Predictive Intelligence for Financial Institutions
Improved targeting performance
Credit card acquisition: Predictive lift of +42% for high-scoring customers compared to baseline.
Better treatment accuracy
Credit-limit increase: Lift of +24% in completion among high-scoring customers.
Prediction driven upsell performance
Additional or authorized card: Lift of +59% compared to average.
More accurate prioritization
Personal loan origination: Lift of +32% in loan take-up for high-scoring customers.
Risk stratification
Lift of + 25% on first call prediction for default, improving downstream collections performance.
Collection Prioritization
High/low risk prediction: Behavioral risk bands show clear separation with 3x improved risk prediction for collection default
Prediction improves performance
Personal credit line or overdraft: Lift of +29% in open or expand actions.
Earlier delinquency prediction
Onboarding stratification: -1.7x higher default in High vs Low risk, supporting tighter approvals.
Additional Select Case Studies
Loan Approvals - Production Impact
A specialty lender deployed VoiceSense as a behavioural validation layer in their approval workflow. Result: the overall default rate reduced by 5% after adoption, with high-risk customers showing a 33% default rate versus 17% for low-risk customers.
Customer
Specialty lender with the goal to reduce portfolio default while protecting growth
Approach
Deploy Voicesense as a behavioral validation layer in the approval workflow, maintain existing scorecards, adjust thresholds and manual review rules using risk bands
Results
Customer segmentation to High-risk default 33.2% vs Low-risk 16.6% (-2x) leading to overall default rate reduced by 5% after adoption
Collections Prioritization Accuracy
A large US collections operation used VoiceSense to classify debtors into five risk bands at first contact. Result: the highest risk group showed an 84% default rate, creating a 3x concentration of effort where recovery was most likely
Customer
Large US collections operation seeking higher recovery and operational savings
Approach
Leverage Voicesense to classify debtors into five evenly sized risk bands at first contact triggering earlier outreach and escalation based on band
Results
High risk default group presenting 83.9% default rate-creating a 3x lift that concentrates effort where recovery is likeliest and automates low-risk paths
