PREDICT FUTURE TRENDS
Predictive Analytics
Capitalize on future trends and respond proactively to challenges by predicting future outcomes using the power of big data and machine learning.
Look into your credit union's future
- Quantify future scenarios
- Personalize member outreach
- Optimize marketing spending
- See beyond predictions
Leverage the power of the best Artificial Intelligence (AI) and Machine Learning (ML) platforms to support the development of ML models that meet real credit union business use cases.
The implementation process takes 2 – 4 months from project start to finish depending on configuration options.
Predictive Analytics overview
Data Sources
Model Input Data
Machine Learning
Targeted Actions
Explore pre-developed and credit union-validated predictive models
From model creation and training to operating your models at scale, we’re with you for your entire predictive analytics journey.
Member Segmentation AI
Predictive Model Type:
Cluster group into segments
Forecast numeric values
Gain a deeper understanding of members through assessment of the level of engagement and overall activity within the credit union’s channels.
Classify each member into one persona group to drive specific marketing actions and boost the efficiency of your marketing campaigns.
- How engaged are our members?
- What members should be targeted for a specific campaign?
Member Retention AI
Predictive Model Type:
Classify predict binary outcomes
Enable your credit union to identify members that are at risk of leaving based on actual members’ activities.
Take a targeted approach and work with “high risk” member groups to reengage and retain them, or to identify those members who would better respond to marketing campaigns.
- What is member churn?
Next Best Product AI
Predictive Model Type:
Time series predictive for the next ‘n’ days
Identify the next two most beneficial products for every member at a credit union based on historical behaviors.
Use data to drive a better match between your members’ needs and your credit union product offerings, driving a quantifiable increase in member satisfaction, engagement and revenue.
- What products should be recommended for a member within the next 6 months?
Member Lifetime Value AI
Predictive Model Type:
Forecast numeric value
Determine members’ value for a credit union and make insightful decisions to drive credit union growth by targeting the most valuable members while minimizing expenses, such as marketing or retention, on unprofitable members.
- How much value does a member bring to the credit union?
Member Risk Score AI
Predictive Model Type:
Time series predictive for the next ‘n’ days
Identify members who are likely to become delinquent by detecting early signals so the credit union can assess and analyze distressed situations ahead of time and take proactive actions, therefore avoiding delinquency management and collections costs.
- Identifies members likely to be 30+ days delinquent in the next 12 months.
Next Best Action AI
Predictive Model Type:
Decision trees decision branches and actions
Prescribe actions that could be implemented for a specific member and then choose the best viable option to implement them.
Use demographic and behavioral data to recommend actions to increase product penetration and promote services.
- How do we customize content and create relevant offers at the right time?
Ready to see how Trellance Predictive Analytics can shape the future of your business? Contact us to get started.
Why choose us?
The Trellance Difference
Credit Union Expertise
We have developed credit union specific models and we keep innovating based on our client’s feedback.
Resident Data Scientists
We have a resident data science team with deep knowledge of credit union systems available for predictive model customization.
AI Driven Technology
We use a spectrum of innovative and scalable artificial intelligence platforms to harness your data and detect intrinsic data patterns.
What credit unions are saying...
“There are 10,000 different formulas for calculating customer value,” said Harrison. “But credit unions face a lot more volatility in terms of rate and market swings. We needed a model built by data scientists with a strong understanding of our environment.”
Jamie Harrison
SVP/Chief Growth Officer
Meritrust Credit Union
