Page 620 - AI for Good Innovate for Impact

AI for Good Innovate for Impact

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Item Details
Model Train- Model Training & Fine Tuning are very important sections, so we opt for a full
ing and description below:
Fine-Tuning SERA’s AI engine is designed to optimize financial outcomes for users by
supporting debt reduction, spending management, and long-term financial
stability. Our model training and fine-tuning process includes the following
key stages:
1. Data Collection & Preprocessing
Source Data: Aggregated, anonymized user financial data including spending
patterns, income flows, debt levels, credit card activity, and budget adherence.
External Data: Public economic indicators (e.g., inflation rates, consumer spend-
ing trends) and behavioral finance research.
Privacy & Compliance: All data handling complies with strict standards like
GDPR, CCPA, and U.S. financial privacy laws (GLBA, FCRA), ensuring user
anonymity and security. It will be localized for any specific country deployment.
2. Model Pretraining
Initial models are pretrained using general financial behavior datasets based
on known public financial offerings as well as our financial tools (Debt Offset,
Smart Budget)
Focus areas include identifying risk patterns, spending leaks, budget compli-
ance behavior, and debt repayment trends.
3. Supervised Fine-Tuning on SERA-Specific Objectives
We fine-tune the models using SERA’s proprietary dataset collected from early
users and simulated financial scenarios.
Custom loss functions prioritize outcomes like debt offset optimization, budget
discipline enforcement, and predictive cash flow management.
4. Reinforcement Learning with Human Feedback (RLHF)
Peer users including special users such as Financial coaches, economists, and
behavioral scientists manually review and guide model outputs to improve
advice relevance, user personalization, and decision-making support.
This feedback loop ensures the AI continually learns to provide practical, action-
able, and empathetic recommendations.
5. Continuous Personalization
Once deployed, each user’s interaction further trains the AI engine in real-time
through adaptive learning.
The system dynamically adjusts its guidance based on individual user progress,
setbacks, lifestyle changes, and financial emergencies.
6. Bias Mitigation and Risk Monitoring
Regular audits are conducted to monitor for model bias, especially to prevent
systemic disadvantages based on income, background, or geography.
Robust fallback strategies are designed to prevent poor advice during periods
of economic volatility.
7. Model Evolution for Future Expansion
Beyond Debt Offset, SERA’s AI is being expanded to address savings growth,
investment starter strategies, and microbusiness cash flow management, creat-
ing an end-to-end AI-powered financial wellness ecosystem.

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