Virtusa Jatayu Season 4 - Dynamic Pricing of Financial Products

📌 Problem Statement

Banks currently use fixed slab-based interest rates for Fixed Deposits (FDs) and Home Loans, which lack flexibility and competitiveness. The challenge is to introduce AI-driven dynamic pricing that adjusts interest rates based on customer profiles, enhancing customer retention and optimizing risk-based pricing.

🚀 Business Need & Opportunity

🔹 Unmet Need & Opportunity:

• AI-driven dynamic interest rates tailored to customer profiles.
• Enhanced customer retention and competitive positioning.
• Optimized risk-based pricing for better customer acquisition.

🔹 Importance & Value:

• Personalized interest rates based on Credit History, Risk Rating, Age, Investment Patterns, and Market Trends.
• Better pricing for low-risk customers while minimizing exposure to high-risk clients.

🎯 Target Segment & Market Size

👥 Intended Customers:

• Retail banking customers (new & existing) looking for Fixed Deposits and Home Loans.

📈 Market Size & Growth:

• Increased competition in banking due to digital transformation.
• Personalization in financial services influences customer choices.

🎯 Expected Outcome:

• Dynamic pricing with 10–100 bps discount for eligible customers.
• Risk mitigation by avoiding high-risk loans.
• Competitive advantage while maintaining profitability.

💡 Solution Idea

A Machine Learning-based model that dynamically suggests interest rates for Fixed Deposits and Home Loans by analyzing multiple customer parameters, improving competitiveness and customer retention.

🔥 Key Differentiators (USP):

✅ Personalized Pricing – Interest rates tailored based on credit history, risk rating, and financial behavior. ✅ Cost Efficiency – Optimized risk-based pricing minimizes bad loans and increases profit margins. ✅ Scalability – Easily expandable to other financial products (e.g., car loans, personal loans). ✅ Fast Time-to-Market – Deployable as an API for easy integration with banking systems.

🎯 Key Features & Unique Benefits

• AI-Driven Interest Rate Adjustments – Automated pricing based on real-time market trends.
• Customer Segmentation & Risk Assessment – Identifies low-risk customers for better offers.
• Bank Profit Optimization – Avoids high-risk loans while offering competitive rates.

🛠️ PoC & Demo

1️⃣ Data Collection – Gather customer profile, credit score, and investment history. 2️⃣ Risk Analysis – The model processes risk factors & market trends. 3️⃣ Dynamic Interest Rate Generation – Personalized rates are calculated. 4️⃣ Customer Offer – The customer receives an optimized rate offer.

📅 Roadmap & Market Launch

• Phase 1 – Develop & train AI model (3 months).
• Phase 2 – PoC deployment in a pilot region (6 months).
• Phase 3 – Full-scale bank-wide rollout (12 months).

🌍 Cross-Industry Applicability

💰 Insurance – Dynamic premium pricing based on risk analysis. 💳 Retail Lending – Personalized credit card interest rates. 🏥 Healthcare – Tailored health insurance pricing.

👨‍💻 Team Members

• A. Ethnic Sai (245321748023)
• S. GunaSekhar (245321748028)
• Naga Prajwalith (245321748041)
• Sri Chandra Vardhan (245321748057)
• T. Vishnu Vandhan (245321748058)

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