SIMI K JOY
I am a finance professional with experience in financial reporting, IFRS-based analysis, and data-driven decision-making across global markets. My work focuses on transforming complex financial information into clear, accurate, and actionable insights that support informed business and investment decisions.
With hands-on experience working on financial data from major international exchanges, I specialize in financial statement analysis, regulatory compliance, and AI-enabled data validation. I bring a structured, analytical approach to every project, ensuring consistency, transparency, and reliability in financial reporting.
Driven by curiosity and continuous learning, I enjoy working at the intersection of finance, technology, and analytics, where precision and insight create long-term value.
Work Experience
Operations Associate - Finance
2022 - 2025
Impactsure Technologies Private Limited
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Performed multi-exchange financial analysis on 100+ financial statements per month, improving processing speed by 40% and accuracy by 35%, and enabling AI extraction accuracy of up to 85% through IFRS-aligned validation across XBRL, JSON, CSV, and Excel.
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Developed 25+ IFRS-aligned reporting outputs, increasing processing efficiency by 50% and improving accuracy by 30% with audit precision above 85%, using standardized templates and automated quality checks to support multiple stakeholder teams.
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Structured financial datasets from 100+ statements, improving AI extraction accuracy by 30% and model speed by 25%, and ensuring consistent automated reporting through standardized JSON, XBRL, CSV, and Excel inputs.
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Analyzed 100+ financial statements monthly to identify profitability, liquidity, and cash-flow irregularities, improving AI interpretation accuracy by 25% and reducing correction cycles through reliable model training data.
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Conducted CMA-aligned compliance validation, achieving 70%+ error-free submissions, reducing audit corrections, and strengthening regulatory trust across SGX, JSE, Tadawul, and ASX by reconciling structured data with original IFRS statements.
Civil Engineer
2019 - 2020
Manthralaya Constructions
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Conducted feasibility studies and cost analyses for 15 construction projects, improving forecasting accuracy by 25% and achieving 15% cost savings through accurate material estimation, structured budgeting, and financial planning.
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Prepared and revised 50 structural drawings, reducing design errors by 30% and shortening approval timelines by 20% using AutoCAD, Primavera, and IS code–compliant drafting with quality assurance checks.
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Ensured compliance with safety and regulatory standards across multiple audits, achieving 100% pass rates with zero incidents and improving project approval efficiency through effective risk controls.
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Managed construction budgets of ₹2–3 crore, delivering projects 12% under budget and 15% ahead of schedule by coordinating vendors and optimizing procurement activities.
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Supported procurement operations by benchmarking quotations, verifying specifications, and validating quantities, securing cost-effective pricing while maintaining quality and budget control.
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Prepared structured project and financial reports, enabling early variance identification and supporting informed decisions on resource allocation, scheduling, and cost management.
Education
Academic Qualifications
Master of Business Administration (MBA) in Finance and Human Resources
University of Kerala, 2022
Specialized in financial analysis, corporate finance, financial reporting, and risk management
Bachelor of Technology (B.Tech) in Civil Engineering
Mahatma Gandhi University, 2018
Specialized in engineering design, project planning, cost estimation, and quantitative analysis
NOTABLE ACHIEVEMENTS
IFRS-Based Financial Data Extraction & Validation (Global Exchanges)
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I worked on IFRS-based financial data extraction and validation projects across major global stock exchanges, including the Singapore Exchange (SGX), Johannesburg Stock Exchange (JSE), Saudi Stock Exchange (Tadawul), and Australian Securities Exchange (ASX). My work centered on analyzing complex financial statements and disclosures to ensure that financial data was accurately extracted, standardized, and aligned with IFRS requirements for use in automated reporting and AI-driven analytics systems.
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A key aspect of my role involved conducting thorough financial statement analysis, covering income statements, balance sheets, cash flow statements, and detailed notes. This analysis ensured that extracted data reflected the correct financial meaning rather than relying solely on document layouts or fixed positions. By combining financial expertise with structured validation techniques, I supported the creation of reliable, automation-ready financial datasets.
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For SGX-listed companies, I built IFRS-aligned AI training datasets by extracting up to 60 key financial parameters per company from annual reports. I validated AI-generated outputs by reconciling extracted values against source disclosures, improving classification and extraction accuracy by 30% and reducing manual processing by 50%. These improvements were achieved through standardized formatting, contextual validation, and noise-reduction techniques, enabling more efficient and accurate AI-based financial data processing.
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Across JSE, Tadawul, and ASX, I focused on improving data consistency, reporting accuracy, and processing efficiency through structured financial analysis and IFRS mapping. At JSE, I standardized extraction across 30 core financial tables, applying multi-year cross-validation and consistent formatting rules to reduce validation time by 45% and errors by 40%. For Tadawul, I developed 25+ IFRS-compliant financial outputs within a six- to eight-week timeframe, accelerating reporting cycles by 50% and achieving error-free validation using structured templates and automated quality checks.
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At the Australian Securities Exchange (ASX), I processed 120+ financial statements, ensuring consistency across reporting periods and disclosures. Through clean dataset preparation and IFRS-aligned structuring, I improved data consistency to 80%, increased extraction accuracy by 25%, and enabled a 40% improvement in automated reporting speed. This work supported scalable financial analytics and strengthened the reliability of downstream reporting systems.