The rise of business AI
The adoption of AI in banks has been stimulated by generative AI. But is this just another short-lived and overhyped technology? Or is this really the start of a new revolution in financial services?
The financial services industry has seen several similar moments over the past decade, but not all of them have led to real transformation. For example, in 2017, blockchain led many to believe it would completely revolutionize banking, but almost a decade later it remains a theoretical interest.
A more recent example is the metaverse, which has also generated a lot of excitement but has not yet seen substantial adoption. Research of the Infosys Bank Tech Index: Part 3 shows that both these technologies account for less than 5% of banking technology and recruitment budgets.
AI: Hype or reality
With the consumerization of AI, through the likes of ChatGPT, individuals have begun to experience AI on a personal level, reminiscent of the mobile phone revolution. Bulky landline telephones were replaced by functional telephones, which made way for smartphones. AI will likely travel the same journey, but at a much faster pace. Moreover, the adoption of AI by companies has increased. According to the Infosys Bank Tech Index, technology now accounts for 31% of banking technology budgets, compared to 20% previously.
But how do we know that the recent push in AI isn’t just hype? There are three factors to consider if a technology’s spark is short-lived or at the peak of a new era.
1. Business value is created
Emerging research shows that AI could increase revenues in the banking sector, and research from the Infosys Knowledge Institute also shows that financial institutions are leading the way in generating business value from AI, with 16% reporting that they already have done. by 13% compared to the average of other sectors.
But it’s still early days and much of the industry is still experimenting. Use cases range from software development and managing negative media (Deutsche Bank) to analyzing Federal Reserve speeches and detecting fraud (JPMorgan) and even personalized financial advice and recommendations (Morgan Stanley).
These use cases are attracting investors’ attention: Generative AI spending by financial institutions is expected to increase by 164% by 2024. By 2026, global AI spending on systems, services and platforms will reach $300 billion, according to IDC, and the financial sector will reach $300 billion, according to IDC. is expected to hold the lion’s share.
2. Positive social impact
For example, digital transformation in India has led to greater financial inclusion, with almost 78% of the population having access to banking – up from just 35% a decade ago.
But there is still work to be done here. Many people still struggle to use mobile banking applications due to reading difficulties, language barriers, age or physical limitations. AI can support these groups with voice banking and personalized financial advice. AI can also be used to infer the creditworthiness of the unbanked population by analyzing alternative data for credit scores, especially for those with limited to no credit history. This will boost microcredit while promoting financial inclusion.
3. Regulations are catching up
Regulatory compliance is paramount for financial institutions looking to deploy AI and put AI first. But technology is evolving so quickly that most regulators are still playing catch-up. While this is concerning, efforts are underway to address concerns about bias, discrimination and other ethical nightmares associated with improper use of the technology. Even as regulations for AI are still evolving, governments and regulators have begun introducing regulations Executive Order of the United States Governmentthe EU AI lawand that of Great Britain Guidelines for the development of secure AI systems – so companies can build ethical, responsible AI.
In turn, institutions must integrate ethical considerations into design and architecture by developing a responsible design framework for ethical AI use. For example, in the mortgage or loan underwriting process, regulators require an audit trail and information must be recorded as to why the decision was made, what parameters were considered and whether decisions were made without any bias. With the right systems, AI can make better, faster and fewer incorrect decisions than a human. AI can eliminate inherent human biases and make decisions in an ethical and responsible manner.
The challenges that hold technologies back
Companies face several hurdles when it comes to AI adoption. The technologies that make AI so powerful are the cloud and data. The cloud provides a dynamic and efficient foundation by enabling access to computing resources, storage and innovative services. Data is critical for financial institutions to train AI models. Yet institutions face obstacles in the form of data quality, accessibility and governance. Ensuring their AI systems don’t violate privacy, prevent bias from creeping in, and stay secure keeps corporate CXOs awake at night.
Sustainable energy consumption, costs and regulations are also challenges when it comes to generative AI. These were some of the many challenges that made blockchain and metaverse unfeasible.
This is different with generative AI. Although regulators are slow, they are more forthcoming with regulations. Large language models have given way to the emergence of targeted and specific narrow transformers, which make energy and costs sustainable. The right talent is the foundation for building resilient, compliant, and secure AI systems. Yet the Infosys Bank Tech Index found that AI and cybersecurity talent are the most difficult skills for companies to recruit.
What does the future hold for AI?
The interesting dichotomy with AI is that it can automate hacking and thus bypass traditional security – but it can also strengthen security by detecting anomalies, predicting threats and monitoring in real time. Institutions must continually adapt to stay ahead of the risks that could shape the future of the industry by addressing data privacy, integrity and fairness.
While the jury is still out on who will emerge victorious among skeptics and futurists, we believe that in five years, AI will solve complex business problems – and at scale. The technology still has a long way to go as it faces many challenges. AI is not yet providing early warning signals when it comes to risks, while fraud and cyber threats continue to increase. AI should also improve decision-making for portfolio analysis and credit decisions. While the technology will become mainstream within financial institutions, now is the time to put together an integrated AI-based strategy and experiment and evolve to build competitive differentiation and realize the bank’s business objectives.
Dennis Gada
Executive Vice President and Global Head of Banking & Financial Services | Infosys
As Global Head of Banking & Financial Services at Infosys, Dennis leads the largest business unit within Infosys together with his Global Financial Services Executive Leadership team. He is a board member of EdgeVerve Systems Limited, a products and platform subsidiary of Infosys and Infosys Compaz Pte. Ltd (iCompaz), a joint venture between Infosys Limited and Temasek Holdings. In his current role, Dennis is responsible for the strategic direction, growth, operational excellence and all commercial and tax management of the Global Banking & Financial Services business. With over twenty years of professional experience in APAC, Europe and the US, Dennis currently lives and works with his family in Charlotte, North Carolina.
