Financial institutions have long realized the benefits of analyzing consumer data. But recently, these organizations have increasingly been turning to predictive analytics, which PwC has called “the future of financial software.”
If you work in financial services, you know massive amounts of data are processed by these firms every day. That means the ability to process data and draw intelligent insights from it is a major competitive advantage.
In the past, financial decision-makers relied on knowledge, experience, and proven business models to deliver services and products to customers. But predictive analytics — powered by artificial intelligence — has disrupted these legacy methods. Many financial services companies are already reaping the benefits of this new technology’s application, particularly when it comes to better planning and cost-effectiveness.
In a recent survey of finance executives from Deloitte, 49 percent of respondents said the greatest benefit of incorporating predictive analytics for financial services has been better decision-making capacity. That said, predictive analytics has a wide range of financial applications, from upgrading routine activities to informing top-level decision-making.
The Benefits of Predictive Analytics for Financial Services
Companies that adopt predictive analytics for financial services are poised to reap several benefits. These might include:
- More organizational agility. Predictive analytics enables a company to keep on top of the difference between customer expectations and the latest developments. This forward-thinking capacity improves the ability to spot gaps between expectations and emerging reality for quick responses.
- Better sales strategy. When predictive analytics is incorporated into an overall sales strategy, it can significantly reduce cost per customer acquisition by allowing sales teams to target the prospects most likely to convert, as well as those most likely to engage in upselling or cross-selling efforts.
- Improved customer satisfaction. By analyzing both historical and the latest customer data, predictive analytics can help financial services companies keep their finger on the pulse of the customer base and identify areas for improvement.
- More targeted products. Predictive analytics provide financial services companies with greater insight into their various financial products. This allows companies to better detect issues, calibrate pricing, and adjust other product factors.
- Better strategy and tactics. With predictive analytics, financial services companies can use the latest data to continuously review ongoing sales, marketing and CX strategies and tactics. This also includes proactive solving support issues in advance resulting in decreeing customer churn. Long-term strategy can also be tweaked incrementally based on the insights obtained from predictive analytics.
Use Cases of Predictive Analytics for Financial Services
How exactly can financial services organizations best leverage predictive analytics? There are a number of ways, including:
- Transactional analysis. Whenever a customer performs a financial transaction, it generates consumer data. On a large scale, a predictive analytics platform can analyze these transactions within seconds to provide insight into marketing and product development activities. Through predictive analytics, companies can create better products in sync with customer demands. They can also more effectively customize marketing to particular customer segments. Products and marketing can be targeted to customers based on patterns in their financial transactions. Customers spending significant amounts on auto repairs, for example, could be targeted with auto loan products and marketing. By providing customers with financial products they want, companies can provide a better experience.
- Demand forecasting. Predictive analytics for financial services can develop highly accurate forecast models for various time horizons. With more accurate demand forecasting, financial services companies can adjust staffing, address inefficiencies in sales processes, and address events-based shifts in demand.
- Addressing overstaffing. A big part of financial services is providing customer service. Predictive analytics can help financial institutions meet customer service demands by providing accurate predictions. Predictive analytics can project the types and size of customer service demands. These projections can optimize staffing levels and potentially reduce overstaffing.
- Customer relations. When applied to customer relations, predictive analytics can improve customer acquisition, service, customer relationship management, and customer retention. Predictive analytics can track customer preferences to enhance selling tactics and overall marketing strategy. The technology can also be used to answer popular questions and customize communication channels based on customer interactions. When customers are better served through a proactive approach, it significantly improves their experience, supporting greater retention. Predictive analytics can also anticipate customer churn. Foreseeing potential churn allows financial companies to apply targeted customer retention efforts.
- Understanding external factors. External factors affect every company, and financial institutions are particularly vulnerable to economic conditions. Using predictive analytics for financial services, companies can model various scenarios — such as rising inflation or falling rates — and assess the impact of these scenarios. Each scenario can include different external factors, allowing for wide-ranging predictive analysis. Based on the most influential factors, an organization can then create actionable strategies targeting different opportunities.
Superior Predictive Analytics for Financial Services and More from Pecan AI
When companies partner with Pecan AI for predictive analytics, they quickly gain access to actionable insights, allowing them to see a rapid return on their investment.
This is particularly true for companies that are already collecting large amounts of data. With Pecan AI, companies can quickly plug data sources into our predictive analytics platform for automatic data restructuring, cleansing, feature selection, and engineering. Once up and running, our platform constantly tracks and optimizes predictive models, minimizing drift and leakage.
Our platform is also organized into projects and models in order to provide a straightforward user experience. Both data analysts and business stakeholders can use the platform to stay focused on specific KPIs and take action based on the latest predictions.
To understand what Pecan AI can do for your organization, see our business-ready demo in action.
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