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TCS BaNCS™: Enabling Transformation in Banks and Financial Institutions
Highlights
- For the first time in 6 years, AI has been mentioned more times than ESG, by the top executives of European banks when going through their annual results.
- This is a clear indicator of the mind share that AI is taking amongst the leadership of European banks.
- The article offers insights into how banking executives can guide their institutions through the AI revolution by focusing on strategic alignment, cultural transformation, workforce reskilling, ethics and trust, regulatory complexity, and cross-functional collaboration.
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AI use by EU Banks
Use of AI by European Banks
For the first time in 6 years, AI has been mentioned more times than ESG, by the top executives of European banks when going through their annual results. In fact, AI mentions have more than doubled since last year. This is a clear indicator of the mind share that AI is taking amongst the leadership of European banks. AI adoption is well on its way within European banks with a wide variety of areas of usage.
This is just the nascent stage, with most use cases focusing on efficiency benefits, and they are expected to soon become more complex and span multiple areas across the bank. This will open a new era of opportunities but will also present a new set of challenges to the leadership of banks across Europe. Leaders will need to reimagine their roles from that of decision makers to navigators of ambiguity, guides to ethics and enablers of human-AI coordination.
The aim is to provide a perspective on how bank leaders can steer their organizations through the AI revolution.
AI Adoption in financial services
Strategic alignment
Strategic alignment: AI with a tangible, measurable purpose
AI initiatives need to be aligned closely to the business objectives, with a focus on those that bring material value that are assessed by bankers and not technologists. Leaders should not treat AI as the next IT buzzword in the bank but see it as a catalyst for reimagining customer experience, compliance, and competitive positioning. Without a strategic view, even advanced and complex AI projects could end up as siloed experiments. In practice, this would require having AI embedded in the strategic planning process of every business unit and focusing on high impact, measurable use cases tied to core objectives, rather than dabbling in disconnected pilots.
Cultural shift
Cultural transformation: Fostering a pro-AI mindset.
Uncertainty on employment, job displacement and degradation have come out as key concerns in a research study on the impact of AI on banking employment in Europe. There’s a real fear that AI will replace people, and leaders will have to assuage these fears as best as they can. They need to impress upon everyone that the more likely reality is that the future of banking lies in augmented intelligence, where humans and AI systems work together. Implementing AI at scale requires a profound cultural shift within traditional banking organizations. It comes as no surprise that 64% of CXOs believe that the success of AI will depend more on the people than on the technology itself. Top executives must therefore function as agents of change, promoting a culture that embraces AI rather than fears it. This involves educating employees at all levels about the benefits of AI, addressing anxieties about job displacement, and demonstrating a commitment to ethical use of AI.
Workforce
Workforce upskilling, reskilling and retention.
AI is overhauling the skills required within a bank. AI can not only automate a whole host of repetitive tasks but is now able to perform more sophisticated tasks. This will have a direct impact on how employees perform their daily tasks with AI, possibly, completely changing the nature of their work. Leaders will need to focus on the optimum ways to upskill and sometimes completely reskill employees. This will need for banks to manage multiple tracks, with one track for most employees to gain broad fluency of AI capabilities in the context of their jobs and the bank’s objectives, with other tracks for technical and domain-related capabilities. AI skills are in high demand and will continue to be so for the near future, and banks will have to actively engage, challenge, nurture and incentivize skilled employees.
Ethics and Trust
Ethics and trust: Ensuring responsible AI
Any conversation in Europe on AI will have the themes of trust and ethics coming up continuously. It comes as no surprise that in a survey of 30,000 Europeans, when it comes to building trust, data security and confidentiality ranks as a top priority (66%) and ranks higher than concerns like track record of accuracy (59%) or transparency of AI decision making process (57%).v It is imperative to have a “trustworthy AI” that is aligned to societal values, transparent, explainable, auditable, accurate and with required human oversight. Leaders should ensure to involve the bank’s compliance teams early on, implement strong AI governance frameworks, layout clear ethical guidelines, ensure bias testing, and frame review processes for AI models. Banks should also consider establishing an ethics review board having cross functional oversight.
Tech & Business
Collaboration between technology and business.
AI spans across all departments and functions and needs close collaboration. In a pan-industry survey, 65% of the CEOs were of the view that the organization’s success in successful AI adoption is related to the quality of the collaboration between business and technology. Culturally, banks have been rigid with each department working in silos. Leaders should establish cross-functional teams for AI projects, bringing together stakeholders from compliance, risk, operations, and front-line business areas alongside data engineers. This “one team” approach ensures that AI solutions address genuine business pain points and can be integrated into operations smoothly. Management should encourage and ensure that senior executives share common goals that incentivizes them and their teams to collaborate and enable agility with the realization of various AI use cases.
Regulations
Regulatory complexity: Navigating GDPR and the EU AI Act
Europe, possibly, has one of the most stringent regulatory environments in the space of data and AI. Managing regulations even on a normal day is not easy and advent of AI brings its own set of complexities. The main relevant regulations are GDPR and the EU AI Act. GDPR imposes strict restrictions on the use of personal data and grants individuals’ rights regarding automated decision-making and hence implicitly restricts full machine-driven decision making for use cases that may be sensitive in nature. The EU AI Act adds another layer of complexity by classifying AI systems into risk tiers having corresponding obligations. For example, a high-risk system will have strict obligations around robustness, transparency, audits, and oversight. Banks can turn these regulations into their advantage with building a perception of trust with strict compliance.
Conclusion
Leaders have a critical role to play in helping their banks navigate as AI reshapes Europe’s banking landscape. If the journey of adopting AI at scale is overseen well, it will unlock the potential of AI. The map for the journey is clear: invest in people and culture; setup the guardrails; enable collaboration; uphold the vision. Successfully negotiating these exciting times can help a bank be well placed for the new AI powered era.