Arunav Daniel

Relevant Coursework: Corporate Finance, Financial Management, Investment Analysis, Financial Markets, Accounting, Business Analytics, Statistics, Economics, Strategic Management, Business Intelligence.

Thesis: Impact of New IPOs on the Indian Stock Market — analyzed the effects of new IPOs on Indian equity-market performance, investor behavior, and market dynamics.

Awarded highest marks in Computer Science (2017); participated in debates and Model United Nations (MUNs); organized and participated in academic and extracurricular events.

• Automated M&A transaction-data workflows using Python, SQL, LLMs, APIs, and data-engineering techniques to improve transaction sourcing, enrichment, mapping, and data quality.
• Developed a CIQ M&A de-duplication model combining embeddings, fuzzy matching, target-name weighting, and configurable confidence thresholds to identify duplicate transaction records and improve M&A intelligence quality.
• Built an embeddings-based Swift Sourcing de-duplication pipeline achieving 95.23% accuracy, filtering duplicate headlines and establishing a reusable framework for additional news sources.
• Automated advisor extraction using GPT-4o Mini, increasing advisor availability for transactions with advisor data.
• Developed LLM-based transaction-attribute extraction workflows using Google Gemini and custom prompt engineering to transform unstructured M&A documents into structured JSON outputs.
• Designed and refined AI prompts for large-scale data extraction, including extraction logic, validation criteria, and edge-case handling to improve structured transaction-data quality and consistency.
• Automated deal mapping and unusual-premium monitoring with Python and database-driven workflows, enabling near-real-time detection of unmapped transactions and pricing anomalies through Microsoft Teams alerts.
• Developed an automated Preqin-to-CIQ deal-mapping solution using GPT-4o, SQL-based matching, and multi-level confidence scoring to reconcile buyers, sellers, targets, and transaction dates across platforms.
• Mentored approximately five new hires through M&A transaction workflows and trained new team members on the Ancillary Process, supporting onboarding, process quality, and independent production execution.
• Collaborated with data specialists, managers, technical teams, and senior leadership to translate complex M&A requirements into scalable AI and automation solutions and present initiatives to stakeholders.

• Supported maintenance of Capital IQ M&A Transactions and developed working knowledge across a range of public, private, and cross-border transaction types.
• Provided advisory alerts and transaction updates to support timely client access to critical M&A developments.
• Contributed to a MarketScreener M&A alerting workflow using Feedly-based sourcing, integrating international M&A announcements with downstream de-duplication and transaction-mapping processes.

• Supported customer-acquisition initiatives and assisted in expanding the client base through client outreach and engagement.
• Managed client interactions and provided portfolio-related assistance and guidance.

• Coordinated food and clothing resources for disadvantaged individuals and families and supported community outreach activities and special events.