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Neuton AutoML for Financial Institutions:
Bring Intelligence and Innovation to your Financial Services with Neuton
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Neuton is an Automated Machine Learning service empowering financial organizations to:
Safeguard their existing customer base via churn prevention
Increase confidence in revenue growth and forecasting by identifying the right product and service for the right customer
Identify and prevent threats to the business and risk of losses via customer risk ratings and highlighting loans that may be at risk for default.
We bring innovation to a streamlined yet proven design process to realize meaningful results to business challenges.
Learn More about Neuton Request demo
How has Machine Learning enabled RedWood Credit Union to drive growth during the pandemic?
Tony Hildesheim
Chief Administrative Officer, Redwood Credit Union
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How can Machine Learning play a vital role in digital banking strategies today and in the future?
Jason Schwabline
Chief Strategy Officer, Alogent
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How can Machine Learning be responsibly implemented and deployed within the Financial Services?
Richard D. Lutkus
Partner, eDiscovery and Information Governance, Seyfarth
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Accelerating Growth within a Credit Union in a Post-Pandemic Society

Leveraging Artificial Intelligence

Over the course of the pandemic, the global economy has faced a myriad of challenges, so credit unions have experienced the need to find creative ways and means to retain their existing customer base and, if possible, to expand their footprint.

The most common challenges for CUs include:

  • Estimation of current and supposed risks
  • Lack of customization and proper analytical tools
  • Existing customer base engagement
  • The attraction of new customers, especially millennials

Since credit unions have a community-based focus, they began to experience additional pressures as some larger financial institutions began to extend their reach into the new customer demographic.

Why are the FICO score & BI tools not enough?

Though FICO and traditional BI tools may provide a comprehensive view into a customer’s historical creditworthiness, they are no longer the answer to addressing some key areas the pandemic has presented, because:

  • They lack flexibility and don’t reflect the customer’s behavior within the credit union
  • FICO is primarily a historical view of behavior
  • FICO and BI tools do not account for personalization

As the pandemic continues to apply unprecedented pressure on the community, it is essential to proactively identify products, services, and sectors that may be at higher risk than others such as mortgages, personal loans, auto loans, or other financial services.

Struggling to provide more personalized services to all groups of members, including typical “thin-file” clients, such as millennials, is a strategic growth area for credit unions. At present, FICO and/or BI tools aren’t an adequate solution for this demographic.

Furthermore, the growth of competition in the CU business makes it difficult for credit union executives to demonstrate what distinguishes their credit union from others. Simply providing a digital experience is no longer sufficient, since almost every credit union now offers online and mobile banking services.

So, if leveraging FICO or existing BI tools is not efficient anymore, then what is? Here’s when AI and machine learning come to the rescue, as they enable credit unions to proactively address the mentioned challenges.

AI-driven Solutions as a More Efficient Alternative

The democratization of AI has contributed to the emergence of tools, such as AutoML, that eliminate the need for coding or having special technical knowledge. In other words, users of any tech level can get customized data-driven insights in a few clicks, at a subscription cost. AI is reshaping the corporate world, and credit unions must be at the forefront of this technological development.

The ability to understand the current standing of all members, while also having a clear view of their risk profile moving forward based on data, is the next-generation method of uncovering new growth opportunities within your existing customer base.

The democratization of AI has contributed to the emergence of tools, such as AutoML, that eliminate the need for coding or having special technical knowledge. In other words, users of any tech level can get customized data-driven insights in a few clicks, at a subscription cost. AI is reshaping the corporate world, and credit unions must be at the forefront of this technological development.

The ability to understand the current standing of all members, while also having a clear view of their risk profile moving forward based on data, is the next-generation method of uncovering new growth opportunities within your existing customer base.

Business Outcomes

Member Attrition
Help your business identify and improve upon areas where customer service is lacking
Gain more understanding of revenue growth opportunities or risks
Reduce costs by retaining customers and focusing more efforts on new customer adoption
Next Best Offer (Cross Selling)
Drive additional revenue via more effective campaigns, by understanding adoption before a service is launched
Higher Return on Investment
Increased Customer Satisfaction and NPS
Customer Risk Rating
Establish and automate your KYC (Know Your Customer) ratings
Confidently develop a wider target audience by understanding the risk factors of each customer individually
Understand which customers are at risk as well as who is posing a risk
Loan pre-approval without FICO score
Analyze customers’ credit score without FICO check
Forecast the probability of an individual failing to pay back a financial obligation
Provide loans based only on customers’ internal score, without additional requests to FICO
Customer lifetime value prediction
Predict how long a customer is going to stay with the CU based on their behaviour
Create special offers for clients who demonstrate high interest in the CU services
Introduce additional products and services to retain customers who are at risk of churn
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Improved Time to Market
Improved Time to Market
Explainability Office
Decision Making
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Outcome Based Solutions
We Bring the Results!
Improved Time to Market
Neuton accelerates your organization’s ability to test out business hypotheses, or to simply execute on business projects through advanced automation techniques coupled with thought leadership from our data scientists. On average, customers reduce their time to market by over 70% with Neuton.
Explainability Office
Neuton’s industry leading Explainability Office empowers customers to have unprecedented insight into their data and the key features driving your predictions. Additionally, our Explainability Office affords confidence and transparency, enabling your organization to quickly align on the “why’s” and focus on project execution and growth of the business.
Behavioral Based Decision Making
Historically, reactive rules-based decisions have been the basis behind actions taken by most businesses. Neuton’s AutoML platform enables behavioral based decisions which positions your business to be proactive, including addressing new demographics such as millennials and thin based clients.
SaaS Based Framework
Leveraging the use of advanced automation techniques, coupled with implementation of critical functions of machine learning, customers reap the ease-of use benefits of a “Zero Code” solution while getting the high-quality accuracy and outcomes that historically required highly advanced data-scientist level technical skills.
Outcome Based Solutions
Automated Machine Learning is a transformative way for customers to extract value from data. In order to maximize and achieve the most value out of Neuton, every customer is paired directly with a data scientist to ensure your business problems are solved successfully.
You Bring your Data!
We Bring the Results!
No longer is there a need to have an internal team of data scientists nor to hire consultants to leverage your data into actionable AI based solutions. From helping customers organize their data to implementation and resolution of their business problems, we got you covered!
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Neuton AutoML
Features
Differentiators
Neuton Currently Features
Neuton Core Components
Explainability Office
Problems Neuton solves
Positioned for the Future
Release Notes
Try and Buy
Start for Free
Pricing Plans
Support
FAQ
Video Tutorials
Quick Start
User Guide
Contact Us
All Things Neuton
About Us
News
Business Outcomes
Case Studies
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Webinars
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How is it Different?
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Credit Union Leverages No-Code Artificial Intelligence to Improve Customer Satisfaction

Predict Risks and Calculate ATM Withdrawal Limits without FICO Score
Number of Members Scored End-to-end Implementation Time Model Accuracy Factors Analyzed by Model Operational Cost Reduction
500,000+ 8 days 91% 40 factors 20%
What were the goals they were looking to achieve?

The executive leadership team set a goal to implement a solution that was personalized for each member, by understanding the inherent risk for each Member and growth opportunity, further leading to improved customer satisfaction by introducing a more personalized approach to each member. In order to address this and future challenges, they selected Neuton.AI, a forward-leaning AutoML solution, as their tool of choice.

Building an ML Model for Risk Rating Without Coding

By leveraging Neuton.AI, the credit union managed to successfully produce a predictive model with a 91% accuracy within a matter of days, rather than months, as it would typically take. The model analyzes more than 40 factors and characteristics around customer behaviors, such as outstanding loan balances, payment history, checking or savings account balances, non-payments, and other attributes. In contrast to FICO, which doesn’t inherently have the prerequisite insight into a customer’s behavior, Neuton was a viable answer.

The Predictive Model in Action

Once the risk rating model was trained, the customer decided to apply it to determine the acceptable withdrawal limit at the ATM. The predictive model was capable of calculating and determining a customer score ranging from 0 to 100. Based on this rating, the limit for withdrawing cash from an ATM in a given period can be easily customized for each member.

Business Outcome

Prior to developing this predictive model, the customer averaged nearly 160K customer engagements due to customers reaching their static limit of ATM withdrawals. Applying the risk rating model to each member enabled the customer to reduce the number of visits by over 20%, which is the equivalent of saving 10K man-hours.

Dynamically applying the risk rating model to ATM withdrawal limits is just one example of how this solution can be applied to the company’s business processes. It is the first step that the customer has taken to continue to position themselves as leaders within their communities and the financial services sector.

This case study has shown that large banks aren't the only ones using artificial intelligence. Credit unions can significantly profit from machine learning without having to invest in costly infrastructure.