Page 613 - AI for Good Innovate for Impact

AI for Good Innovate for Impact

Docker and Kubernetes, with offline inference support in places with poor internet connectivity.
Representational State Transfer Application Programming Interfaces(RESTful APIs) and mobile-
friendly lightweight interfaces will be created to enable seamless integration into small-lender
setups. 4.6: Finance

These will include such metrics as enhanced access to credit, better repayment rates, and
borrower livelihood shifts (e.g., farm output or business growth). Threshold logic will be
regularly updated with real-time economic feedback loops so that the model remains adaptive
and equitable.

Lastly, as part of our research objectives, we plan to publish subsequent research highlighting
the rural implementation of our model and put it out for peer review. An open-source tool
kit consisting of datasets, threshold optimization examples, and region-specific configuration
scripts will be published to facilitate wider adoption and community innovation in this area.

3 Use Case Requirements

• REQ-01: Access to Loan History Data

Secure access to 5–10 years' anonymized loan application and payment data. They should
have numeric fields (e.g., income, amount borrowed, number of days past due), categorical
attributes (e.g., job type, education level, type of housing), and time-series (e.g., monthly
repayment habits, credit balances over time) to enable comprehensive borrower attribute
engineering.

• REQ-02: Data Preprocessing Pipeline

Automated Extract, Transform, Load(ETL) pipelines for filling missing values, normalization/
scaling, categorical encoding, and creating derived features (e.g., debt-to-income ratio).

• REQ-03: Computational Infrastructure

Elastic CPU/GPU resources (cloud VMs or on-prem servers) capable of supporting LightGBM
training, MCMC sampling (≥1,000 iterations), and Hyperopt searches in a reasonable timeframe.
• REQ-04: MCMC and Bayesian Optimization frameworks

Installed packages for Markov Chain Monte Carlo (such as Numerical Python(NumPy) +
Scientific Python(SciPy) or Python Monte Carlo (Version 3)(PyMC3)) and Hyperopt (or equivalent
Bayesian optimizer), having same APIs for auto-tuning hyperparameters.
• REQ-05: Integration API

A REST endpoint or batch interface that accepts data from fresh applicants, uses the trained
model and dynamic threshold reasoning, and returns immediate "accept/reject" decisions.

• REQ-06: Monitoring & Logging

Ongoing performance monitoring (ROC-AUC, profit scores), input distribution drift detection,
and threshold changes noted for auditing and retraining.

• REQ-07: Compliance and Fairness Testing

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