Industry
Banking
Banking Services Transformation: Unlocking Exponential Value
Highlights
- Banks and financial institutions have benefited from the adoption of AI through improved efficiencies, better customer experience, and reduced costs.
- In the financial services industry, GenAI has moved past the sandbox environment given its potential to bring in disruptive transformation across various areas.
- Successful implementation will demand a well-defined strategy to overcome challenges around security, privacy, and ethical GenAI use, helping banks to differentiate themselves and unlock new value.
On this page
The AI journey so far
The banking, financial services, and insurance (BFSI) industry recognizes the transformative potential of artificial intelligence (AI).
This is corroborated by the BFSI Report of the TCS AI for Business Study which reveals that 56% of senior executives believe AI’s impact will be greater than or equal to cloud computing while nearly two-thirds are optimistic about its impact on their business.
Over the past decade, the banking and financial services (BFS) industry has adopted multiple new technologies such as cloud, application programming interfaces (APIs), blockchain, AI, and machine learning (ML). While the pace of adoption of many of these technologies has been moderate, financial institutions have leveraged AI technologies in several areas, particularly in front-, mid-, and back-office operations, data analytics, payments, and fraud management, over the last few years (see Table 1).
Table 1: The AI journey in the BFS industry
In addition, the rise of generative AI (GenAI) technology with the capabilities to process large data sets, detect patterns, and generate new insights with the potential to reimagine specific business processes is gaining traction in the BFS industry. GenAI has immense transformational potential across various areas of banking (see Table 1)—in our experience, GenAI implementations have moved past the sandbox environment.
Going forward, the BFS industry will see increased adoption of AI as evidenced by the study. The B2C Banking report reveals that 66% of banking executives are primarily focused on using AI to innovate and generate revenue while 34% are interested in using it for cost and efficiency optimization (see Figure 1).
Figure 1: Drivers for AI adoption
The future is GenAI
Traditional AI and ML models have already benefited the BFS industry through improved productivity.
GenAI, however, has caught the attention of not just the industry, but the public at large. Though the prevailing macro-economic environment has induced caution in IT spends, CXOs are bullish about initiatives that leverage a combination of GenAI, predictive AI, ML, and analytics given their potential for a disruptive, transformative shift through direct impact on efficiencies, costs, and customer experience.
GenAI will equip banks and financial institutions with the capabilities to redefine their customer interaction models in the business-to-consumer (B2C) and business-to-business (B2B) spaces. With GenAI, identifying new customer segments, launching innovative marketing and sales campaigns to enhance brand position, and optimizing costs and efficiencies will become much easier. In addition, it will enable banks to deliver hyper-personalized customer service and increase stickiness, thereby enabling them to stand out in the highly commoditized financial services industry.
GenAI can transform multiple functions and sub-domains in financial institutions by enhancing the existing AI capabilities to deliver competitive advantage through better business decisions, superior customer experience, and improved revenue management, among others (see Figure 2).
Figure 2: GenAI opportunity canvas in BFS
Challenges to implementation
As with any emerging technology, GenAI brings its own challenges.
This is primarily because the technology is at a nascent stage and evolving rapidly. The study has identified the top three challenges that hamper the overall progress of GenAI efforts in the B2C banking industry (see Figure 3).
Figure 3: Top ranked GenAI concerns in banking
- Lack of IT readiness: According to the study, 43% of B2C executives believe that over half their employees will be using GenAI regularly in the next three years. Efforts to adopt GenAI has highlighted the limitations in their existing IT infrastructure. Most banks operate with legacy applications, manual processes, and tightly coupled systems that compromise their ability to adopt GenAI technology and can potentially lead to integration challenges. In the absence of a golden source of data, GenAI models are unable to extract meaningful actionable insights. In addition, as banks use external data from the partner ecosystem to design meaningful offerings, the existing infrastructure may not be sufficiently secure to prevent data leakages and cyberattacks.
- Security and privacy: GenAI implementation requires access to significant amounts of data, giving rise to security and privacy concerns. Ensuring the security and confidentiality of large volumes of data will pose difficulties. Furthermore, the existing security infrastructure in banks may not have the capabilities to manage the cyber vulnerabilities that GenAI adoption will introduce. Sustained effort will be need to ensure GenAI models meet prevailing data privacy norms and comply with upcoming regulatory requirements around the use of GenAI across Europe, the US, and China.
- Digital ecosystem participation: The ecosystem of technology players, cross-industry players, and other stakeholders is not yet mature enough to support the adoption of GenAI technology. As the ecosystem evolves, banks will have to foster new collaborative partnerships with peers, adjacent industry players, fintechs, and technology partners to create an ecosystem that will help drive adoption.
In addition to those identified by the study, there are other challenges that the BFS industry must overcome to successfully adopt GenAI:
- Executive sponsorship: Leadership buy-in is crucial to the success of large transformation programs and GenAI adoption will be no exception to this. Given that value realization might not be immediate, convincing the leadership to prepare for the long haul may not be easy.
- Talent management: GenAI models are still evolving and there is limited availability of human talent with these niche skills.
- Regulations: Given GenAI is still evolving, there is significant regulatory ambiguity on its use. Regulations vary across regions, and banks must navigate the complexity that governs data usage in AI applications. Some of these regulations such as those concerning privacy or data protection can negate banks efforts to develop certain use cases.
- Ethical and responsible AI use: The fairness of GenAI models depends on the data used to train them. However, accessing diverse and inclusive data to ensure transparent and unbiased algorithms can be a challenge. In addition, banks will need to monitor how the insights generated by GenAI models are used in decision-making. Banks will need to invest in establishing robust governance frameworks to ensure accountability and responsible use of data as well as sufficient assessment measures to monitor how the insights generated are used.
An action plan for success
To successfully integrate GenAI into their operations, banks and financial institutions must consider the following approach.
Roadmap
- Form a steering committee with members from top management to define the business and operating model in alignment with the strategic vision—this will ensure support from the leadership from idea incubation to implementation.
- Adopt a fail fast approach to measure returns and alignment with business value realization—this will help retain leadership buy-in through the implementation process.
- Identify functions, domains, and use cases suitable for GenAI adoption based on organizational priorities and business objectives—competitive pressure must not influence these decisions.
- Train the workforce in the use of GenAI tools as well as on ethical considerations; consider a collaborative approach to help mitigate talent cost and availability challenges.
Technology and infrastructure readiness
- Adopt scalable GenAI platforms that can manage large datasets and complex algorithms.
- Utilize cloud technologies for data storage and processing to enhance agility and scalability, enabling efficient management of GenAI workloads.
- Invest in robust data management and governance systems to ensure quality data.
- Establish comprehensive data governance and ensure a golden source of data for GenAI models to generate insights.
- Fortify core systems and processes to prevent cyberattacks and data breaches as well as manage cyber risks due to vulnerabilities that GenAI may introduce.
Privacy and security
- Reconfigure customer-facing applications to include consent management processes.
- Ensure compliance with geography-specific data privacy regulations.
- Establish additional controls and security measures such as data encryption and anonymization.
Digital ecosystems
- Nurture partnerships and collaborations with peers, technology providers, fintechs, academia, large technology firms, and cross-industry players to foster widespread GenAI adoption.
- Explore collaborative models to derisk from upfront large scale investments and cocreate journeys that will enrich partners and customers.
Talent
- Train and upskill employees in GenAI and data science to equip them to work with advanced GenAI tools and technologies.
- Engage with technology partners and academic institutions to access cutting-edge GenAI research and innovations.
Ethics and governance
- Implement ethical guidelines for GenAI usage to ensure transparency, fairness, and accountability in GenAI-driven decisions.
- Put in place sufficient checks and balances to curb biases, disinformation, and negative sentiments.
- Establish governance frameworks to oversee GenAI initiatives and ensure alignment with business objectives and evolving regulatory frameworks.
The way forward
Banks and financial institutions operate in an environment of perpetual change.
Global economic shifts, geopolitical tensions, sustainability demands, new legislations, and rapid technological advancements constantly impact the environment in which they operate. To become a frontrunner in this complex and dynamic environment, leveraging emerging technologies like GenAI will be a key factor.
In the highly commoditized financial services industry, customer experience is a vital differentiator, and GenAI can help banks distinguish themselves through hyper-personalization leading to customer delight. We believe that banks must move fast to adopt GenAI technology for first movers will have the opportunity to lead through differentiation in product innovation and customer experience, in turn creating new business value.
