risk executives expect their overall budget to increase year-over-year, for data analytics/big data and analytics/data science tools for each role, respectively. This points to a significant investment in expanding the amount of data available despite ongoing challenges such as data cleansing and prep, which 76% of respondents said can be significantly challenging. This is in addition to the larger operational issues such as cumbersome technology and the talent deficit.