For financial institutions, this kind of forecasting can directly translate to revenue. Models can predict how a stock might perform or how much interest the organization can collect on loans. According to Deloitte's David Cutbill, it plays a crucial role for those financial services navigating tumultuous times.
“Agile planning, short-term capital allocation, and predictive forecast models collectively foster a more resilient business that can improve business and finance performance now while setting up future success in an ever-changing environment,” Cutbill writes in a blog post.
A large part of becoming resilient is being able to properly assess and mitigate risk, and a growing number of financial institutions are using predictive analytics for this purpose.
How Predictive Analytics Can Be Used in Risk Management
Forecasting and risk management aren’t new concepts for financial institutions, which have always accounted for risk when approving loans or making other decisions. The difference now is that risk analysis doesn’t have to be done manually, and that can lead to fewer mistakes and biases while freeing up key resources.
“Organizations can start by hotwiring traditional planning and forecasting processes and leveraging more predictive analytics and data technology for data consolidation, planning, budgeting, and scenario assessment,” writes Cutbill. “Better risk mitigation with scenario analysis capabilities can identify the degree of influence of inputs and outputs and lessen the impact of management biases on decisions and execution.”
For financial services, risk management is particularly critical. There are compliance standards that must be met, and the sensitive nature of the business means that there is more potential backlash from customers if a mistake is made. Predictive analytics can help inform sound investment decisions, detect fraud and provide accurate projections for revenue.
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There are additional benefits to customer operations as well. For example, organizations can use forecasting to assess whether someone is a good candidate for a loan, shortening what can otherwise be a tedious process. Leaving the decision up to a model can also eliminate potential bias that can arise when these procedures are done manually.
What Infrastructure Is Needed to Use Predictive Analytics
Like most data-driven tools, predictive analytics must be built on a strong foundation of data infrastructure. Artificial intelligence and machine learning solutions are key to predictive forecasting, and the organization’s data center must be able to support those tools.
Organizations also need to have multiple inputs available when building these models. This can be difficult if data is siloed throughout the IT environment. Integration and visibility are key to success with predictive analytics, and cloud solutions can often provide both.
This can present a challenge for financial services, because there are additional compliance concerns surrounding data center infrastructure. According to FinTech Futures, 39 percent of IT leaders interviewed said that complying with regulatory standards would prevent them from using predictive modeling or predictive analytics solutions in their organizations.
Financial institutions also face different circumstances in the cloud, but there are many ways organizations can tackle their cloud environment to ensure they reap the benefits of next-generation data solutions while obeying regulators.
Data analytics isn’t just for examining the past — it also can help organizations build toward a better future. For financial institutions batting back risk every day, predictive analytics can unlock a more stable and productive operation.
