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Three ways credit unions use predictive analytics today By Wendy Grieco

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Advances in processing speed combined with the advent of sophisticated machine learning (ML) and artificial intelligence (AI) solutions have improved credit unions’ ability to stay ahead of the curve by tracking the latest customer behavior patterns. With modern software tools capable of sifting through tremendous amounts of raw data, credit unions can benefit by using
The post Three ways credit unions use predictive analytics today appeared first on ibi.

Advances in processing speed combined with the advent of sophisticated machine learning (ML) and artificial intelligence (AI) solutions have improved credit unions’ ability to stay ahead of the curve by tracking the latest customer behavior patterns. With modern software tools capable of sifting through tremendous amounts of raw data, credit unions can benefit by using predictive analytics to mine actionable insights.

With high-end analytics solutions no longer limited to large banks and other major financial institutions due to pricing, credit unions of all sizes can now use cutting-edge analytical tools to:

  • Put machine learning and AI to work to develop improved models
  • Engage in iterative benchmarking
  • Help guide strategic development

These tools, which use statistical models and advanced ML algorithms, can parse member data to reveal patterns that would otherwise remain hidden.

There are three use cases where credit unions can effectively use augmented analytics:  to identify potential new customers, retain existing customers, and mitigate foreseeable risk.

Identifying potential new members

The plethora of data available to organizations these days has bolstered the use of predictive analytics to help boost customer retention and acquisition. Credit unions can use these augmented analytics to help them identify potential new members who fall within desired risk criteria.

Advanced analytics solutions can ingest massive data sets, which removes the time and effort that would otherwise be required to sort through your data to determine where you are most likely to find valuable insights. As a result, credit unions can mix data taken from their portfolios with data sets comprising multiple years of credit, commercial, auto and alternative data that has been stripped of personalized information.

Using advanced analytics solutions such as the ones ibi offers in conjunction with tools such as TIBCO Data Science, Tableau, Panoply, SAS, and Python can enable credit unions to gain further actionable insights. By using AI and ML solutions, credit unions can hone in on the most valuable information in their data without having to wade through excessively complex logic chains. These solutions enable them to efficiently structure their data flow to optimize its value and use their most effective tools for reaching out to prospective customers.

An additional use for predictive analytics in the client acquisition process involves applying advanced tools to quickly analyze prospect rejection behavior. The software uses a reject inferencing approach that enables credit unions to determine if prospects who chose not to accept a loan offer instead got a loan with another lender. Typically, this type of archiving takes at least six months, but advanced analytics engines can dramatically reduce this time, helping lenders boost portfolio size and quality.

Retaining existing members with excellent portfolio management

To manage their portfolios optimally, credit unions are increasingly turning to AI and ML applications. These tools can help them find new ways to both retain and expand their member base.

Predictive analytics help credit unions find opportunities for cross-selling. This is accomplished by using propensity scoring, which gives lenders a chance to, for instance, determine the likelihood that a client will apply for a credit card and a loan with them.

Another way to identify situations where cross-selling may be beneficial is to discover what financial services offerings your members are searching online. Offering your members additional products that are likely to appeal to them helps direct their business to your institution and enhances member loyalty.

Peer benchmarking, which compares your portfolio to those of your industry peers, is another method credit unions can use to enhance member retention by keeping their portfolio within industry standards. Analytics solutions can provide this type of information, helping credit unions see where they are doing well and what their competitors are doing to attract and retain members.

Peer benchmarking can be an effective method for identifying ways that a credit union’s business approach can be changed to enhance member loyalty and increase market share. For instance, if analysis shows that a segment of a credit union’s auto loan members are getting mortgage loans from another lender, that represents an opportunity to cross-sell mortgage loans if the credit union offers that product.

By helping credit unions keep up with the latest industry trends, advanced analytics help them stay competitive in a rapidly changing financial landscape.

Mitigating foreseeable risk

Predictive analytics can help credit unions sort through huge amounts of data to improve risk management in a number of ways, including:

  • Running analytics models with greater frequency to manage risk while boosting revenue from new member acquisition. An example would be a credit union using analytics to gain the insight that members who become delinquent on personal loans also tend to have difficulty paying their auto loans, enabling the credit union to act on and mitigate this risk going forward.
  • Bolstering collection performance: Credit unions using ML and AI can more accurately determine which members can pay their debts.
  • Loss forecasting: Advanced analytics tools can help credit unions prepare for the future by using loss forecasting. ML tools that incorporate trends linked to periods of recession enable credit unions to effectively analyze risk by testing their portfolio’s response to such conditions in near-real time.This analysis allows you to determine what adjustments you might need to make so your portfolio can weather the next recession.

Advanced analytics engines capable of performing predictive analytics can help credit unions compete with much larger financial institutions in client retention, risk management, and trend identification.

ibi, a TIBCO company, provides the analytics that power data-driven applications worldwide. We streamline data using AI and ML to help you quickly garner relevant insights from augmented analytics. To see how ibi can help you get the most out of your data, request a demo today to see our analytics platform in action.

To learn more about the value of analytics to credit unions,download our ebook Understanding the value of analytics: a guide for credit unions

About the author:
Jon M. Deutsch is Senior Director of Financial Services at ibi, a TIBCO company.  Jon leads the effort to develop market-leading enterprise data and analytics solutions for global financial services, with a focus on Credit Unions.

 

The post Three ways credit unions use predictive analytics today appeared first on ibi.