Ajay Mirani

From Batch Bottlenecks to AI-Native Finance: Scaling Financial Reconciliation to Millions of Transactions with Predictive Intelligence

Abstract:

Financial reconciliation is a critical yet resource-intensive process in enterprise finance, especially in environments processing millions of transactions per month across multiple interconnected systems. Traditional batch-based approaches generate thousands of daily exceptions and significantly extend financial close timelines due to manual investigation and delayed detection of discrepancies.
This session introduces the Financial Reconciliation and Transaction Intelligence System (FRTIS) an AI-native architectural framework composed of six core patterns that transform reconciliation into a continuous, intelligent process. The approach incorporates real-time event-driven matching, machine learning–based exception classification using Isolation Forest and BERT, adaptive tolerance management, and predictive break prevention powered by XGBoost and Monte Carlo simulation techniques.
A high-volume billing reconciliation scenario processing 1.2 million transactions per month across five integrated systems illustrates the impact of this architecture. The system enables automated handling of a large portion of routine exceptions, reduces manual intervention, and improves resolution prioritization. Close cycle timelines are shortened from multiple days of reconciliation effort to a significantly reduced timeframe, while audit evidence generation is accelerated from weeks of manual preparation to a few hours using an automated audit trail.
The framework also introduces proactive capabilities, where predictive models identify high-risk reconciliation issues ahead of financial close cycles, enabling early intervention and improved operational stability.
Attendees will gain practical insights into designing scalable, AI-native reconciliation systems that enhance data integrity, improve operational efficiency, and enable audit-ready financial processes at enterprise scale.

Profile:

Ajay Mirani is a senior engineering leader with over 20 years of experience in architecting, modernizing, and stabilizing enterprise-scale platforms across finance, taxation, accounting, manufacturing, and ERP ecosystems. He has a strong track record of leading mission-critical transformations, regulatory-compliant migrations, and large-scale system modernization initiatives.
Currently a Senior Staff Software Engineer at Tesla, Ajay is recognized for his architectural judgment, risk mitigation strategies, and ability to deliver high-impact outcomes with lean, high-performing teams in complex environments. His expertise spans enterprise architecture, microservices, cloud platforms, and financial systems, with hands-on proficiency in technologies including C#, .NET, Java, Python, Kafka, and containerized CI/CD pipelines.
Ajay has led major initiatives such as enterprise tax engine transformations, ERP modernization programs, and microservices-based order-to-cash platforms integrating sales, fulfillment, billing, and CRM systems. He has also played a key role in stabilizing critical finance platforms, improving system reliability, and ensuring compliance with SOX and regulatory requirements.
He holds a Master of Computer Applications from Saurashtra University and brings deep experience in delivering scalable, compliant, and high-performance enterprise solutions.