PHOENIX, AZ (PinionNewswire) — The intersection of artificial intelligence and banking operations has found a formidable architect in Thirupurasundari Chandrasekaran, Senior Project Manager at Citizens Bank, whose latest research publications address two critical challenges facing modern financial institutions: the need for explainable AI in banking decisions and the transformation of traditional project management into predictive, compliance-driven operations.
Thirupurasundari’s dual contributions, “Neurosymbolic AI: Bridging Neural Networks and Symbolic Reasoning” and “The AI-Augmented PMO: A 2025 Framework for Predictive Oversight, Regulatory Compliance, and Enterprise Value Delivery in Banking,” represent a comprehensive vision for how financial institutions can harness AI while maintaining the transparency, compliance, and reliability that banking demands.
From Theory to Banking Reality: The Neurosymbolic Revolution
In her first research paper, Chandrasekaran tackles a fundamental challenge that has long plagued AI adoption in banking: the black-box nature of neural networks versus the explainability requirements of financial regulations. Her neurosymbolic framework offers a solution that Citizens Bank and other financial institutions have been seeking AI that can both learn from vast transaction datasets and provide logical, auditable explanations for every decision.
“Banking operates in a unique environment where every automated decision must be defensible to regulators, auditors, and customers,” Chandrasekaran explains. “Traditional AI excels at pattern recognition but fails at explanation. Symbolic reasoning provides logic but lacks learning capability. Our framework delivers both.”
The dual-layer architecture she developed integrates neural networks for pattern detection in fraud prevention and risk assessment with symbolic reasoning for regulatory compliance and decision documentation. This approach directly addresses requirements under regulations like the Fair Credit Reporting Act and Equal Credit Opportunity Act, which mandate that financial institutions explain adverse credit decisions.
Her experimental evaluations demonstrate the framework’s effectiveness across multiple banking applications:
- Credit decisioning with full explainability paths
- Fraud detection that can articulate suspicious pattern logic
- Customer service automation that combines learned preferences with policy rules
- Risk assessment that maintains regulatory compliance while adapting to new threats
The framework shows superior accuracy, generalization across unseen tasks, and robustness against adversarial attacks critical capabilities for financial systems where a single vulnerability could expose millions of customers.
Transforming the Banking PMO Through Predictive Intelligence
Thirupurasundari’s second publication addresses an equally pressing challenge: modernizing how banks manage their massive transformation portfolios. Drawing from her role orchestrating enterprise-wide initiatives at Citizens Bank, where she has reduced development silos by 80% and accelerated integrated feature delivery, her research presents empirical evidence from 28 multinational banks over five years.
The AI-Augmented PMO Framework she introduces represents a radical departure from traditional project management. Instead of reactive status reporting, the framework enables:
- 41% reduction in delivery delays through predictive risk identification
- 52% fewer high-severity risks via automated pattern recognition
- 63% reduction in regulatory audit findings through continuous compliance monitoring
- 37% improvement in enterprise value contribution from optimized resource allocation
“Modern banking transformation involves hundreds of interdependent projects, each with regulatory implications,” Chandrasekaran notes. “Traditional PMOs simply cannot process the complexity. AI augmentation isn’t optional, it’s essential for survival.”
Her framework integrates predictive analytics for identifying delivery risks before they materialize, automated compliance checking against evolving regulations, resource optimization across competing priorities, and strategic alignment scoring for portfolio decisions.
Real-World Impact at Citizens Bank
Thirupurasundari’s research isn’t theoretical, it’s grounded in her extensive experience leading digital transformation at Citizens Bank. As Senior Project Manager, she has orchestrated the bank’s transition to cloud-native platforms, implemented real-time payment systems, and driven API ecosystem development while maintaining strict compliance with KYC and AML requirements.
Her expertise in platform modernization initiatives, including CI/CD automation and DevOps integration, directly informs both research papers. The neurosymbolic framework addresses challenges she encountered integrating AI into customer-facing products while maintaining regulatory compliance. The PMO framework codifies lessons learned from managing complex, interdependent banking transformation programs.
“Thirupurasundari brings a unique perspective combining deep technical knowledge with practical banking experience,” notes a senior technology executive familiar with her work. “Her research solves problems that keep banking CEOs awake at night.”
Industry-Wide Implications
The timing of Thirupurasundari’s research is particularly significant as banks face unprecedented challenges:
Regulatory Complexity: With new regulations emerging monthly, banks need AI systems that can adapt while maintaining compliance. Her neurosymbolic approach provides the explainability regulators demand.
Digital Competition: Fintech disruption requires banks to innovate rapidly while maintaining stability. Her PMO framework enables faster delivery without sacrificing governance.
Risk Management: Evolving fraud patterns and cyber threats demand intelligent response systems. Her dual-layer AI architecture provides both learning and reasoning capabilities.
Customer Expectations: Modern customers expect personalized, instant service with bank-level security. Her frameworks enable this balance.
Breaking Down the Technical Innovation
Neurosymbolic Architecture for Banking
Thirupurasundari’s neurosymbolic framework employs a sophisticated dual-layer design specifically tailored for financial services:
The Neural Layer processes unstructured data transaction patterns, customer behaviors, market signals extracting features that traditional rule-based systems miss. This layer continuously learns from new data, adapting to emerging fraud patterns and changing customer preferences.
The Symbolic Layer encodes banking regulations, compliance rules, and business policies into logical structures. This ensures every decision can be traced through a clear reasoning chain, satisfying regulatory requirements for explainability.
A Dynamic Integration Mechanism enables bidirectional communication between layers. Neural insights inform symbolic reasoning, while symbolic constraints guide neural learning. This creates a system that’s both adaptive and compliant, a combination previously thought impossible in banking AI.
The Predictive PMO Revolution
Her PMO framework introduces four integrated components that transform project management:
Predictive Risk Engine: Analyzes historical project data to identify early warning signals, enabling intervention before issues escalate.
Compliance Automation: Continuously monitors project outputs against regulatory requirements, flagging potential violations before they occur.
Resource Optimization: Uses machine learning to allocate resources across portfolios, maximizing value delivery while minimizing conflicts.
Strategic Alignment Scoring: Evaluates each project’s contribution to enterprise goals, enabling data-driven portfolio decisions.
Validation Through Rigorous Research
What sets Thirupurasundari’s work apart is its empirical foundation. The PMO research analyzes five years of data from 28 multinational banks, providing statistically significant evidence of AI’s impact on project delivery. The neurosymbolic research includes experimental evaluations across multiple banking use cases, demonstrating practical applicability.
Her research methodology combines:
- Quantitative analysis of performance metrics
- Qualitative assessment of organizational impact
- Comparative studies against traditional approaches
- Real-world validation through banking implementations
This comprehensive approach ensures her frameworks aren’t just theoretically sound but practically viable for immediate banking adoption.
Recognition from the Banking Community
Financial technology leaders have responded enthusiastically to Thirupurasundari’s research. Several major banks have initiated pilot programs based on her frameworks, while regulatory bodies have expressed interest in her approach to explainable AI.
“This research addresses the exact challenges we face,” comments a Chief Risk Officer at a top-10 U.S. bank. “The neurosymbolic framework could revolutionize how we approach AI governance.”
Academic institutions are incorporating her work into fintech curricula, recognizing its significance for next-generation banking professionals. Professional organizations have invited Chandrasekaran to present her findings at upcoming conferences, anticipating strong industry interest.
The Broader Vision for Banking Transformation
Thirupurasundari’s dual research contributions reflect a comprehensive vision for banking’s AI-enabled future. Her work demonstrates that banks can embrace artificial intelligence without sacrificing the trust, compliance, and reliability that define financial services.
The neurosymbolic framework enables banks to deploy AI in customer-facing applications while maintaining complete explainability. The PMO framework ensures transformation programs deliver value while managing risk and compliance. Together, they provide a blueprint for banks navigating digital transformation.
Her research also addresses ethical considerations increasingly important in banking AI:
- Bias mitigation through transparent reasoning chains
- Fairness assurance via symbolic rule enforcement
- Privacy preservation through controlled data usage
- Accountability maintenance via decision audit trails
Implementation Roadmap for Financial Institutions
Based on her research and experience at Citizens Bank, Chandrasekaran outlines a practical adoption path:
Phase 1: Foundation Establish data governance and regulatory mapping Phase 2: Pilot Deploy neurosymbolic AI in low-risk use cases Phase 3: Integration Implement PMO framework for transformation programs Phase 4: Scaling Expand AI adoption across enterprise functions Phase 5: Optimization Continuously improve based on performance data
This phased approach minimizes risk while maximizing value realization, enabling banks to transform gradually rather than disruptively.
Future Research Directions
During recent presentations, Chandrasekaran outlined emerging areas for investigation:
- Quantum-resistant cryptography for future-proof banking security
- Federated learning for multi-bank fraud detection without data sharing
- Autonomous compliance systems that adapt to regulatory changes automatically
- Predictive customer experience platforms that anticipate needs before expression
These directions suggest continued innovation at the intersection of AI and banking, with Chandrasekaran positioned as a thought leader in this evolution.
The Professional Behind the Research
Thirupurasundari Chandrasekaran brings unique qualifications to her research. As Senior Project Manager at Citizens Bank, she has:
- Led strategy, development, and scaling of cloud-native digital platforms
- Orchestrated enterprise-wide product alignment across multiple business units
- Collaborated with compliance, legal, and risk teams on regulatory adherence
- Improved customer satisfaction through comprehensive gap analysis and competitive research
- Managed the bank’s transition to microservices architecture and DevOps practices
Her planned initiatives in predictive analytics for fraud prevention, coordinating with security and compliance teams to implement AI models, directly build upon her research foundations. This combination of theoretical innovation and practical implementation distinguishes her contributions to the field.
Impact on the Global Banking Landscape
Thirupurasundari’s research has implications beyond individual institutions. As banks worldwide grapple with digital transformation, her frameworks provide standardizable approaches that could reshape the industry:
Regulatory Harmonization: Explainable AI frameworks that satisfy multiple jurisdictions simultaneously Industry Collaboration: Shared PMO practices that enable cross-bank initiatives Risk Reduction: Systematic approaches to managing transformation complexity Innovation Acceleration: Faster adoption of emerging technologies with maintained compliance
International banking associations have begun discussing her frameworks as potential industry standards, recognizing their value for systematic transformation.
Conclusion: Defining Banking’s AI Future
Thirupurasundari Chandrasekaran’s dual research contributions represent more than academic achievements; they provide practical solutions to banking’s most pressing challenges. Her neurosymbolic framework enables AI adoption without sacrificing explainability. Her PMO framework transforms project management into predictive value delivery.
As financial services continue their digital evolution, Thirupurasundari’s work offers both theoretical foundation and practical guidance. Her unique position as a practicing banking executive who conducts cutting-edge research ensures her contributions address real-world needs while advancing the field’s knowledge frontier.
For banks seeking to harness AI’s potential while maintaining trust and compliance, Thirupurasundari’s research provides the roadmap. Her frameworks don’t just solve today’s problems, they anticipate tomorrow’s challenges, positioning adopters for sustained competitive advantage in an AI-driven financial future.
The banking industry stands at an inflection point where traditional approaches no longer suffice. Through her research and professional leadership, Thirupurasundari Chandrasekaran is helping define what comes next, a future where artificial intelligence enhances rather than replaces human judgment, where innovation proceeds without sacrificing stability, and where banks can transform confidently knowing they have frameworks to guide their journey.
For more information about implementing AI frameworks in banking and financial services transformation, interested parties can access Thirupurasundari’s complete research papers through academic publishing channels.
