An agentic Ecosystem for Financial Services
Pinakyne's AI agents are the next-generation workforce, transforming finance, healthcare, and Airline Industry by automating customer support, transactions, medical processing, and operations with efficiency and intelligence.
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API Integration Ecosystem
Intelligent data flow orchestration
External Data Sources
CRMs, ERPs, Databases
Intelligent API Gateway
Authentication, Rate Limiting, Routing
AI Processing Layer
Data Transformation, Enrichment
Business Applications
Insights Delivery, Automated Actions
Supported Protocols
All ActiveTalk to AI Voice Agents
Sales, Debt Collection, and Customer Support agents are ready to help you.
AI Credit initiation & control
Self-learning creditpolicy optimization system
+15 points+5% from baselineWithin targetDocument scanning and retrieval
With our blazing fast, state of the art, cutting edge, we are so back cloud servies (read AWS) - you can deploy your model in seconds.
Credit Risk Management Policy
SECTION 1: GENERAL PROVISIONS
1.1 This Credit Risk Management Policy ("Policy") establishes the framework for evaluating and managing credit risk across all lending operations. The Policy aims to maintain a high-quality credit portfolio while ensuring compliance with regulatory requirements and risk appetite limits.
SECTION 2: CREDIT ASSESSMENT CRITERIA
2.1 All credit applications must undergo comprehensive evaluation based on the following criteria:
- Credit History Assessment
- - Minimum credit score requirements
- - Payment delinquency analysis
- - Length of credit history
- Financial Capacity Evaluation
- - Income verification and stability
- - Debt service coverage ratio
- - Asset and liability assessment
SECTION 3: RISK MITIGATION
3.1 The following risk mitigation measures must be implemented:
- Portfolio diversification across sectors
- Regular stress testing and scenario analysis
- Continuous monitoring of credit quality indicators
SECTION 4: REGULATORY COMPLIANCE
4.1 All lending activities must comply with applicable regulations, including but not limited to:
- Capital adequacy requirements
- Risk-based pricing guidelines
- Fair lending practices
AI-Powered Risk Analysis Report
EXECUTIVE SUMMARY
The AI system has analyzed the credit policy framework and identified optimization opportunities based on historical performance data and current market conditions.
KEY FINDINGS
- Policy Effectiveness Analysis:
- - Current approval rate: 82% (Target: 75-85%)
- - Default rate: 2.1% (Below industry average)
- - Risk-adjusted return: 14.3% (Above target)
- Market Context Integration:
- - Sector-specific risk trends identified
- - Macroeconomic indicators incorporated
- - Competitive landscape analysis
- Recommended Policy Adjustments:
- - Increase DTI threshold by 2% for high-score segments
- - Implement dynamic pricing based on risk profile
- - Enhanced early warning system for portfolio monitoring
IMPLEMENTATION STRATEGY
The proposed policy adjustments should be implemented in phases:
- Phase 1: System updates and staff training
- Phase 2: Pilot program for selected segments
- Phase 3: Full rollout with monitoring framework
RISK CONSIDERATIONS
The AI system continuously monitors and adjusts for:
- Market volatility impacts
- Portfolio concentration risks
- Regulatory compliance requirements
Our Features at a Glance
Autonomous systems incorporating proactive decision making.
AI reasoning for complex workflows
Pinakyne's AI reasoning engine allows you to create complex workflows that can be used to automate your business.
AI reasoning for complex workflows
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Extract, analyze, and process data from various sources with high accuracy.
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Agent based workflows to help you with your business.
Highly Custimizable
Pick your LLM, be it opensource or proprietary, we support all of them.
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Groq LLM
23rd March
Llama 3
21st March
OpenAI GPT-4o
3rd May
DeepSeek R1
1st April
Claude 200k
2nd June
Continuous Learning
Agents observe, learn, and act based on feedback loops, constantly adapting to shape their behavior for future interactions.
AI Learning Progress
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Meet Our Team
Aditi Chatterji
Founder & CEO
Aditi is an AI and deep learning expert with a strong background in Gen AI and fintech, bringing 10 years of experience across global markets. She has worked with KPMG, HSBC, Wizely Inc., and MortgageKart, developing AI-driven solutions for risk assessment, deep reinforcement learning, and scalable AI architectures. An IISc alumna, DAAD scholar, and Stanford LEAD participant, she is passionate about transforming financial ecosystems with AI.
Bhavna Taneja
Co-founder & CMO
Bhavna is a Finance AI Transformation & Digital Product Leader with 18+ years of experience in AI-driven finance, digital journeys, and product innovation. At Google, she advised leading banks (Stanchart, DBS, UOB, Bank Mandiri) and fintech firms (GCash, Maya, SEA Money), driving AI-powered growth and best practices. She spearheaded finance AI transformation, developed AI maturity frameworks, and published industry-leading playbooks. Bhavna has also designed digital financial products.
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