A Response to “Troubled Banks: Plug governance gaps for systemic correction” on The Hindu’s Business Line. 21 August 2025
Link to article: https://www.thehindubusinessline.com/opinion/troubled-banks/article69957121.ece
Today’s Business Line’s editorial on troubled banks correctly identifies a critical weakness in our financial system: the persistent governance gaps that threaten systemic stability. However, while the diagnosis is accurate, the solution requires a more radical reimagining of how banks approach governance, risk and compliance (GRC). The answer lies not in incremental reforms, but in leveraging artificial intelligence to create fundamentally superior governance systems.
The editorial rightly points to incomplete data flows, inaccurate information and inadequate monitoring as core problems. These are not confined to operational inefficiencies alone. They represent systemic vulnerabilities that traditional approaches cannot adequately address. The scale and complexity of modern banking operations have outpaced human capacity for effective oversight, creating dangerous blind spots that threaten both individual institutions and the broader financial ecosystem.
The Automation Benefits in Compliance Monitoring
Real-Time Risk Detection That Never Sleeps
Traditional compliance monitoring operates like a part-time security guard, checking systems periodically and hoping nothing goes wrong in between visits. This approach was barely adequate when banking was simpler. Today, it’s dangerously insufficient.
AI transforms compliance into a 24/7 surveillance network with superhuman analytical capabilities. Advanced machine learning algorithms can simultaneously monitor millions of transactions, identifying patterns that would be impossible for human analysts to detect. Beyond the sheer processing speed, it’s about recognizing sophisticated schemes that exploit the limitations of human oversight.
Real-World Example: Consider a complex money laundering operation where criminals conduct thousands of small transactions across multiple accounts, staying just below reporting thresholds. A human analyst might review these individually and see nothing suspicious. An AI system, however, can instantly correlate these seemingly unrelated transactions, recognizing the coordinated pattern and flagging the entire network for investigation.
This capability addresses the “incomplete data flows” problem identified in the editorial. AI processes existing data better, it identifies missing connections and hidden relationships that traditional systems miss entirely.
Intelligent Regulatory Adaptation
The regulatory landscape changes constantly, with new guidelines emerging from multiple jurisdictions simultaneously. Banks often struggle to keep pace, leading to compliance gaps that create systemic risks.
AI-powered natural language processing can monitor regulatory feeds from dozens of authorities simultaneously, instantly identifying changes that affect specific banking operations. More crucially, these systems can translate complex regulatory language into actionable compliance protocols without waiting for legal interpretation and implementation delays.
Practical Implementation: When new anti-money laundering regulations are published in different jurisdictions, an AI system can immediately map these changes to specific bank procedures, automatically updating risk assessment criteria and transaction monitoring rules. This eliminates the dangerous gap between regulatory change and operational compliance that often creates vulnerabilities.
Transforming Risk Management Through Predictive Intelligence
From Reactive to Predictive Risk Assessment
The editorial highlights the need for better risk indicators and monitoring systems. Traditional risk management is fundamentally reactive, that is, problems are identified after they occur, often when it’s too late to prevent significant damage.
AI enables truly predictive risk management, analyzing vast datasets including market conditions, customer behavior patterns, economic indicators and even social media sentiment to identify emerging risks before they materialize into actual losses or compliance failures.
Concrete Example: An AI system analyzing customer communication patterns, transaction histories and external economic data might predict which customers are likely to default on loans three to six months before traditional models would identify the risk. For a mid-sized bank, this early warning could prevent ₹50-100 crores in potential losses by enabling proactive loan restructuring or risk mitigation measures.
This predictive capability directly addresses the “inadequate monitoring” concern raised in the editorial. Instead of waiting for problems to surface, AI systems can identify and address risks while they’re still manageable.
Advanced Fraud Detection and Financial Crime Prevention
Modern financial fraud has evolved far beyond simple check forgery or identity theft. Criminals today use sophisticated networks, complex transaction patterns and subtle manipulation of legitimate processes to evade detection.
AI’s pattern recognition capabilities make it uniquely suited to combat these evolving threats. Unlike rule-based systems that rely on predefined scenarios, AI systems learn from every transaction and continuously improve their detection capabilities. They can identify new fraud patterns as they emerge, adapting their detection algorithms in real-time.
Operational Impact: While traditional systems might flag obvious suspicious activities like large cash deposits, AI systems can identify subtle indicators such as unusual timing patterns in transactions, anomalous geographical transaction clusters, or behavioral changes that suggest account compromise. This dramatically reduces both fraud losses and false positives that disrupt legitimate customer activities.
Ensuring Data Integrity and Governance Excellence
Automated Data Quality Management
Poor data quality is perhaps the most insidious risk facing modern banks, undermining every aspect of risk management and compliance. The editorial’s reference to “inaccurate data” reflects a widespread problem that traditional approaches have failed to solve.
AI addresses this through comprehensive automated data validation, cleansing and reconciliation processes. These systems can identify inconsistencies across multiple data sources, flag missing information and even predict and fill data gaps based on historical patterns and external validation sources.
Example Implementation: When customer data conflicts across different systems, say, different addresses in the CRM versus the transaction processing system; AI can cross-reference multiple authoritative sources including government databases, utility records and transaction patterns to determine the accurate information and automatically update all affected systems. This eliminates the data inconsistencies that often lead to compliance failures and operational errors.
Breaking Down Information Silos
Many banks operate with fragmented legacy systems where critical information remains trapped in departmental silos. This fragmentation creates the “incomplete data flows” problem highlighted in the editorial, where decision-makers lack comprehensive visibility into organizational risks.
AI can create intelligent integration layers that connect disparate systems without requiring expensive infrastructure overhauls. By establishing unified data models and automated integration processes, AI enables the holistic risk assessment that effective governance demands.
Strategic Benefit: Instead of different departments working with incomplete pictures, credit risk teams seeing only loan data, operations teams seeing only transaction data, compliance teams seeing only regulatory reports; AI creates a unified view that combines all relevant information for comprehensive risk assessment and decision-making.
Building Adaptive Governance Frameworks
Dynamic Risk Management Systems
Traditional governance structures are static, designed for predictable environments and standard operating procedures. The modern banking environment is anything but predictable, requiring governance frameworks that can adapt to changing conditions while maintaining strict oversight.
AI enables dynamic governance systems that automatically adjust risk parameters based on market conditions, modify compliance procedures in response to regulatory changes and reallocate resources based on emerging risk patterns. This creates governance frameworks that are both more responsive and more resilient than traditional approaches.
Implementation Example: During market volatility, an AI-powered governance system might automatically tighten risk parameters for certain loan categories, increase monitoring frequency for specific transaction types and alert senior management to emerging concentration risks, all without requiring manual intervention or committee meetings.
Comprehensive Third-Party Risk Management
The interconnected nature of modern banking means that governance failures at partner institutions or service providers can quickly become systemic risks. AI dramatically improves third-party risk management by automating due diligence processes and providing continuous monitoring of vendor risk profiles.
AI systems can analyze thousands of data points about potential partners, financial health, regulatory compliance history, cybersecurity posture, operational resilience and continuously monitor these factors throughout the relationship. This addresses the systemic interconnectedness risks that the editorial identifies as a key concern.
The Urgency of Implementation
The editorial’s call for “systemic correction” is both timely and necessary. However, the scale of change required cannot be achieved through traditional approaches. The volume of data, complexity of regulations and speed of market changes have outpaced human analytical capabilities.
Banks that implement comprehensive AI-powered GRC solutions today will build sustainable competitive advantages while contributing to overall financial system stability. More importantly, they will be prepared for the next crisis instead of merely reacting to it.
The technology exists today to address every governance gap identified in the editorial. Machine learning algorithms can process vast amounts of data in real-time, natural language processing can interpret complex regulations automatically, and predictive analytics can identify emerging risks before they become systemic threats.
A Need for Transformational Leadership
The path forward requires immediate and decisive action. Banking leaders must move beyond incremental improvements to embrace transformational change. This means:
Investing in AI Infrastructure: Building the technological foundation for intelligent governance systems that can handle the complexity and scale of modern banking operations.
Developing AI Literacy: Training staff to work effectively with AI systems, understanding both their capabilities and limitations.
Creating Adaptive Processes: Redesigning governance frameworks to be dynamic and responsive rather than static and reactive.
Fostering Innovation Culture: Encouraging experimentation and continuous improvement in GRC practices.
The editorial correctly identifies the problems plaguing our banking system. The solution is clear. AI-powered governance systems that can match the complexity and pace of modern financial markets. Banking leaders must have the vision and commitment to implement it before the next crisis tests our system’s resilience.
Beyond Technology There is a Need for Human Touch
However, technology alone cannot deliver systemic correction. The most sophisticated AI systems will fail without ethical foundations, cultural transformation, and strong leadership commitment.
Ethical and Attitudinal Prerequisites
AI implementation in banking governance must be grounded in transparency, fairness and accountability. Systems must be auditable and explainable, with clear pathways for review and appeal. Banks must cultivate a proactive governance mindset, moving from reactive compliance to strategic risk management.
This requires fundamental attitudinal shifts: from siloed thinking to integrated risk management, from technology fear to technology partnership and from process rigidity to adaptive excellence.
Strong Execution and Managerial Oversight
Success demands visible leadership commitment from CEOs and boards, with clear accountability, adequate resources and disciplined execution timelines. Managers must develop AI literacy while maintaining crucial human oversight capabilities, that is, knowing when and how to intervene when situations require human judgment.
The integration challenge is complex: technological capability must seamlessly combine with ethical responsibility, cultural transformation, and management excellence. Banks excelling in only one dimension will fail to achieve necessary systemic improvements.
The time for half-measures has passed. Our banking system needs transformational solutions and AI provides the tools to build them. The only question remaining is whether we’ll act with the urgency this challenge demands.
