Financial institutions, both retail and commercial, have more data on their customers than anyone else. But they still struggle to extract meaningful information and use it for good business decisions. By some estimates, businesses use only 0.5% of available data. To turn data into insights, firms must overcome data stuck in silos and incompatible formats, privacy concerns, and more. This is costly, both in time and money. Firms need a new approach.
Beyond the buzzwords. In 2017, financial firms were busy finding productive ways to use the mountains of data they collect. Asset managers use big data to analyze non-financial factors when evaluating financial instruments. Banks use heuristics to analyze marketing campaign results, improving the return on these investments. Some home insurers now offer discounts on premiums for customers who install connected smoke detectors in their homes. And while these devices can mitigate losses, insurers also hope to use the data to better understand customer risk profiles.
Insurers take the lead. Many insurers started to focus on upgrading their model risk management programs in 2017. Of course, the industry has always relied on analytics. Now, instead of a commercial software package with a single model, firms use multiple tools with multiple models and more data sources. Some are used in similar processes with separate intent (such as claims models to assist adjusters in predicting severity versus claims level actuarial models to predict severity for reserving or pricing). While this adds complexity and validation risk, firms should be able to make more sophisticated decisions with greater confidence.
Devil in the data. For firms with varying account structures and naming conventions, finding the right data is rarely simple. In 2018, firms will prepare data for machine learning, making it a priority to label a lot of data. It means sourcing, organizing, and curating unstructured data (text, images, and audio), too. They may even make more—creating “synthetic data” mimicking real client profiles to help train systems.
Information overload. The volume and speed of newly available data is exploding, and we could see 44 zettabytes of data created annually by 2020. Firms need new ways to store, classify, and use it all. In 2018, look for more focus on “lean data,” an approach that applies the lean principles of maximizing value while minimizing waste. We’ll see teams defining specific goals for the data (creating value) with a focus on efficiency and speed (minimizing waste).
Teaming for success. Financial institutions will look for success by combining business domain, analytics, and artificial intelligence (AI) experts who understand algorithms and new techniques, as well as data engineers/scientists who can work with cloud technology and machine learning systems. For now, it’s a rare combination, and we expect firms to focus on finding, training, and building teams with these profiles in 2018.
Decisions, decisions. According to our most recent Big Decisions™ survey, only 37% of financial services respondents said that internal data and analytics will drive their next big decision. So how can you make more sophisticated, data-driven decisions? First, you’ll need to understand when to sacrifice sophistication for speed, or vice versa. Make sure your data scientists and business leaders are working together, and match the type of analysis to the problem you’re trying to solve. If you need to understand fast-moving trends about how your clients behave, for example, prioritize speed over lengthy data cleaning and advanced analytics. Ideally, you should also mine unstructured feedback data for more immediate insights on the changes you should make.
Let’s get it together. You can’t get insights from the data you have on client behavior if it’s scattered across the firm in disconnected databases. For information you can act on, create data lakes (repositories for both structured and unstructured data that can evolve based on business needs) that bring together data from different sources to power the applications of the future.
“We can now get access to very different types of data to make better decisions in almost any function. But this will require different skill sets, and everyone in the organization will need to adapt.”
We’re in a world of data. And as PwC’s Sean Joyce and Dave Hoffman note, financial institutions realize the importance of protecting that data.
Why is artificial intelligence (AI) expanding so rapidly in financial services? PwC’s Anand Rao says it goes way beyond accelerating technology.
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Financial Services Advisory Leader, PwC US
Tel: +1 (646) 471 4771
US Data and Analytics Insurance Advisory Practice Leader, PwC US
Tel: +1 (312) 298 3597
Global Growth Strategy, US Financial Services Practice, PwC US
Tel: +1 (312) 298 6823
Leader, Financial Services Institute, PwC US
Tel: +1 (720) 931 7836