Pre-Delinquency Detection & Customer Support Engine

Inspiration

1. Financial systems today act only after default happens, making recovery costly and damaging customer trust.
2. We asked: Can we detect financial stress before it becomes visible?
3. Inspired by early-warning systems, we built a solution focused on prevention, not recovery.

What it does

1. Detects early financial distress 2–3 weeks before delinquency.
2. Combines financial + behavioral signals for accurate risk scoring.
3. Explains why a customer is at risk (transparent AI).
4. Recommends personalized, supportive interventions.
5. Enables human oversight for responsible decision-making.
6. Not just prediction—a decision intelligence system for prevention.

How we built it

1. Leveraged Zerve AI for end-to-end autonomous workflows.
2. Engineered behavioral features (payment cycles, spending spikes, loan stacking). Built:
3. Ensemble models (XGBoost + LightGBM) for stability
4. Attention-based DL for behavior understanding
5. Integrated SHAP explainability for transparency.
6. Developed a real-time API + interactive dashboard.

Challenges we ran into

1. Real-world data limitations → used realistic synthetic datasets.
2. Capturing behavioral dynamics, not just static data.
3. Balancing accuracy, interpretability, and fairness.
4. Designing interventions that are supportive and ethical.

Accomplishments that we're proud of

1. Built a full pipeline: detect → explain → act → learn.
2. Created a system that shifts finance from reactive to proactive.
3. Delivered a deployable, real-world-ready prototype.
4. Ensured trust, transparency, and human governance.

What we learned

1. Prediction without action has limited impact.
2. Behavioral insights unlock deeper risk understanding.
3. Trust in AI comes from explainability + human control.
4. AI-native platforms like Zerve enable rapid innovation.

What's next for Pre-Delinquency Detection & Customer Support Engine

1. Integrate real-time financial data streams.
2. Use LLMs for adaptive, personalized interventions.
3. Add continuous learning via feedback loops.
4. Deploy in real fintech environments for validation. Expand into a full financial health intelligence platform.

Built With

• aws-(s3
• dynamodb)
• fastapi
• lightgbm
• mlflow
• pandas
• plotly
• python
• pytorch
• sagemaker
• scikit-learn
• shap
• streamlit
• tensorflow
• xgboost