Predictive Analytics in Finance

Credit: Wikimedia user Modaniel

Over the last two decades, the financial services industry (banks, insurance companies, and CPA businesses) has seen a rapid evolution of customer behavior caused by digital transformation. This transformation has resulted in the accumulation of vast volumes of enterprise data. Consequently, businesses need predictive analytics to tackle this opportunity for accelerated growth.

Predictive analytics is about building statistical and empirical models to make predictions, applying predictive models, and improving their performance over time.

In today's digitized economy, the success of any business depends on its ability to convert information about the target market(s) and customers into knowledge. Predictive analytics helps us to identify relationships between different variables and then use them to predict future outcomes, i.e., suggesting the likelihood of a behavior or event to take place in the future.

Predictive analytics would let a business make informed decisions, e.g., who should be sent promotion of 25% off on auto and home insurance, or who should be the target of second (home) mortgage marketing campaign by a bank.

Predictive analytics are extensively used in digital marketing to understand customer behavior, and based on that a business can do three things;

  1. acquire new customers
  2. retain existing customers, and
  3. maximize customer value and profitability

Descriptive Analytics

We build descriptive analytics around events or behaviors which have already happened in the past. Descriptive analytics provide an opportunity to analyze performance of business processes and improve them accordingly.

For instance, a banking institute ran a campaign for insurance on credit card balance. The following month when the campaign data came in, the bank generated descriptive analytics, and it turned out that the campaign performed exceptionally well among young people, was average among seniors, and had a low conversion rate among families.

Prescriptive Analytics

Prescriptive analytics is the final frontier of business analytics, and also depends on descriptive and predictive analytics. Prescriptive analytics suggests decision options for a business so that it can take advantage of descriptive and predictive analytics. Prescriptive analytics not only anticipates what will happen and when it will happen but also why it will happen.

For instance, a bank can give mortgage pre-approval to a newly arrived immigrant in North America (prospect). The prospect customer will most likely avail the offer in the next 3-6 months (descriptive and predictive analytics) because by then, he/she would have found a job and arranged down payment (prescriptive).

How Can Datalya Help with Predictive Analytics?

Our Ph.D. data scientists can help you with the development of analytics strategy, implementation of customized predictive modeling solutions, and improvement of existing analytics solutions. We have years of experience in providing predictive analytics services to our clients in commercial and investment banking, insurance, brokerage, and planning businesses. If you have any questions or need help, please do not hesitate to contact us through email or phone.

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