Hello • I am John Best I am here because I have worked in Credit Unions for over 25 years, and I have been given opportunities that I doubt I would’ve had in any other industry. I believe that every person in America would be in a better financial position if they banked at a Credit Union. Our company is dedicated to driving innovation and delivering quantifiable results so that our clients and our industry will continue on.
Two Paths Transformational Moments Slow Progression
Goals 2. Identify any data that strongly correlates a user’s “profile” with a specific user group. 4. Extract interesting/insightful relationships in the data. 3. Accurately predict a member’s usage profile using machine learning algorithms. Being able to predict how a single user leverages the three primary channels (ATM, Online, Branch) is the fundamental data point needed for higher-level decision making and is therefore the focus of our machine learning. 1. Determine whether natural member “usage groups” exist.
In order to see what Machine Learning might be capable of learning from this data, a few different techniques were tested. What Can Machine Learning Do? Benchmarks Neural Network Decision Tree
Machine Learning With The New Data Random Forest Before We Jump In We asked for more data and we got it. Let’s investigate what impact this had on our ability to use machine learning to forecast usage rates. Bench- marks Decision Tree KNN Neural Network
Do Loan Types Matter? In some cases, yes. On average, there appears to be a correlation between having any direct loan and increased online channel use. No real surprise that indirect auto loan holders use all resources less (on average.) Typically, having any loan increases channel use across the board.
Outlier Group One Here we can see that the tree learned that users of Kwik and the casino are more inclined to use the ATM at higher rates:
Drawing Conclusions Despite some of the challenges faced during this analysis, some conclusions can be drawn from the ML analysis: • ATM usage is most influenced by: • Kwik Shopper • Holiday Shopper • Walmart Shopper • Age • Casino User • Online usage is most influenced by: • Kwik Shopper • Holiday Shopper • Amazon Shopper • Age • Paypal User • Neflix Subscriber • Branch usage is most influenced by: • Distance from branch • Age • Walmart Shopper • Core credit union attributes are relatively week predictors (credit score, balances, loan types, etc.) • “Ancillary” info about the user is a stronger predictor of behavior.
Future Machine Learning Paradigms Regulatory Compliance Current Expected Credit Loss (CECL) “Incurred Loss” (Descriptive) “Expected Loss” (Predictive) The new rules require financial institutions to “predict” risk in their portfolios.
Robotic Process Automation What is it? What is the promise? Who is looking at this? What is the approach?
Technical Details • Using Microsoft Cognitive services • Using Clarifai.com • Raspberry PI • Windows IoT • Web Cam • 3g GSM/GPRS transmitter • GPIO – to shut off ATMs • Microsoft Azure cloud services
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