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Predictive Analytics for Financial Institutions

Predictive analytics is already changing the world. Stock brokers, Netflix and data miners are all using customer data to anticipate needs, create valuable offerings and better communicate with their customer base. Financial institutions have huge potential with analytics, both in terms of customer data and the capacity to help customers by better understanding their behaviors. With the security of customer data as a top priority for financial institutions, predictive analytics haven’t been as quick to take off as with other less data-sensitive industries (such as media and entertainment), but 2019 and beyond will see the full expansion of analytics into the financial realm and a world of new benefits for customers.

Acquisition

Understanding current data about customers helps financial institutions better segment their potential customers based on behaviors and promote to them accordingly. In this way, institutions can expand their reach while also differentiating their client base.

The ability to savvily target outreach for new audiences helps financial institutions grow, but pre-screening and understanding customers they reach out to also helps to identify ideal customers. Financial figures, spending habits and credit all play into a financial institutions target market, and predictive analytics helps identify these people beforehand.

Marketing and Promotions

Understanding current customers and their finances goes a long way. Especially with FinTech partnerships on the rise, financial institutions are finding creatives ways to make offerings to customers. With predictive analytics, those offerings can be budget/income/customer-specific and feel more relevant as a result.

Cross-selling, offering new products, enhancing existing services offered to customers and simply providing a higher level of customer service are all core benefits of predictive analytics when used as a marketing function. Consider an example of a FinTech budgeting tool. Promotions and advertisements, like something a customer might see on a dashboard or as a banner ad, could be personalized and reflect their unique financial behaviors. By promoting in this data-driven format, customers can get a valuable perspective on the benefits of a new tool from a personalized ad.

Screening and Qualification

Pre-screening is a valuable and important step in selling a financial product or service. Whether a car loan, mortgage or a credit card, pre-qualification helps pave the way for an appealing and seamless purchasing experience. Customers will be delighted by not having to go through the screening process, and will feel validated as a result. Additionally, predictive analytics can help promote theĀ right type of product a customer successfully qualifies for, avoiding any annoyances or questions that might arise when an unexpected obstacle could have come up in the past due to their financial standing and history.

Security

Predictive analytics serve an essential security function for financial institutions by identifying fraudulent activity. Understanding customer behavior and buying habits enables predictive analytics to accurately identify outliers in their traditional purchases as a means of preventing identity theft.

Similarly, predictive analytics will play an important role in the future of information sharing. Open banking is taking off, and financial institutions need to be able to secure important customer information while also sharing it with FinTech startups and other partners. Predictive analytics, including the dissemination of that information and the way it’s being accessed, will help to identify any breaches in security quicker and allow institutions to act quickly in the event of a security breach. This idea extends to businesses and other vendors, who will benefit from the quick identification and nullification of fraudulent activity.




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