As the U.S. economy faces unprecedented challenges, predictive analytics in financial services is necessary to accommodate customers’ immediate needs while preparing for future changes. These future changes may amount to enterprise transformation, a fundamental overhaul of how an organization does business.
Small and large organizations will have to learn to operate in new ways, even if the economy rebounds quickly. Consumer confidence will likely be low after COVID-19 and financial services companies must learn to react in real-time to rebuild relationships and increase investments.
Predictive analytics in financial services is a growing area of interest with constantly emerging technologies. It can make a huge difference in customer experience and your organization’s digital transformation, thanks to its ability to help you make smarter decisions and plan for the future. Even during unprecedented times, predictive analytics’ ability to deal with ever-changing circumstances and new data can be the key to success for organizations across industries.
What Is Predictive Analytics?
As its name implies, predictive analytics is the science of predicting future events using existing data. It uses big data, machine learning, and other data analytics tools to forecast industry characteristics based on current trends and historical data.
Predictive analytics is related to prescriptive analytics, which uses artificial intelligence and big data to tell businesses what profit-maximizing choices to make. In contrast, predictive analytics does not make normative claims or tell the financial services and insurance industry what to do; instead, it makes descriptive claims about industry outlooks.
The Importance of Data Management and Analytics
Finding valuable insights from the data your company gathers takes time and effort. Data governance tools, including artificial intelligence and data lakes, can make your massive amounts of data more manageable.
For example, data lakes allow customer satisfaction surveys to be stored and analyzed in their raw form, reducing the need to manually process or simplify their content. Cloud storage eliminates the need for expensive, on-site servers while still processing your data securely and allowing you to access it quickly. Data processing is faster than ever thanks to technological improvements in computer processing power and artificial intelligence.
Once a general data analytics system is set up, data scientists can continue to improve upon its accuracy, add new features, and update data. Data scientists are taught to recognize how ‘noisy’ data can be misinterpreted and take steps to avoid false conclusions about potential future events.
Putting Customers First
Big data analytics for financial services can benefit you by providing a better understanding of your current customer base. Even if your organization values customer service, there simply aren’t enough hours in the day to reach out to and interview customers about their needs and wants. Predictive analytics in financial services can provide surprising answers to unasked questions and help you consider the whole customer, regardless of which services they’re currently using.
As customers mature and their families grow, their needs change as well. This is especially true in the financial services industry, where customer needs are shaped by family size, income, education levels, and existing assets. A young professional couple preparing to have children will likely develop an interest in college savings accounts, life insurance, and a mortgage.
In addition to influencing the types of financial services offered, predictive analytics can improve your ability to serve individual customers. When a customer fills out an application for a loan or other service, predictive analytics can help assess the likelihood your customer will repay the loan. A high-quality predictive analytics system can guide your business to offer different services, like secured loans or lower loan amounts, to customers who don’t qualify for the service they originally applied for.
Better Online Banking
Predictive analytics can show areas where consumer interest is likely to spike, giving managers enough advance notice to shore up online infrastructure in those areas. If internal metrics and external market factors indicate that many people are likely to become interested in buying homes, marketing teams can update the website to promote mortgage loans to existing customers and IT staff can invest in making online mortgage applications easier.
Data-driven analytics can also show gaps in the system that allow fraud and abuse. Although fraud usually is analyzed as a past pattern and is not fully covered by predictive analytics, predictive analytics can play a role in advising IT staff about which online services should be secured against potential scammers.
Although predictive analytics in banking is helpful and essential, prescriptive analytics takes the data a step further. Predictive analytics shows companies the raw results of their potential actions, while prescriptive analytics shows companies which option is the best. Read More
Predicting Market Changes
The ability to predict future revenue is another growing use of data analytics in finance. With a combination of both internal and external data, your organization can predict revenue growth from specific sectors of the market.
The ability to predict market changes is especially important for growing companies. Even profitable ventures should be examined with predictive analytics to create demand projections, especially with the uncertainties caused by COVID-19. Minor changes to growth plans can increase or decrease your return on investment, with serious implications for investor confidence in the future.
Predictive analytics can also help determine which marketing campaigns and strategies are likely to be effective. If there’s an up-and-coming neighborhood in your service area, intel from predictive analytics could inform a smart marketing strategy targeting this new market.
Rising to Meet Future Challenges
Predictive analytics in financial services are constantly improving thanks to new technologies and abundant interest in science. Your organization can use customized data solutions to minimize the guesswork involved in meeting the needs of your existing customers and reach new ones effectively.
Syntelli Solutions is a leader in providing cutting-edge data science services, including predictive analytics, prescriptive analytics, and data management services. We provide services to a range of industries, but we have special knowledge of the financial and insurance industries.
No matter how large or small your customer base or service area is, custom-tailored data solutions can help you serve customers better and make smarter decisions. Contact us today to learn more about how we can unlock your data’s potential.
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