Financial institutions have seen a very volatile month. Many believe this is just the beginning. However, by knowing their membership more deeply, banks and credit unions can protect themselves from the market risk and volatility in the industry today.
When we talk about knowing their membership more deeply, we mean understanding the financial needs, goals and behaviors of their members. This involves collecting and analyzing data on their members’ spending habits, credit history and financial goals. By having a deeper understanding of their members, credit unions can tailor their products and services to meet their member’s specific needs, which in turn can help increase member loyalty and retention.
One of the key benefits of knowing their membership more deeply is that credit unions can reduce their exposure to market risk. Market risk refers to the potential for financial losses due to changes in market conditions such as interest rates, currency exchange rates, or commodity prices. By having a deeper understanding of their members’ financial needs and preferences, financial institutions can develop products and services that are better suited to their members’ risk profiles. This can mitigate the impact of market volatility on their bottom line.
Credit unions can leverage predictive modeling to better understand their members’ behavior and improve their financial performance. Predictive modeling is the use of statistical algorithms to analyze data and make predictions about future behavior or outcomes. By using predictive modeling techniques like regression or advanced machine learning to determine member propensity to add or churn from products, credit unions can gain insights into member behavior and tailor their products and services to meet their members’ specific needs.
These analytical models can interrogate behavioral data like account usage, transaction history and frequency as well as demographic and psychographic data to identify patterns and trends in member behavior that are predictors of needs for particular products or risks of not needing products and likelihood to churn.
For example, if a credit union knows that a significant portion of its members are risk-averse investors, it may choose to offer low-risk investment products such as certificates (CDs) or money market accounts. This would protect the credit union from losses due to market volatility, as these types of investments are less affected by changes in market conditions.
In addition to protecting themselves from market and credit risk, knowing their membership more deeply can help improve overall financial performance. By tailoring products and services to meet the specific needs of members, credit unions can increase member satisfaction and loyalty, which will lead to improved revenue and profitability.
In today’s fast-paced and ever-changing financial landscape, credit unions face increased market risk and volatility. However, by knowing their membership more deeply, they can reduce their exposure to market and credit risk, improve their overall financial performance, and increase member satisfaction and loyalty. With the right analytical tools and strategies, credit unions can gain deepen member relationships with the institution and personalize the right products and services to serve the economic participation goals of their members.
Vertice AI was founded to help credit unions better know the needs and goals of each of their members using predictive analytics. We want to put the power of AI and machine learning in the hands of credit unions without having to hire a PhD in data science or mathematics. Leveraging the power of these analytical techniques can provide an efficient and more effective way to personalize the member experience while optimizing your marketing and member engagement spend. The Vertice solution provides the data science in a consumable user experience that marketing and growth teams can easily understand and take actions to better serve their memberships with the right products and services for their financial journey.