By Sunando Roy
The old division between “business people” and “technology people” is becoming increasingly difficult to sustain. Financial institutions now need professionals who understand customers, products, regulation and risk, while also being comfortable with data, artificial intelligence, cloud services and digital platforms.
The defining characteristic of the next wave of digital-finance talent will therefore not be technical expertise alone. It will be the ability to connect technological capability with sound financial judgement.
It is increasingly clear that AI will redesign tasks, not simply erase jobs. Bank for International Settlements distinguishes between an “AI copilot” scenario, in which technology supports and accelerates an employee, and an “AI agent” scenario, in which defined activities are performed with greater autonomy. Even in the more automated model, human oversight remains necessary for interpretation, ethical judgement and accountability.
This transition is already under way. A 2024 survey by the Bank of England and the Financial Conduct Authority found that 75 per cent of responding financial firms were already using AI, while a further 10 per cent intended to do so within the following three years. Firms also identified insufficient talent and access to skills as an important constraint. Nearly half reported only a partial understanding of some of the AI technologies they used. Adoption without internal capability can therefore create new dependencies and blind spots.
The likely outcome is not simply fewer traditional jobs and more programmers. Finance will create more hybrid roles: AI product owners, digital-conduct specialists, model-risk translators, financial-data stewards, cloud-resilience professionals and technology-aware compliance officers. These roles require enough technical knowledge to challenge models and providers, but also enough financial-sector experience to recognise when an apparently efficient solution may create unsuitable, unfair or unsafe outcomes.
The emerging talent stack
The next generation of digital-finance professionals will need a layered capability rather than a single specialist qualification.
1. Domain knowledge
A professional must first understand the financial activity itself: banking, payments, insurance, investments, prudential risk, consumer protection or financial regulation. Technology cannot compensate for a weak understanding of the underlying business and its obligations.
2. Data fluency
Data fluency is more than the ability to operate a dashboard. It includes understanding definitions, quality, lineage, limitations and the consequences of incomplete or inconsistent data.
3. AI literacy
Employees do not all need to become data scientists, but they should understand how models are trained, where hallucination and bias may arise, and when independent verification is required.
4. Product and process thinking
Professionals must be able to see an entire customer or operational journey rather than optimise one departmental task in isolation. A fast decision is not an improvement if it shifts delays, confusion or risk to another part of the service.
5. Governance literacy
Digital work increasingly touches privacy, cybersecurity, outsourcing, model risk, consumer protection and operational resilience. These issues cannot be left to a final compliance review after the product has already been designed.
6. Distinctly human capabilities
Judgement, communication, constructive scepticism, empathy and the courage to challenge an automated recommendation remain essential. Intelligent systems may generate an answer; people must still decide whether the answer is fair, prudent, lawful and appropriate.
Supervisors need the same blended capability
The talent challenge is not confined to banks and other financial institutions. Financial supervisors also require sufficient technological capability to understand how AI, machine learning, cloud computing and application programming interfaces are changing the institutions they oversee.
Technology-enabled supervision will not be achieved merely by purchasing suptech tools. Supervisors must be able to interpret outputs, question assumptions, recognise data limitations and connect technological findings to supervisory judgement.
Build talent instead of only buying it
Traditional recruitment often searches for complete candidates who already possess financial expertise, technical proficiency, leadership experience and regulatory awareness. Such people are scarce, and competition for them is intense. Institutions must therefore become builders of talent rather than only buyers of talent.
A stronger development model combines structured learning with practical assignments. A credit specialist can rotate through an analytics team. A data scientist can spend time with collections, compliance or customer-service functions. A supervisor can participate in technology-focused inspections or AI model reviews. Internal academies should be connected to real work, with employees assessed on whether they can apply new skills rather than merely complete courses.
The early-career pipeline also needs protection. Routine analytical and administrative tasks have historically helped junior employees understand how financial institutions work. If those tasks are automated without replacing their developmental value, institutions may create a future shortage of experienced judgement. International Monetary Fund analysis indicates that entry-level work may be particularly exposed to AI-driven change even as demand for new professional, technical and managerial skills grows.
Talent is now a strategic capability
The institutions that succeed will not necessarily be those employing the largest number of AI specialists. They will be those that distribute digital competence throughout the organisation while maintaining centres of deep expertise.
Boards and senior management should therefore treat workforce transformation as part of business-model and risk strategy, not as a narrow human-resources programme. The next wave of talent will consist of professionals who can work with intelligent systems without surrendering judgement to them. In financial services, that distinction will remain fundamental.
The future finance professionals will combine data science, AI and finance specializations with the ability to assimilate diverse skills for strategic advantage. Outside the financial world, the same convergence of domain specialists and tech will happen. The change will happen with breakneck speed.
Selected references
Bank for International Settlements. (2025). Artificial intelligence and human capital: Challenges for central banks (BIS Bulletin No. 100). https://www.bis.org/publ/bisbull100.htm
Bank for International Settlements, Financial Stability Institute. (2025). Smart supervision: Sound capacity development approaches for tech-savvy supervisors (FSI Insights No. 70). https://www.bis.org/fsi/publ/insights70.htm
Bank of England, & Financial Conduct Authority. (2024). Artificial intelligence in UK financial services – 2024. https://www.bankofengland.co.uk/report/2024/artificial-intelligence-in-uk-financial-services-2024
Georgieva, K. (2026, January 14). New skills and AI are reshaping the future of work. IMF Blog. https://www.imf.org/en/blogs/articles/2026/01/14/new-skills-and-ai-are-reshaping-the-future-of-work
Monetary Authority of Singapore. (2022, September 15). MAS launches Financial Services Industry Transformation Map 2025 [Media release]. https://www.mas.gov.sg/news/media-releases/2022/mas-launches-financial-services-industry-transformation-map-2025
Monetary Authority of Singapore. (n.d.). Skills and talent development. Retrieved July 24, 2026, from https://www.mas.gov.sg/development/jobs-and-skills
World Economic Forum. (2025). The future of jobs report 2025. https://www.weforum.org/publications/the-future-of-jobs-report-2025/
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