AI & BFSI Watch for October 2026

Banks Warned of AI-Linked Cyber Risks

Union Finance Minister Nirmala Sitharaman and Electronics and Information Technology Minister Ashwini Vaishnaw chaired a high-level meeting with scheduled commercial banks to review emerging artificial intelligence-linked risks affecting the banking sector.

The discussion focused on the possibility that advanced AI tools could be used to identify software vulnerabilities and increase the speed and sophistication of cyberattacks.

Banks were advised to strengthen cybersecurity capabilities by engaging specialised cybersecurity professionals and agencies. The emphasis was on improving preparedness and developing preventive measures against emerging AI-enabled threats.

A key recommendation was the establishment of stronger real-time threat-intelligence sharing mechanisms involving banks, the Indian Computer Emergency Response Team (CERT-In) and other relevant agencies.

As banks increasingly depend on interconnected digital systems, AI-related cyber risks have become an important operational and technology-risk concern. Faster information sharing can help financial institutions identify attack patterns, address vulnerabilities and coordinate responses more effectively.

FinTech AI Revenue to Rise

FinTech companies are making significant investments in artificial intelligence (AI), but meaningful revenue contributions from AI-led products are expected to emerge only from FY28, according to an assessment by Emkay.

The outlook indicates that the sector remains in a capability-building phase, with companies focusing on technology infrastructure, data capabilities, specialised talent and AI governance before achieving large-scale commercial benefits.

AI adoption across financial technology is creating opportunities in areas such as lending, customer service, fraud detection, risk assessment and financial advisory. However, developing effective AI solutions requires substantial investment and does not immediately translate into higher revenue.

The transition from experimentation to commercially scalable AI products is expected to take several years. Financial institutions are also increasingly partnering with FinTech companies to integrate AI capabilities into banking operations.

AI-based lending and underwriting solutions can improve credit assessment, while fraud detection systems can identify unusual transaction patterns and customer service tools can automate routine interactions.

Central Banks Examine Systemic Risks from Growing AI Adoption

Central banks and financial regulators globally are increasingly examining the potential systemic risks arising from widespread artificial intelligence adoption across financial institutions and markets.

While AI can improve efficiency, risk assessment and financial services delivery, its increasing integration may create new vulnerabilities affecting financial stability. Financial institutions are using AI in areas such as algorithmic trading, credit assessment, fraud detection, customer service and compliance monitoring.

One major concern is model concentration risk. If multiple institutions rely on similar AI models, data sources or technology platforms, failures or errors could affect several organisations simultaneously.

AI-driven decision-making may also amplify market movements, particularly when automated systems respond to similar signals during periods of stress. Regulators are focusing on challenges related to model transparency, explainability, data quality and governance.

Cybersecurity risks remain another important area, with AI systems potentially exposed to manipulation, data poisoning and unauthorised access. Dependence on common technology providers also creates third-party concentration risks.

Effective AI governance, human oversight and strong risk management frameworks will be critical as financial institutions expand their use of artificial intelligence.

AI Adoption in Supplier Risk Management Grows but Integration Challenges Remain

Artificial intelligence adoption in supplier risk management and sustainability functions is increasing, but many organisations continue to face challenges in moving from experimentation to large-scale implementation.

According to industry findings from an Achilles survey, businesses are recognising AI’s potential to improve supply chain visibility, risk assessment and sustainability monitoring. However, integration across organisations remains limited.

AI applications in supplier risk management include supplier screening, risk scoring, compliance monitoring and sustainability assessment. These capabilities help organisations analyse large volumes of supplier information and identify potential risks in increasingly complex global supply chains.

Challenges remain around data quality, system integration, technology costs and internal expertise. Effective AI-based risk assessment depends on accurate and consistent supplier data, while fragmented systems and incomplete information can reduce the reliability of outputs.

AI can also support sustainability risk management by monitoring environmental performance, evaluating supplier practices and identifying potential compliance concerns. Strong governance and reliable data frameworks will be essential for organisations seeking to scale AI-driven supplier risk solutions.

CyberCube Develops AI Loss Framework for Emerging Cyber Risks

Cyber risk analytics firm CyberCube has introduced a framework to help insurers, businesses and risk professionals evaluate potential losses arising from the increasing adoption of artificial intelligence (AI).

The framework aims to improve understanding of AI-related exposures and support better assessment of financial impacts from AI-driven cyber incidents.

The growing use of AI in decision-making, automation, customer services and operational processes is creating new risk scenarios. Unlike traditional cyber risks, AI-related exposures may involve model failures, inaccurate outputs, data manipulation, unintended automated decisions and large-scale operational disruptions.

For insurers, understanding these emerging risks is becoming important for developing cyber insurance products, improving underwriting approaches and creating appropriate pricing models.

Traditional cyber risk assessments may not fully capture AI-specific exposures, which can include business interruption, regulatory consequences, reputational damage and liability risks.

A structured assessment framework can help analyse factors such as AI system usage, dependency levels, data exposure, operational impact and recovery requirements.

Banks Need Collective Vigilance as Frontier AI Transforms Banking Risk

The increasing adoption of frontier artificial intelligence (AI) in financial services requires stronger collaboration among banks, regulators and technology providers to manage emerging risks and strengthen operational resilience.

Advanced AI systems are being explored across banking functions including customer service, fraud detection, risk analysis and process automation. However, greater dependence on AI introduces challenges related to cybersecurity, data governance, model reliability and financial stability.

A key concern is concentration risk arising from the use of common AI platforms, technology providers and shared infrastructure. A failure or vulnerability in widely used systems could potentially impact multiple financial institutions simultaneously.

Banks are therefore focusing on AI governance frameworks covering model validation, transparency, accountability, human oversight and continuous monitoring. Data quality remains critical, particularly where AI is used for credit assessment, compliance monitoring and customer risk analysis.

Cybersecurity is also becoming increasingly important as AI systems may face threats such as data manipulation, model exploitation and unauthorised access. Risk management teams will need greater understanding of both traditional banking risks and emerging technology risks.

India-UK Strengthen AI-Based Anti-Fraud Cooperation

India and the United Kingdom are strengthening cooperation in telecommunications and exploring the use of artificial intelligence (AI) to combat digital fraud, reflecting the growing need for coordinated action against technology-enabled financial crime.

The partnership focuses on using technology and information sharing to address increasing digital fraud risks. As online banking, digital payments and mobile-based financial services expand, fraudsters are adopting more advanced techniques to target individuals and businesses.

AI can support fraud prevention by analysing large volumes of transaction and communication data, identifying unusual patterns and detecting potentially fraudulent activities. Machine learning-based systems can help financial institutions improve monitoring capabilities and enable faster responses.

For banks and financial institutions, AI-driven fraud detection has become increasingly important as digital fraud creates financial losses, operational challenges and risks to customer trust.

Since fraud networks often operate across jurisdictions, international cooperation between governments, regulators, telecommunications providers and financial institutions is becoming essential. Telecommunications infrastructure plays a critical role as mobile numbers, messaging platforms and digital communication channels are frequently exploited for phishing, impersonation and social-engineering attacks.

SEBI Establishes AI Cyber-security Task Force

The Securities and Exchange Board of India (SEBI) has established a dedicated task force, cyber-suraksha.ai, to examine cybersecurity risks arising from advanced artificial intelligence tools.

The task force will focus on assessing AI-related cyber threats, developing mitigation strategies, strengthening threat-intelligence sharing and improving reporting mechanisms for critical cyber incidents.

The initiative recognises that AI-driven vulnerability identification tools can accelerate the discovery of system weaknesses and potentially increase the speed and scale of cyber exploitation.

The task force includes representatives from market infrastructure institutions, qualified registrars and transfer agents, regulated entities and other stakeholders. The approach reflects the interconnected nature of India’s securities-market ecosystem, where incidents affecting one participant can create wider operational risks.

SEBI has advised regulated entities to strengthen cybersecurity measures through timely patching, vulnerability assessments, vendor-risk management, change-management controls, API security and continuous Security Operations Centre monitoring.

Entities have also been encouraged to conduct cyber-risk assessments, maintain updated inventories of critical assets, strengthen system hardening and evaluate risks arising from third-party technology dependencies.