Conventional forensic accounting approaches are generally retrospective in nature, spotting fraud only after financial consequences have taken place, and subsequently altering records to mask damages. With transactions becoming quicker and digital financial marketplaces becoming more prevalent, these methods will fail to provide timely tax fraud detection. In addition, these methods have not sufficiently explored an AI enabled anomaly detection within a globally compliant, ethically approved environment.
Build a forensics accounting framework within AI that can produce anomaly detection to identify anomalous financial activity in real time
Develop fraud detection algorithms that can leverage advanced statistical pattern detection and transaction behaviour modeling.
Develop the fraud detection methodology that shifts from a historic perspective to comprehension, predictive, and prevention based on anomaly detection.
Tackle the ethical risk and regulatory risk of a real-time artificial intelligence observer in financial data.
Compare fraud trends and reporting protocols in regions (i.e., EU AML obligations, U.S. SEC alerts and Indian RBI notifications).
Data Sources:
Using publicly available datasets such as:
Kaggle’s Credit Card Fraud Detection Dataset
IEEE-CIS Fraud Detection Dataset
Simulated/Anonymized bank transaction logs for academic modeling
Modeling Techniques:
Apply unsupervised algorithms such as Isolation Forest, Autoencoders, and One-Class SVM to detect anomalies in data.
Analyze temporal patterns in transactional data through time-series clustering.
Evaluating metrics:
Investigate the accuracy, precision, recall and false alerts
Per the previous item, use cost-sensitive metrics for avoiding losses
Global Benchmarking:
Connect models to regulators benchmarks (EU Anti-Money Laundering (AML) Directives, SEC alerts (US), FATF guidelines).
Forensic Accounting Models
Leverage AI techniques like pattern recognition, deep learning, and anomaly detection for identifying financial fraud.
Anomaly Detection Models – statistical and machine learning based
Ethics of AI Surveillance – transparency, fairness, and data privacy for financial systems.
Kranacher, M.-J., Riley, R. A., & Wells, J. T. (2022). Forensic Accounting and Fraud Examination (5th ed.). John Wiley & Sons.
West, J., & Bhattacharya, M. (2016). A comprehensive review of intelligent methods for detecting financial fraud. Expert Systems with Applications, 55, 1–19.
Rezaee, Z. (2002). Financial Statement Fraud: Prevention and Detection. Wiley.
Although ESG (Environmental, Social, and Governance) disclosures were recently mandated by regulators, gaps in reporting standards and practices remain across much of the developing world. In countries like India, Brazil, and South Africa, the ESG data is incomplete and sector-dependent (including voluntary frameworks), making it difficult to measure its financial implications and attractiveness to global investors.
Compare the ESG disclosure practices between key emerging markets—India, Brazil, and South Africa—by sector (e.g., energy, manufacturing, IT).
Examine the relationship between ESG scores and firm financial performance measures (e.g., ROE, ROA, market returns).
Analyze investor sentiment and valuations of ESG firms using media, analysts, and market signals.
Review regulatory frameworks and disclosure convergence (e.g., GRI vs SASB, SEBI vs CVM vs FSCA).
Data Extraction: Obtain ESG scores and metrics from global databases: MSCI ESG Ratings, Bloomberg ESG, Refinitiv ESG, annual reports
Financial Metrics: Utilize financial indicators such as ROE, ROA, EPS, and fluctuations in stock prices to assess performance.
Statistical
Methodologies: Panel regression for multi-country comparison
Sentiment analysis related to investor communications (e.g., earnings calls, news sentiment)
Sector Analysis: Evaluate the quality of ESG disclosures by categorizing them according to industry sectors and national contexts.
Regulatory Mapping: Compare ESG disclosure requirements between national and global requirements (e.g., GRI, SASB, TCFD, SEBI-BRSR)
Triple Bottom Line Theory (Elkington) – where profit, people and planet are balanced
Stakeholder Theory – aligning responsible disclosure with stakeholder trust and financial return
Institutional Theory – evaluating the parameters of national regulatory institutions on ESG adoption
Sustainability Reporting Standards (GRI, SASB, IFRS-S) – for a comparative framework alignment
Khan, M., Singh, A., & Verma, R. (2022). Exploring the relationship between ESG disclosures and corporate financial outcomes in emerging economies. Journal of Sustainable Finance and Investment, 12(3), 211–228.
Elkington, J. (1997). Cannibals with Forks: The Triple Bottom Line of 21st Century Business. Capstone Publishing.
Securities and Exchange Board of India (SEBI). (2023). Guidelines for Business Responsibility and Sustainability Reporting (BRSR). SEBI Publications.
Although blockchain can provide unparalleled transparency and immutability in audit trails, the challenges related to technical infrastructure, regulatory clarity, and auditor preparedness have limited the adoption of blockchain technology. Referring to how audit firms are investigating blockchain technology around the world at different rates, in sectors such as fintech, insurance, and supply chain finance, and with limited comparative perspectives on the transition.
Evaluate the readiness and willingness of audit firms in India, Asia-Pacific, North America and Europe to use blockchain for financial auditing.
Identify blockchain-specific opportunities in various industries for sectors, such as real-time audit, data integrity, and cross-border compliance tracking.
Investigate any regulatory, technological, and organizational obstacles in the current environment.
Evaluate the applicability and appropriateness of implementing blockchain against global regulatory regimes (FATF, OECD, ICAI).
Case Studies:
Audit firms with pilot implementations, or active research in blockchain auditing (e.g., PwC India, Deloitte Canada, KPMG Singapore)
Industry Focus:
Comparative use in fintech auditing, insurance claims validating, and blockchain enabled supply chain finance auditing
Surveys & Interviews:
Gather insights through structured surveys and interviews with regional auditors and professionals involved in audit technologies.
SWOT & PESTLE Analysis:
Examine strategic readiness and external influences including regulatory landscapes
Regulatory Review:
Review contemporary policy guidance (e.g., blockchain tax and transparency reports from the OECD, FATF Guidance for the supervision of virtual assets, ICAI Blockchain Integration Report, 2023)
Distributed Ledger Technology (DLT) Theory – as the basic tech model
Audit Lifecycle Theory – can be used to map blockchain integration in the planning, testing, and reporting phases
Technology Acceptance Model (TAM) and Diffusion of Innovations Theory – to evaluate adoption behaviour and preparedness for innovation
Dai, J., & Vasarhelyi, M. A. (2017). Advancing accounting and auditing practices through blockchain technology: A conceptual framework. Journal of Information Systems, 31(3), 5–21.
Institute of Chartered Accountants of India (ICAI). (2023). Technical Report on the Integration of Blockchain Technology in Accounting and Auditing. ICAI Digital Accounting and Assurance Board.
Nakamoto, S. (2008). Bitcoin: A Peer-to-Peer Electronic Cash System. Retrieved from https://bitcoin.org
Although traditional models like Altman’s Z-score are popular as predictors of bankruptcy, they often fail in emerging economies due to data opacity, irregular financial disclosures, and local accounting standards. It would be worthwhile to determine how machine learning (ML) models can outperform traditional models across countries, especially in predicting bankruptcy in SMEs using different accounting standards (IFRS vs GAAP).
Build and compare machine learning algorithms to predict SME bankruptcy using firm level financial characteristics (i.e., liquidity, solvency, operational efficiency).
Compare the ML models with other traditional models (e.g., Altman’s Z-score) amongst different countries.
Understand the effect of different accounting standards (e.g., Indian GAAP, IFRS, US GAAP) on bankruptcy prediction reliability.
Utilize explainable AI methods such as SHAP and LIME to enhance transparency and interpretability of machine learning models.
Propose region-specific early-warning indicators on SME financial difficulties.
India: MCA, CMIE Prowess, privately available SME balance sheets
Global: OECD SME Finance Scoreboard, World Bank MSME Database, Orbis (for global SME data)
Random Forest, XGBoost, Logistic Regression and Neural Networks
ROC-AUC, precision-recall, F1-score and confusion matrix analysis
Apply tools like SHAP and LIME to interpret model predictions, highlighting key features, their influence, and the decision boundaries involved.
Determine performance differences across SMEs subject to different accounting regimes (India vs EU vs US)
Consider ratio dependability differences under IFRS, Indian GAAP and US GAAP.
Financial Distress Prediction Theory
Altman’s Z-Score and Credit Risk Models
Machine learning in Financial Forecasting
Accounting Harmonization Theory – referring to the potential impact divergent standards may have on financial metrics
Altman, E. I. (1968). A statistical model for evaluating the likelihood of corporate bankruptcy using financial ratios and discriminant analysis. Journal of Finance, 23(4), 589–609.
Barboza, F., Kimura, H., & Altman, E. (2017). Predictive modeling for business insolvency using machine learning techniques. Expert Systems with Applications, 83, 405–417.
Reserve Bank of India (RBI). (2024). Annual Report on Performance and Credit Access in the SME Sector. RBI Publications.
Although accounting conservatism is often viewed as a mean of providing protection from misstatement in financial reporting, an exploration into the varying levels of conservatism and stockholder reactions in periods of economic expansion has not yet been studied, and specifically in developing economies like India. Additionally, there is a lack of comparative knowledge between developing and developed economies on an apparent behavioural difference between conservative vs. aggressive financial reporting.
Identify the level of accounting conservatism illustrated by financial reporting utilizing quantitative measures.
Compare responses to conservative versus aggressive accounting practices by investors in chosen developing (e.g., India, Brazil) and developed markets (e.g., Germany, UK).
Investigate how investor sentiment evolves through different macroeconomic cycles / conditions (e.g. recovery after COVID-19, periods of inflation).
Ascertain whether conservatism increases trust or delays responses in stock markets.
Use Basu’s Asymmetric Timeliness Model and related accrual-based measures to measure accounting conservatism.
Carry out event study analyses around earnings announcements using a multi-country panel dataset.
Validate conclusions using global investor sentiment proxies such as global indices (MSCI Emerging or Developed Markets), proposed ETFs, and volatility.
Examine the differences in share price/movement response and shifts in trading volume firms that engaged in conservative versus aggressive reporting.
Examine changes over time during significant global events (e.g. chomunity Covid 19– 2020–2023, BRICS growth trajectory).
Accountability Conservatism Theory – To explain delay in revenue recognition
Behavioural Finance Theory – To account for investor judgment and bias
Signaling Theory – To evaluate in what way a conservative evaluation may provide assurance, or does it send out a cautionary signal
Institutional Theory – To compare how markets respond differently under conditions of different types of institutional quality and governance standard.
Basu, S. (1997), “The Conservatism Principle and the Asymmetric Timeliness of Earnings.”
Watts, R. L. (2003), “Conservatism in Accounting Part I: Explanations and Implications.”
NSE/BSE data sets, 2019–2024.
The Indian Public Sector budgeting frameworks are static and do not allow changes to revenues (i.e. GST revenues) and expenses in real time, ultimately resulting in mistakes and inefficiencies under fiscal governance, delays and allocations and underspending.
Create and apply a model for continuously updating the allocation of budgets for revenue based on expected revenues using machine learning.
Measure the value add (cost control, responsiveness, efficiencies of resource use, etc.) of any AI-based interventions.
Conduct pilots based on datasets from municipalities or state level government departments.
Assess India’s state of readiness for dynamic budgeting against other international models of smart governance.
Data Sources: Use, and combine, historical budget datasets which have live feeds with financial information like GST collections dashboards, government grants and reported expenses from departments
Modelling approach: Time series forecasting with LSTM, ARIMA or combinations of models; budgeted reallocations in real time.
Comparative Framework: Compare the outputs of AI-led dynamic budgets to those of static budgets for deficit control, responsiveness, sustainable surplus allocation.
Implementation pilot: Using open access datasets, e.g. OECD public finance statistics, cities budget APIs, USA, or simulated budgets of local governments in India.
Zero-Based Budgeting (ZBB) – for need-based funding allocation.
Usage of Artificial Intelligence (AI) in Public Finance – adaptive learning and automation.
Government Resource Planning (GRP) – ensure system integration for the budget module.
Smart Governance Models – to cte examples of smart nation in Singapore, smart government/ UAE, e-governance model in Estonia.
