By Gaurav Nigam, EVP & Business Head, NIIT Limited
A fraud analyst who cannot interpret the output of an AI model may now be less employable than someone who can, even when both have the same understanding of credit and risk.
That captures what is changing across banking, financial services and insurance. Jobs are not necessarily disappearing, but the skills required to perform them are evolving. Employers are therefore assessing candidates differently.
The debate has moved on
For some time, the debate around AI and employment centred on whether roles would continue to exist in five years. That remains a valid concern, but organisations are now asking a more immediate question: can employees work effectively with AI-enabled systems, understand the insights they generate and use them to make better decisions?
The World Economic Forum’s Future of Jobs Report 2025 says employers expect 39% of workers’ core skills to change by 2030. This does not mean that 39% of jobs will be lost. It suggests that many skills required within existing jobs will evolve, often while people remain in the same roles.
Closer to home, the NIIT India Skills Gap Report 2026, a nationwide study conducted with YouGov covering 3,500 students, professionals, recruiters and CXOs, found that 40% of employers expect AI to have only a moderate-to-minimal impact on roles. The emphasis is more likely to be on redesigning tasks and improving productivity than on reducing headcount.
What is changing inside BFSI
AI is increasingly being used for fraud detection, customer-service triage, initial risk assessment and compliance monitoring. It is taking over many repetitive and time-sensitive tasks, but the judgement that follows still requires people.
An AI system may flag a transaction as suspicious, but a professional must decide whether it is genuinely fraudulent. A model may generate a risk score, but someone may still need to determine whether the case warrants an override. A compliance alert may indicate a serious issue or simply reflect noise. Understanding the difference requires context, experience and accountability.
This is beginning to influence hiring and promotion decisions. Mid-level operations and compliance professionals, who may traditionally have relied on audit trails and manual checklists, are now expected to interpret AI-generated fraud and risk outputs. This is becoming part of the role, not a capability reserved for technology teams.
The NIIT report also found that professionals with six to fifteen years of experience are among the most sought-after candidates and the most difficult for recruiters to find. This matters because this is the workforce segment where AI is changing established roles most visibly.
These professionals bring domain knowledge and operating experience, but many now need an additional layer of digital and AI fluency. The challenge is finding the right combination of industry experience, technological confidence and decision-making ability.
Why this extends beyond banking
The pattern is not unique to BFSI. In healthcare, AI may support diagnostic triage. In law, it may assist with document review. In retail, it may be used for inventory forecasting.
Across sectors, AI is handling more of the repeatable and data-intensive parts of a job. Human contribution becomes more concentrated in judgement, interpretation, trust and communication. Professionals must understand the output produced by technology, place it in context and explain the resulting decision to those affected by it.
BFSI may be further along because financial data was digitised relatively early and competitive pressures accelerated technology adoption. What is happening in the sector today offers an indication of what other industries may experience as AI adoption expands.
What employability means now
For someone beginning a career in BFSI, knowledge of financial products, regulations and customer requirements remains essential. What has been added is the expectation that professionals should be comfortable interpreting AI-generated insights, asking follow-up questions and recognising when a model’s recommendation should be accepted, investigated further or challenged.
This does not mean every banker, insurance professional or financial analyst needs to become an AI specialist. It means they need enough understanding to work confidently in an environment where AI is embedded in everyday processes.
Human capabilities are also becoming more valuable. Building customer trust, making ethical decisions in ambiguous situations and communicating complex outcomes clearly cannot be treated as secondary skills. AI may flag a suspicious transaction, but it cannot independently manage the customer relationship or explain why an account has been frozen.
Learning does not end at graduation
A qualification can help someone enter the workforce, but it is unlikely to remain sufficient throughout an entire career. Certifications, applied projects, simulations and exposure to how AI tools are used in financial environments are becoming increasingly important.
For organisations, this means building a continuous approach to capability development. For individuals, it means accepting that learning is now a regular part of professional life.
AI is not necessarily reducing the size of the BFSI workforce. It is changing the basis on which professionals are considered ready for roles and career progression. Employability will increasingly depend on what people know, how effectively they can learn and how confidently they can apply judgement alongside technology.