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The key to gaining more members without additional risk

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In today’s data-driven world, financial institutions of all sizes are constantly trying to stay ahead by understanding rapidly changing consumer behaviors. As many lenders know, access to rich data sets is a critical first step to achieving this.

The second, yet equally valuable step, is having the right tools in place to gain actionable insights from data. When lenders have the ability to combine their data with today’s leading advanced analytical tools, they have the key to unlock better decisioning, improved model development, benchmarking and much more.

The problem for credit unions has traditionally been limited resources and access to analytics platforms comparable to those being used by larger banks.

Thanks to recent advances, the latest in machine learning and predictive analytics is now available to credit unions through new configurations of state-of-the-art technology platforms. This new-found access can help credit unions and smaller financial institutions become more competitive while answering questions, such as “where am I winning,” “where am I losing and why” and “what should I do next?”

The benefits of machine learning and artificial intelligence solutions go far beyond this, though. Here are three examples that highlight how credit unions can benefit from integrating analytical environments to their business.

Identify new, creditworthy clients

Credit unions can use advanced analytics to identify new, predictive attributes by looking at more data sets. By doing this, financial institutions may be able to identify new, creditworthy clients without compromising risk.

The latest advanced analytics tools eliminate the need to pick and choose which data sets will be most useful. These advancements allow credit unions to seamlessly combine their portfolio data with years’ worth of additional depersonalized data sets, including credit, alternative, commercial, auto and more. Additionally, credit unions can leverage industry leading tools such as R, Python, H2O, Tableau, SAS and more to gain actionable insights from these big data sets. From there, lenders ensure they’re delivering the right offers to new consumers in much less time.

Through the power of artificial intelligence and machine learning platforms, credit unions can turn on self-service access to the right data while eliminating unnecessary batch data refreshes, complex workflows and inconsistent identity pinning logic. This allows credit unions to streamline their operating environment and turn on the most advanced tools to reach new clients faster and more effectively.

Another way credit unions can attain more clients is by using sophisticated systems to incorporate rapid reject inferencing into models. Reject inferencing allows credit unions to identify prospects who did not move forward with a loan offer and see if they ended up going for the loan somewhere else and how they performed. The traditional archive process can take more than six months. With access to the latest advanced analytics, one lender reduced the process from 180 days to two weeks. Speeding up the process for reject inferencing can significantly increase the size and quality of a lender’s portfolio.

Retain members and stay on top of portfolios

Machine learning and artificial intelligence can empower lenders to identify new opportunities to retain and grow their client base.

Advanced analytical environments can help credit unions uncover cross-sell opportunities by leveraging propensity scoring. This allows lenders to see how likely a client is to open a credit card in addition to a personal loan with them, for example. Credit unions can also pinpoint potential cross-sell opportunities for existing customers by identifying what other lending products their customers may have with other lenders.

Credit unions can also use peer benchmarking, which compares a lender’s current portfolio against peers in the industry, to remain competitive. The latest advanced analytics tools can help lenders identify where they are succeeding in the market and see what consumers are looking for with their competitors.

This helps lenders uncover opportunities to shift their business strategies to help retain clients and gain market share. An example of the kind of insight a lender could gain would be if an existing set of customers are opening automotive loans with other lenders. If you also offer automotive loans, you may be able to market to your existing clients more effectively.

Ultimately, these tools can help lenders discover industry trends and seize new opportunities.

Mitigate risk effectively and prepare for the future

For credit unions, the power of more data and more predictive models can help with improved risk management. It also helps deploy more frequent model updates to maximize acquisition revenue while managing risk. For example, a credit union may be able to identify a trend that clients who go delinquent on auto loans also tend to go delinquent on personal loans, allowing them to potentially mitigate future risk.

To enhance collections strategies, financial institutions can utilize machine learning and artificial intelligence to better identify customers that have the ability and willingness to pay debts. Advanced data environments also let financial institutions continuously evolve and prepare for the future by incorporating loss forecasting. By using machine learning models that include trends identified during the recession, lenders can perform risk analysis and test their portfolio strength and preparedness.

Through artificial intelligence and machine learning, a customizable report can be pulled in near real time to allow for accurate recession planning so credit unions can identify areas to adjust portfolios to prepare for an economic downturn.

The latest advancements in artificial intelligence and machine learning are empowering credit unions to compete with larger banks, benchmark their portfolios against the industry and identify credit trends.

Now, credit unions can leverage these tools to create real business opportunities and prepare their portfolios for what the future has in store. By using machine learning and artificial intelligence, credit unions can make faster, more informed decisions and turn insights into action.

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