How machine learning helps credit unions compete during a pandemic

The US financial market is a competitive and ever-changing landscape. Over the last year, the pandemic has only heightened and illustrated this matter.

As a result, the market is now exploring alternate means to engage with customers since the more traditional means to interact with customers has changed, as brick-and-mortar locations have been abandoned throughout this period. Some of the larger financial institutions have been historically more prepared to adapt to such demanding and dynamic changes. Needless to say, credit unions have been presented with a different set of challenges from a capital perspective, whether it be financial or resource-based. 

Now, more than ever, credit unions are faced with various issues that require revamping of marketing strategies, streamlining the onboarding processes, improving retention, etc. 

Below are just a few of the challenges that most credit unions must be prepared to address:

• Severe competition in the market

The National Credit Union Administration (NCUA) mentioned in its Annual Report 2020 that small credit unions face risks regarding viability due to such reasons as low returns on assets, declining membership, high loan delinquencies, etc.

Of 5,099 existing federally insured credit unions in the US, only 649 have assets of at least $500 million. In other words, if the current trend persists, there will be fewer credit unions in operation.

• Difficulty of attracting the youth

The World Council of Credit Unions states that the average age of CU members is mid-to-late 40s, to be precise, in the US the median age is 47.

Millennials, in their turn, tend to have a really poor CU history which becomes one of the main challenges for credit unions today – the need to reassess offers, channels, and messages to better meet the needs of Generation Y.

• Unsustainable manual labor

Excessive manual operations often lead to situations when credit unions can’t deliver personalized service and shorter turn-around times for their members. The inability to reduce operational pressure leads to declining customer satisfaction and attrition.

• Lack of resources

Many credit unions don’t have enough budget or staff to build advanced data-driven strategies and exploit the potential market through omnichannel targeting to upscale their digital and physical presence.

• Security risks

Like most financial institutions, credit unions can be vulnerable to attacks on their networks. According to AP News, the annual financial risks of direct attacks on credit unions may range from $190,000 for small organizations to more than $1.2 million for large credit unions. These figures demonstrate an acute need for crafting smart prevention measures based on accurate forecasts.

AI Automation for CUs: Why and How?

The challenges mentioned above can become a complete nightmare, but credit unions have an enormous and undeniable advantage – historical databases containing valuable information about their products, operations, clients, and their credit history. Processed appropriately, this data becomes the key to better tailoring of offers according to the client’s needs, attracting and retaining more customers, predicting risks and a lot more.

But how to process such loads of data in the most efficient way? It has become increasingly clear that AI-driven enterprises have the best chance of competing with, and often beating the competition. AI transformation of business implies not only replacing human efforts with regard to repetitive manual tasks, but also creating and defining new, innovative, and forward-thinking means to engage one’s customer base. Such models, once trained, can significantly increase the efficiency of the entire organization by providing timely, accurate forecasts and/or realizing trends that were previously unseen or, quite frankly, thought to be impossible. 

Now, how is all of this typically accomplished with a traditional approach? You would need an army of data scientists and analysts to shovel the data and set all the ML algorithms manually, which is far from being efficient or cost-effective. According to Glassdoor, the average salary of a senior data scientist in the US varies from $140,000 to $150,000 per year, and such specialists are really difficult to find and retain. Luckily, AI automation comes to the rescue.

Contrary to popular belief, AI adoption ceases to be complex, costly and time-consuming – like never before. Now, such technologies have entered the path of democratization and become accessible to credit unions of all sizes. Needless to say, even business users can build machine learning models in a matter of minutes, without special technical knowledge or coding.

Using predictive analytics is a prudent strategy for credit unions which helps them to stay ahead of the curve by automating the workflow, mitigating the risks, and predicting members’ behavior.

Customer risk rating is among the main CU challenges, so let’s take a closer look at this case and how a no-code AutoML platform can help to address this issue more quickly.

Case: Customer Risk Rating

Issuing loans always implies certain risks with regard to the borrowers possibly failing to pay them back, so credit unions use credit scoring to forecast and minimize any possible risks associated with their borrowers. Such scoring represents an evaluation of how well the CU member can pay off debts and is usually based on many factors such as the member’s credit history, salary, amounts owed, etc. However, traditional approaches to credit scoring methods tend to lack sensitivity due to the high degree of standardization. As such, credit unions often reject borrowers who are credit-worthy, which means low profitability.

Today, AutoML platforms offer the ability to engage a more individualized approach to credit scoring. The creation of predictive machine learning models provides credit unions with a more accurate assessment of customers’ financial behavior based on not only historical data, but also on a large number of in-depth rules and potential income forecasting.

As such, machine learning enables the consideration of specific attributes of each CU member and making of transparent credit decisions, without wasting time on the repetitive manual review. In addition, such self-learning models continuously enhance their predictions once new data appears in the system. This helps to make faster decisions and increases the pool of credit-worthy members, with fewer risks.

On a Final Note

The global business impact of AI automation is hard to underestimate as some solutions are able to reduce the time for prediction making from weeks to days – or even hours – and streamline the average customer’s time to market by 70%.

In addition, AutoML platforms can relieve credit unions of excessive manual efforts and speed up the decision-making process, leaving more time to focus on higher priorities and corporate values, such as building trust within the community, while offering responsive and transparent customer service.

If you are looking to compete effectively in your market, Neuton.AI is a good example of such a no-code AutoML platform. To learn more about how this tool can help you solve your credit union’s current challenges, contact us at welcome@neuton.ai.