CA journal - Nov 2025

 The article "AI-Driven Finance: Redefining the Role of Chartered Accountants in the Age of Intelligent Automation" by CA. Chandrasekar Parameswaran is reproduced below, synthesizing the information available in the sources:

AI-Driven Finance: Redefining the Role of Chartered Accountants in the Age of Intelligent Automation

(By CA. Chandrasekar Parameswaran)

Finance has continuously evolved with technology, and AI is the next big leap. AI now handles fraud checks, reconciliations, and compliance, freeing Chartered Accountants (CAs) from routine tasks. For CAs, this shift is about reinvention—moving into roles of judgment, ethics, trust, and advisory. The CA’s role is being reshaped for a world that is real-time, data-driven, and technology-led. CAs are well-positioned to guide how AI is used in financial oversight due to their knowledge of finance, law, audit, compliance, logical thinking, and professional judgment.

Understanding AI in the Financial Context AI refers to systems that learn from data, find patterns, and make decisions with very little human help. In finance and accounting, three key parts of AI are crucial:

1. Machine Learning (ML): This allows systems to find trends, detect unusual activities, and make predictions, such as forecasting cash flows or warning about suspicious transactions.
2. Natural Language Processing (NLP): This helps computers understand human language, useful for reading compliance reports, contracts, or large sets of emails.
3. Robotic Process Automation (RPA): This manages repetitive, rule-based work like filling tax forms or reconciliations.

CAs must understand how AI models are trained, the type of data they use, how biases or errors can enter, and whether the logic can be explained, in order to trust the results. In a world where AI not only supports decisions but also makes them, CAs must act as interpreters and supervisors.

Real-World Applications – Global and Indian Examples AI is no longer just a lab experiment; it is changing how advisory services, compliance, taxes, and audits are delivered.

• Global Examples: Multinational firms use AI-powered audit platforms for real-time checks across entire ledgers. Advanced analytics engines perform deep, data-based audits. AI-based tax tools monitor regulatory changes and provide early warnings about potential risks. Machine learning models are applied in forensic audits to detect fraud by analyzing behavioral patterns.
• Indian Initiatives: Private banks, such as HDFC Bank and ICICI Bank, use AI to improve credit scoring models. Startups like ClearTax and RazorpayX utilize AI for anomaly detection, invoice checking, and GST reconciliation. Many Indian CA firms are also embracing AI for transaction reviews during concurrent audits, forensic audits, and GST reconciliation.
• ICAI Initiatives: The Institute of Chartered Accountants of India (ICAI) launched CA-GPT (trained on 75 years of ICAI guidance) and ICAI-GPT for financial reporting. These tools have processed over 2.5 lakh prompts, generating over 70 specialized GPTs to support various fields.

The Role of Chartered Accountants – Risks or Rewards? As AI takes over repetitive, rules-based tasks, the role of CAs becomes stronger and more strategic. The true value of a CA lies in judgment, interpretation, and ethical oversight, areas where AI cannot work alone. CAs are shifting from operators to analysts, moving into roles as:

• AI-assisted auditors, checking exceptions identified by algorithms.
• Data interpreters, converting complex data outputs into meaningful insights.
• Ethical watchdogs, ensuring AI systems are unbiased, fair, and compliant.
• Advisors, guiding clients through digital change and AI adoption.

The real risk is not that AI will replace accountants, but that accountants who do not understand AI will be replaced by those who do.

Ethical Considerations and Governance AI is a decision-making system, and every decision in finance must follow strong professional and ethical standards. Ethical risks include biased data, leading systems to unknowingly favor certain groups or wrongly flag innocent behavior as suspicious. CAs are the right people to govern AI in finance because they understand controls, governance, and laws. They can ensure audit trails are proper and test AI outputs against legal and professional standards.

India’s Preparedness

• Government Momentum: The Digital Personal Data Protection (DPDP) Act, 2023, provides a legal framework for AI systems based on privacy and consent. The MCA21 V3 platform uses AI for compliance alerts and checking company filings. Tax authorities use AI to find fraud and irregularities, flagging over ₹14,000 crore in false claims.
• ICAI Action: ICAI has launched several initiatives, including the AI Innovation Summit 2025, where it signed an MoU with Google India; AI Certification Courses; and a project with regulators to build an Early-Warning System Pilot for fraud detection in listed companies.

Challenges and Roadblocks Several challenges hinder AI adoption: a Digital Literacy Gap exists, particularly among older CAs and those in smaller firms; there is Resistance at the Firm Level due to technical costs and lack of knowledge; there is a Lack of Regulatory Clarity regarding liability for AI mistakes; and the Black-Box Problem (hidden decision-making logic) complicates the CA's need for transparency and auditability.

The Path Ahead The future requires CAs to adopt structured growth. This includes incorporating AI literacy and prompt engineering into the CA curriculum and articleship; making AI literacy mandatory for continuous professional development (CPD); viewing tools like CA-GPT as everyday utilities; and leading the development of AI Governance Standards and AI Audit Certification.

Conclusion AI can automate many tasks, but it cannot replace judgment, ethics, or trust—the very foundation of the CA profession. The future requires a CA to mix strong finance knowledge, comfort with technology, and a new strategic advisory mindset.

The article titled "The DeFi Evolution: A New Financial Paradigm" by Quiser Aman explores the rise of Decentralized Finance (DeFi) and its impact on traditional systems.

The DeFi Evolution: A New Financial Paradigm

Definition and Core Principles Decentralized Finance (DeFi) is a method of providing financial services without relying on traditional banks or financial organizations. Instead, users interact with technology, primarily blockchain, to conduct transactions such as borrowing, lending, trading, and saving. The fundamental goal of DeFi is the elimination of intermediaries, achieved through smart contracts—self-executing agreements on the blockchain that automatically enforce their terms without human oversight. DeFi aims to create an open and unrestricted financial ecosystem that functions independently of existing financial institutions.

Key Characteristics and Growth DeFi encourages accessibility by allowing anyone with an internet connection to use its services, thereby serving people who may lack access to traditional banking. It ensures enhanced transparency, as all transactions are recorded on a public blockchain that anyone can check. By eliminating costly intermediaries, DeFi reduces transaction fees and enhances access to financial services globally.

However, the decentralized financial market has experienced volatility: the overall value of DeFi contracts (Total Value Locked - TVL) decreased by approximately 42.61%, falling from $161.975 billion on April 3, 2022, to about $92.948 billion on April 1, 2024.

Structure of DeFi DeFi is organized into four primary layers:

1. Settlement Layer (Zero Layer): This layer serves as the foundation, including cryptocurrencies and blockchain, creating a safe and transparent atmosphere for transactions.
2. Protocols Layer: This layer includes standards and regulations that govern operations and ensure liquidity across the network.
3. Applications Layer: This layer contains user-oriented applications, such as Decentralized Exchanges (DEX) and lending portals.
4. Aggregation Layer: Aggregators generate this layer by joining applications to optimize financial transactions and earnings.

Evolution of DeFi The evolution of DeFi can be traced through several phases:

• Beginnings (2015-2017): DeFi began with the release of Ethereum, which provided smart contracts for developers to design decentralized applications (dApps).
• Initial Projects (2018): The first systems focused on lending and borrowing, such as MakerDAO, which allows borrowing stablecoins by pledging crypto assets as security.
• Rising Popularity (2019-2020): Platforms like Compound and Aave emerged, enabling users to lend cryptocurrency for interest, and the concept of "yield farming" gained popularity.
• Broader Recognition (2021): DeFi started receiving mainstream attention, and Decentralized Exchanges (DEXs) allowed users to trade directly with one another.
• 2022-Present: As DeFi matured, it faced obstacles related to security concerns and regulatory oversight, though it continues to evolve with breakthroughs in governance.

Key Elements of DeFi Key functional components include:

• Smart Contracts: Self-executing agreements written in code (like Solidity) that automate transactions.
• Decentralized Exchanges (DEXs): Platforms like Uniswap that allow users to trade cryptocurrencies directly with each other.
• Borrowing and Lending Protocols: Services like Aave and Compound that allow users to lend crypto for interest or borrow against collateral.
• Stablecoins: Linked to existing assets (e.g., US dollar) to provide price stability for trade and lending (e.g., DAI and USDC).
• Insurance Protocols: Platforms like Nexus Mutual that cover risks such as smart contract failures and hacker attacks.

Challenges and Opportunities for Chartered Accountants (CAs) Despite its benefits, DeFi faces risks, including security concerns due to smart contract weaknesses, regulatory uncertainty, and the inherent unpredictability of the cryptocurrency market.

The rise of DeFi creates significant opportunities for Chartered Accountants:

• Compliance and Advisory: CAs can help guide DeFi projects through legal frameworks and ensure compliance with developing rules.
• Taxation: They can provide consulting services regarding the complex tax implications of DeFi transactions.
• Auditing: CAs are well-suited to perform financial audits on smart contracts and decentralized apps, ensuring their integrity.
• Accounting Frameworks: They can provide proper bookkeeping frameworks for tokenized assets.

The article addressing your query is titled "The Global Shift Away from Dollar Dominance: A New Path Forward" by CA. Jay Joshi.

This article examines the ongoing global movement, known as "de-dollarization," which is driven by nations seeking greater economic autonomy, resilience against dollar-centric policies, and a strategic rebalancing of global power.

Key Points on Dollar Dominance and the Shift Away:

The Rise of the Dollar:

• The dollar’s dominance began after World War II, when the United States emerged as a global economic superpower.
• The Bretton Woods Agreement in 1944 established the dollar as the anchor of the global monetary system by linking it directly to gold at a fixed rate of $35 per ounce.
• The continued demand for the dollar globally allowed the U.S. to print currency without needing to maintain equivalent gold reserves.
• The U.S. utilized this monetary influence to shape global commerce and exert control over geopolitics, including implementing economic sanctions against nations relying on dollar transactions.
• This dominance provided substantial economic benefits to the U.S., enabling it to import goods inexpensively and support a high domestic standard of living.

The Shift Toward De-Dollarization:

• De-dollarization is gaining momentum due to cracks appearing in the dollar's foundation.
• The reliance on imported goods has led to significant trade imbalances for the U.S..
• China has started to reduce its exports to the West, consequently reducing demand for the dollar.
• Nations are forming alliances like BRICS (Brazil, Russia, India, China, and South Africa) to lay the groundwork for local currency trade agreements that bypass the dollar.
• India is emerging as a critical economic force, setting up manufacturing centers and actively negotiating trade agreements with neighboring countries that prioritize the use of the Indian Rupee in international trade.
• Technological innovations, including blockchain technology and the development of digital currencies (like the Digital Rupee and Yuan), are facilitating smoother cross-border payments in local currencies, further reducing the need for dollar intermediaries.
• The transition points toward a multipolar economic future, requiring strategic planning and the strengthening of regional financial institutions like the Asian Infrastructure Investment Bank (AIIB) and the New Development Bank (NDB).

The article titled "Ind AS 118: Presentation and Disclosure in Financial Statements," authored by CA. Jinal Arpit Patel, discusses the introduction and key requirements of the proposed standard.

Ind AS 118: Presentation and Disclosure in Financial Statements

Background and Implementation The International Accounting Standards Board (IASB) issued IFRS 18 in April 2024. In alignment with IFRS convergence, the Accounting Standards Board (ASB) of the Institute of Chartered Accountants of India (ICAI) issued an exposure draft of Ind AS 118 on January 06, 2025. This proposed standard is intended to replace Ind AS 1 and is set to apply to reporting periods beginning on or after 1 April 2027.

The primary goal of Ind AS 118 is to improve how companies communicate financial performance in their financial statements. While it does not change how performance is measured, it standardizes presentation and disclosure requirements.

Key Changes Introduced by Proposed Ind AS 118 The proposed standard focuses on three key areas:

1. Introduction of new defined subtotals in the statement of profit or loss.
2. Enhanced disclosures on management-defined performance measures (MPMs).
3. Stronger requirements for aggregation and disaggregation.

1. Statement of Profit or Loss Categories and Subtotals Proposed Ind AS 118 mandates the presentation of new totals/subtotals in the statement of profit or loss. It classifies income and expenses into five mandatory categories:

• Operating: Includes all income and expenses arising from the company’s operations, regardless of whether they are volatile or unusual, excluding income/expenses from investments accounted for using the equity method.
• Investing: Includes income and expenses related to investments in associates, joint ventures, cash and cash equivalents, and other assets that generate a return individually and largely independently (e.g., debt or equity investments, investment properties).
• Financing: Includes income and expenses that arise from the initial and subsequent measurement of liabilities stemming from transactions that involve only the raising of finance.
• Income taxes: Comprises tax expense or income included in the statement of profit or loss, along with any related foreign exchange differences.
• Discontinued operations: Comprises income and expenses required by Ind AS 105.

The standard mandates three specific totals/subtotals be presented in the statement of profit or loss:

1. Operating profit or loss.
2. Profit or loss before financing and income taxes.
3. Profit or loss.

2. Management-Defined Performance Measures (MPMs) An MPM is defined as a subtotal of income and expenses that an entity uses in public communications outside financial statements.

• Disclosure Requirement: An entity must disclose information about all measures meeting the MPM definition in a single note.
• Required Disclosures: These disclosures must include, at a minimum: a description of the aspect of financial performance communicated by the MPM; how the MPM is calculated; and a reconciliation between the MPM and the most directly comparable subtotal in the financial statements, including the income tax effect and effect on non-controlling interest for each reconciliation item.
• Exclusions: MPMs specifically exclude totals such as gross profit or loss, operating profit or loss before depreciation/amortization/impairments, profit or loss before income taxes, or profit or loss from continuing operations.

3. Aggregation and Disaggregation Principles Ind AS 118 establishes principles for how items are to be combined or separated:

• Aggregation involves adding together assets, liabilities, equity, income, expenses, or cash flows that share characteristics.
• Disaggregation involves the separation of an item into component parts that have characteristics that are not shared.
• The overall purpose is to provide a useful, structured summary and material information without obscuring material information.
• Examples of shared characteristics include the Nature, Function, Persistence (recurring vs. non-recurring), Size, and Geographical location of the items.

Comparison with Ind AS 1 Several changes are noted in comparison to the previous standard, Ind AS 1:

Feature	Ind AS 1 (as on April 01, 2025)	Proposed Ind AS 118
Title	Presentation of Financial Statements	Presentation and Disclosure in Financial Statements
Balance Sheet Line Items	Standard list	List retained with the addition of Goodwill
Expense Classification	Required classification only by nature	Allows flexibility (nature, function, or both), but if classified by function, entities must disclose nature-based details in the notes
Cross-referencing	Required from a line item to notes	Requires both standard referencing and reverse cross-referencing (from notes back to the line item)

The sources contain a reproduction of an Opinion issued by the Expert Advisory Committee (EAC).

The specific EAC opinion provided is titled: "Accounting treatment of expenditure towards Special Development Plan (SDP) by the Company, under Ind AS framework".

Key details of this opinion include:

• The opinion was finalized on December 30, 2024.
• It addressed whether a company (a wholly owned undertaking of the Government of Karnataka) should treat expenditure on the construction of civil infrastructure (like barrages) that it did not own or control as revenue expenditure by debiting the Statement of Profit and Loss.
• The query was raised due to divergent opinions between the statutory auditors (who qualified the accounts, viewing it as capital expenditure) and the Comptroller and Auditor General of India (C&AG) (who viewed the revenue treatment as appropriate since the company was only acting as an executing agency).
• The EAC concluded that the company’s accounting treatment—recognizing the expenditure as an expense—was appropriate. This was based on the premise that the expenditure did not meet the definition of an asset because the company lacked the requisite right or control over the resources created, and there was no potential for future economic benefit to the Company from these assets.
• The sources also note that the EAC has published a Compendium of Opinions in forty-three volumes.

The article addressing Artificial Intelligence (AI) in the credit card industry is "Smarter Payments and Growing Influence of Artificial Intelligence in Credit Card Industry," written by Dipra Bhattacharya.

The article focuses on how AI and Machine Learning (ML) are transforming the credit card sector by enhancing security, personalizing services, and optimizing operations.

Smarter Payments and Growing Influence of Artificial Intelligence in Credit Card Industry

(By Dipra Bhattacharya)

The Growing Influence of AI and ML Artificial Intelligence (AI) and Machine Learning (ML) are fundamentally changing how the credit card sector operates. With the volume of transactions rising and fraud becoming more sophisticated, AI and ML offer unparalleled capabilities in processing vast amounts of data in real-time. This allows companies to ensure security, optimize operations, and personalize customer experiences.

Fraud Detection and Prevention Credit card fraud remains a significant challenge for financial institutions. In 2023, gross fraud losses incurred by card issuers, merchants, and acquirers reached $33.83 billion worldwide, slightly up from $33.45 billion the prior year. AI and ML step in to perform real-time processing and fraud identification beyond human capacity.

Key technical models used for fraud detection include:

• Anomaly Detection Algorithms: These identify unusual patterns in real-time transactions. Examples include Isolation Forest, which quickly isolates suspicious transactions; One-Class Support Vector Machine (One-Class SVM), which Mastercard uses to scan for transactions outside a user’s normal spending behavior; and Local Outlier Factor (LOF).
• Neural Networks: Deep learning models offer high accuracy in recognizing fraud patterns.
  • Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTMs) process sequential data to identify patterns in transaction history, adopted by companies like Visa and Amex.
  • Convolutional Neural Networks (CNNs) are used to analyze the spatial relationships between data points for high-risk transactions.
• Reinforcement Learning (RL): This is effective in dynamic environments where fraudsters constantly evolve their strategies. RL systems adjust decision thresholds based on historical patterns, such as for cross-border transactions monitored by Visa.

Enhancing Customer Experience AI is pivotal in creating tailored customer experiences, including targeted offers and customized services.

• Personalization: Collaborative Filtering analyzes user behavior to recommend similar products or offers, such as specific credit cards based on spending habits. Predictive Analytics anticipates a customer’s needs, for example, sending pre-emptive offers for travel insurance to frequent travelers.
• Natural Language Processing (NLP): NLP is extensively used in customer support via chatbots and virtual assistants. The integration of AI-powered chatbots has reduced operational costs for companies by 20–30% while improving customer satisfaction. Models like BERT understand the context in customer queries, and GPT generates conversational, human-like responses.

Credit Risk Assessment Machine learning algorithms provide a significant boost in predictive power for credit risk modeling.

• Models used include Logistic Regression and Decision Trees/Random Forests to predict the probability of default.
• Gradient Boosting Machines (GBM) and XGBoost are used widely due to their high predictive accuracy; Visa leverages XGBoost to analyze real-time transaction data to predict customers likely to default.
• AI has introduced alternative credit scoring models that use non-traditional data (like mobile phone usage and social media activity) to expand credit access to individuals without a formal credit score.

Implications for Chartered Accountants (CAs) The integration of AI and ML requires CAs to adapt beyond traditional practices.

• The CA profession is key to financial integrity, compliance, and strategic decision-making in the payment systems industry.
• CAs must now understand and evaluate AI-driven fraud systems, audit credit scoring models using alternative data, and ensure compliance with evolving digital regulations.
• As governance and risk oversight extend into algorithmic domains, CAs are positioned as key advisors to ensure the ethical, accurate, and financially sound deployment of AI within the payment ecosystem.