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.
Smarter decisions, faster
Decision optimization is ideal for financial organizations that are wrestling with how to make business decisions faster and more efficient while aligning them with business goals. This technology can uncover new ways to drive profit and customer centricity while continuing to provide new insights that can amplify results even after the initial return on investment (ROI) has been reached.
Consider these real-world examples in three of the most common types of applications:
1. Customer-focused sales and marketing
Banking customers today are more knowledgeable and mobile than ever before. They have a plethora of choices and they shop carefully for banking products, including checking and savings accounts, loans, and investment products. These customers expect their data to be packaged into personalized advice and benefits, tailored to their financial goals and personal needs.
Sales: a large bank in Spain with more than 90 million customers worldwide uses decision optimization to enhance customer care and drive sales. By filtering several million sales opportunities and selecting only the ones likely to result in new sales, the bank can optimize its sales network without risking customer relationships. The results are improved customer satisfaction and higher bank ROI.
2. Product innovation and management
Banks can be challenged to develop new products and solutions that serve customers’ immediate financial needs. In a competitive environment in which fintech firms are encroaching, decision optimization offers the speed and innovation firms need to differentiate their businesses.
Loan configuration: a retail bank wanted to improve the efficiency of loan processing and find a better mix of loans to meet customer demand. Applying a decision optimization solution enabled it to increase average loan size by 15 percent, reduce time to funding from 21 to eight days, lower underwriting costs by 78 percent and achieve an increase by a factor of four in monthly loan volume.
Derivatives pricing: an optimization-based solution quickly determines mid- and short-term derivatives trades and identifies the lowest cost hedges and unhedged risks, reducing trade pricing and decision time by 83 percent and quantifying the cost of the risks.
3. Streamlined operations and execution
Many firms seek to reduce operational costs and improve processing speed and security. This is often the case for trading firms or financial exchanges, where maintaining both transparency and confidentiality during the clearing and settlement process must be balanced against regulatory requirements. Decision optimization delivers technical and infrastructure innovation that can reduce or even eliminate human intervention where it would slow down or negate the value of the decision.
Conditional value at risk (CVaR): a global financial services company with assets of more than USD 1.4 trillion needed to meet Federal Reserve stress test standards to demonstrate its ability to remain stable throughout various economic fluctuations and gain approval to increase dividends. By creating a simulation for the stress tests using decision optimization, the bank met the standards.
Collateral optimization: a large European bank has reduced customer costs and increased business through prescriptive, analytics-based collateral management optimization, achieving a savings of 5.3 basis points above “cheapest to deliver” best practices.
Simplifying complex decisions with data science
These solutions all have one thing in common: they were built using IBM prescriptive analytics that brings powerful operations research technology into the mainstream for everyday business use.
As financial firms move to transform their intelligence into action, mathematical optimization is becoming a must-have tool for strategic and operational planning. Portfolio managers and other banking professionals can use IBM technology to explore scenarios in a fraction of the time, accelerating decisions and improving outcomes.
View the infographic to learn more about the ROI of IBM Decision Optimization and explore how data science teams can capitalize on the power of prescriptive analytics using machine learning and optimization.