The future of banks and credit unions is murky. No, they’re not going to disappear, despite what the doomsayers will say, but they’re going to change. They have to. And the winners already are.
Artificial intelligence and machine learning have already started changing the landscape of financial institutions. That trend will continue, both in the near future and the long term.
Uses for AI in Credit Unions and Banks
There are several quick wins in the world of AI and finance right now. They’re the sort of turnkey, plug-and-play solutions that you can onboard and begin reaping the benefits of in a matter of weeks or months.
So, if you’re looking for some quick wins, here are a few ideas.
Conversational AI. Do you remember chatbots? Like ELIZA or SmarterChild? Well, they’re better now. A lot better. Banks and credit unions are using conversational AI to help direct web traffic, answer questions, and even cross-sell products. The early ROI for this type of banking AI is impressive.
Pattern recognition. Machine learning can analyze transactions to identify patterns. WalletFi uses these patterns to help users better manage their cards, payments, and subscriptions. Other companies use this same technology for extremely accurate fraud prevention.
Credit scoring. FICO scores are obviously not the best indicator of creditworthiness out there. Many companies are using AI to predict creditworthiness with a much higher degree of accuracy than their traditional counterparts. AI in lending is big.
But to be clear, this is the present of AI in banking. Let’s look a bit at the future.
Future AI in Credit Unions and Banks
Looking down the road a bit, AI solutions will get more robust. That’s not to say that the options above aren’t worth pursuing—they absolutely are! However, many AI products are currently standalone options.
The future is more… connected.
Enterprise intelligence will be a major buzzword in the financial industry. And for good reason. Banks and credit unions generate massive quantities of data every day. It’s only a matter of time before they’re able to harness it.
To see benefits from data, financial institutions will need to:
- Generate and collect more data. Machine learning needs a lot of data to work effectively.
- Ensure privacy and security. Financial data is sensitive data, and protecting against breaches and controlling what sorts of information can be accessed in the case of one is top priority.
- Give consumers more control over their own data. Europe is already starting to provide consumer protection with GDPR regulations, and California is quickly following suite.
These steps aren’t far off future to-do’s. These regulations are being rolled out today, in order to pave the way for enterprise intelligence in the future.
What Is Enterprise Intelligence?
Enterprise intelligence is a somewhat nebulous concept. Generally, it refers to a business-wide ecosystem driven by data. Machine learning provides predictive analytics that offer detailed insight into many or all aspects of a business. It can also access huge amount of interconnected data points around account holders, business processes, products, and services.
Think about what you could do with that.
Think about what kind of correlations you could find.
Think of the problems you could avoid.
We’re not talking about regular business intelligence platforms here, either. We’re talking about AI-powered platforms. Rather than just handling reporting and analytics, AI would be more able to tie things together, make recommendations, and adapt on the fly.
And this isn’t far off in the future, either. For many institutions, that future is almost here.
The reality is that AI is already here in the financial industry. Moreover, it’s getting more impactful each year. If your institution isn’t already looking at AI-powered products, it’s slipping behind.
But it’s not too late to catch up!
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