5 Ways Prescriptive Analytics Helps Deliver Better Financial Services

Financial services companies are always striving to serve consumers better, but maximizing services requires efficiency and financial stability. Each service provided must be calibrated to provide what the customer needs without wasting company resources.

Prescriptive analytics is a data sciences field that shows companies the best decision to make in a given scenario. This field uses specialized data analysis programs to consider a range of possible decision parameters, then analyze which one gets the desired results.

Although predictive analytics in banking is helpful and essential, prescriptive analytics takes the data a step further. Predictive analytics shows companies the raw results of their potential actions, while prescriptive analytics shows companies which option is the best.

Prescriptive analytics is useful in a wide range of applications, from manufacturing investments to self-driving cars. However, it can be particularly helpful for financial services analytics due to its ability to harness long-term economic trends and customer data, including big data.

Here are the top five ways financial services companies can embrace prescriptive analytics to make even better business decisions.

1. Optimize Financial Services

Prescriptive analytics can be trained to calculate what would happen if companies tweaked aspects of their products. For example, prescriptive analytics can tell a company how much to reduce the cost of a product to attract new customers while keeping profits high.

Since most financial services companies have a wide variety of products and services, applying prescriptive analytics to each of those services can maximize profits while minimizing risks. Prescriptive analytics enables leaders to determine the best potential ideas in a simulation, instead of experimenting in real life.

Making financial services more efficient isn’t the only way to improve them. Customer value analysis in financial services requires companies to take a close look at what elements of a service make it valuable and likely to attract customers. Customer perception of services can be surprisingly subject to change, and even a minor shift in service terms and limits can make customers seek out a competitor.

3. Risk Management

Data analytics in financial services can help companies assess and deal with risks. Insurance plans, mortgages, and new credit card accounts all come with risks for the provider. While some of this risk is unavoidable, some of it can be accounted for and mitigated.

An example of prescriptive analytics in risk management is calculating what would happen if a company tweaked its mortgage qualification criteria. By analyzing a broad amount of data like income and risk of foreclosure and accounting for several economic scenarios, a mortgage company can determine if relaxing their criteria is worth the potential increase in customers.

Prescriptive analytics programs can tell companies exactly how much to relax or tighten their qualification criteria for services. They can even take into account customer satisfaction and long-term economic forecasting.

Risk analytics in financial services cannot account for all variables, especially in the wake of the COVID-19 crisis and economic recession. However, prescriptive analytics can still analyze long-term trends, and current customer needs to help shape their conclusions.

4. Plans for Expansion

Opening new branches and services requires financial services companies to commit a large number of resources. Prescriptive analytics in banking includes processes for determining which expansions are worthwhile, and which are more costly than they are worth.

This is one of the uses that make prescriptive analytics part of big data strategies for financial services. Big data like customer location, financial habits, and mobility can play a large part in deciding whether a new service or location is worthwhile. A bank or credit union shouldn’t open a new branch if the market is already saturated, or if the branch is in an area that isn’t accessible to the target demographic.

It’s also a key area for harnessing data mining in financial services. Data mining allows companies to take large amounts of data, including big data, and sort it into a usable format. Data mining can find hidden and emerging patterns in customer habits and trends, and prescriptive analytics can then apply that information to future scenarios.

5. Reduce Spreadsheets and Boost Efficiency

In addition to affecting your customer-facing services and income, an excellent prescriptive analytics program can reduce your reliance on spreadsheets and manual data analysis. Most financial services companies use data professionals who clean, maintain, and update data in several formats.

Digital analytics in financial services don’t have to rely only on a team of professionals. By using prescriptive analytics from financial services analytics companies, banks, and other providers can overcome the limits of traditional methods and reduce the strain on data analysts. This, in turn, frees them up to work on other tasks to improve the company further. 

Projecting the Future

Since computer processing power is continuously improving, prescriptive analytics technologies also improve. Big data analytics in finance will continue to evolve and provide more accurate calculations and predictions as more companies use them.

Prescriptive analytics enables you to make more informed decisions, decreasing risk and loss. At Syntelli Solutions, we have experience serving a variety of clients in the financial services sector and can work with you to implement customized solutions.

We offer a range of support services for data science in finance, including prescriptive analysis, software training, cloud server support, and more. Contact our team today to learn more about how we can help you maximize your growth.

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