Financial services analysts must make sense of mountains of data—so they can predict what’s likely to happen next and make precise, strategic decisions for maximizing returns and minimizing risks in that given future.
This level of analysis and decision-making requires a complete data analytics toolbox, including both machine learning and mathematical optimization. With a full toolbox, analysts can get a glimpse into the future, while also identifying the best way to proceed.
To learn more about how financial services decision-makers are using optimization technologies today, Gurobi commissioned a study from Forrester Consulting. And the results are in.
Download your copy of the complete Forrester study, infographic, and webinar recording today.
Over half of decision-makers indicated that optimization is critical to their financial services organization and over three-quarters of them are planning, expanding, or upgrading their optimization technology implementations.
Financial firms recognize that optimization tools can give them a competitive edge in making business decisions, so time is of the essence.
Every business decision affects the decisions that follow it. It’s therefore important to consider all of an option’s factors before moving forward.
With mathematical optimization, decision-makers can explore a possible decision’s impact by simply tweaking and re-running their mathematical models. This enables them to adjust their strategies frequently and to keep up with the ever-changing business landscape.
Forty-five percent of respondents want to assess and adjust their strategies more frequently, knowing that this approach enables them to reduce operating costs, respond quickly to market changes, manage risk, and achieve greater efficiency.
Financial services firms are quickly taking steps to invest in optimization projects, with 70% investing either in mathematical optimization technology or in using machine learning with mathematical optimization technologies. Over 60% are investing in two or more initiatives for optimization projects.
But acquiring technology is not enough. That’s why 64% of respondents are investing in people with mathematical optimization expertise — whether hiring and training employees in-house or reaching out to a consulting firm.
By turning to mathematical optimization and leaning on optimization experts, financial services firms can embrace the complexity of running a modern business and, ultimately, maximize profits while minimizing risk and maintaining compliance.
In this article, Gurobi Technical Fellow and VP Dr. Gregory Glockner details how organizations are applying mathematical optimization, a powerful prescriptive analytics technology, to power digital transformation, decision optimization, and competitive advantage.
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