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3 ways prescriptive analytics helps deliver better financial services

As any financial services executive knows, improving business results with precise, timely decisions is much harder than it looks. A multitude of factors can get in the way of achieving the optimal mix of risk mitigation, profitability, operational efficiency and customer experience, including the speed of technology advancements, new market entrants and constantly changing regulatory requirements.

Even if you have enhanced your business decisions with predictive analytics, you may not be accounting for all the risks and uncertainties present in today’s financial landscape. For example, you may not be considering how issuing too many lines of credit or underpricing loans may impact other areas of your business, such as your collections department.

That’s why traditional companies and fintech startups alike are turning to decision optimization technology to maximize opportunities and minimize risks. Powered by prescriptive analytics capabilities, decision optimization software applies simulations, operations research and complex mathematical algorithms to big data. You can optimize trade-offs between business goals — such as reducing customer service costs or improving customer satisfaction — and determine the best course of action in each situation…

Read Full Source: IBMBigDataHub

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