A US commercial lender [client name] moved from arrears-based reporting to continuous, predictive monitoring — surfacing deteriorating accounts to the credit team long before collections.
Risk was monitored in arrears, manual reviews didn't scale, and concentration risk built unseen across segments. The credit team needed to see stress forming — not react after it landed in collections.
Our AI & Data Engineering team calibrated the Early Warning accelerator to the lender's products and risk appetite, integrated the data, and stood up alerting workflows the team would actually use — built on the same explainable-AI foundations as the decisioning engine.
Continuous ML scoring of the portfolio.
Delinquency & default prediction, not arrears.
See risk building across segments.
Signals to the right owner, in time.
The credit team gained a documentable early-warning framework and acted on emerging risk quarters earlier — protecting portfolio value before accounts went bad. [Insert real metrics once available.]