When OpenAI released ChatGPT in November 2022, businesses in banking and finance quickly recognised the commercial potential of Generative AI (GenAI). However, due to the AI technology’s nascent qualities, archaic legacy systems and a lack of established strategies for integration, leaders have struggled to translate GenAI into greater revenues.
Two years on, the landscape is finally taking shape. According to PwC, 70% of global CEOs now expect GenAI to significantly reshape how their operations create value. Furthermore, more than two-thirds are already working with AI, having reworked their tech strategies to align with AI-driven opportunities.
Of course, the banking and finance sector is no stranger to technological change. The first plastic credit card was introduced in 1959, by American Express. The ATM was launched in London by Barclays Bank. And today, mobile banking, investing and high-level financial management can be done by any smart device nestled in a person’s pocket.
However, as with any new frontier tech, GenAI has its risks: implementation challenges, upskilling, regulatory and ethical considerations—these risks are heightened in finance and banking. And there’s the classic possibility of simply getting it wrong. Plus, what’s hot in GenAI today may be old news tomorrow.
To help organisations drive change within, let’s explore the good, the bad, and the ugly of GenAI adoption through the lens of recent insights from CI&T research and case studies.
The Good side of GenAI
The analogy between the Old West and GenAI holds up: both involve exploring new territories, uncovering valuable resources, and building infrastructure. Today, these frontier outposts are becoming cities, and full-scale reinvention is on the horizon for financial institutions.
So, what’s the new gold rush? According to CI&T’s new research, The Future of Finance: How AI is powering the intelligence era, the answer is ‘hyper-personalisation.’ This field is ripe, with fintech firms using it to deliver two key benefits: bespoke services and rapid issue resolution.
Using Customer Data Profile software—tools that gather and standardise data from online and offline sources to create detailed customer profiles—GenAI is helping these firms take personalisation to new depths. This can enable bespoke services in real-time. Indeed, McKinsey reports that personalisation drives profit: companies that prioritise it achieve growth rates 40% higher than their peers. For example, it enables institutions to offer solutions that foster smarter money habits among customers. This can be done by aligning services with consumption patterns and inflationary trends. This strengthens customer loyalty while driving cross-selling opportunities. Similarly, by facilitating enhanced financial decision-making, financial institutions can provide tailored advice and tools that differentiate their services in a competitive market, boosting retention rates.
On the investment side, hyper-personalisation creates avenues for smart investment moves by delivering customised strategies aligned with individual risk profiles. This not only attracts more customers but also improves portfolio performance, translating into increased asset management fees and long-term profitability.
GenAI is also giving businesses the gift of time. By 2030, up to 30% of current hours worked could be automated. For example, in the financial sector, portfolio managers are using GenAI to automate routine performance and risk reports. Hyper-personalisation could lead to strategies tailored to individual risk appetites, the latter being a revenue opportunity.
The Bad with GenAI
GenAI is like the newest member of the crew, full of promise but with some questionable traits. Without oversight, it can enable manipulation, misinformation, and privacy breaches. The tech, unmanaged, can also be prone to biases and inaccuracies. Often presenting errors as facts, adding pressure on teams to manage them. Moreover, it poses a security risk, requiring businesses to safeguard their data, or risk being ‘robbed in the night.’
To manage these risks, GenAI is increasingly subject to complex regulations. Gartner predicts that by 2026, 50% of governments will introduce regulations and policies to enforce the responsible use of AI. These challenges will be even more significant in banking and finance.
Balancing the pros and cons of GenAI is the key to extracting value. GenAI itself can often help. For example, CI&T assisted fintech firm Bulla, which specialises in flexible credit and benefits, with managing common complaints. Using our enterprise-ready GenAI platform, CI&T FLOW, Bulla analysed customer service data to gain a detailed view of pain points and rethink support systems. They also used it to give employees access to essential information and to train staff in GenAI.
The Ugly side of Artificial Intelligence
When the going gets tough, our relationship with GenAI can take an ugly turn if outdated legacy systems stand in the way. The challenge of digging through impenetrable layers, reworking outdated processes, extracting valuable data, and training staff accustomed to old ways of working is no easy feat. Moreover, the costs can quickly add up.
Historically, banking has been one of the sectors worst affected by legacy hardware. Nearly six in ten bankers see their legacy systems as a major business challenge, describing them as a ‘spaghetti junction’ of interconnected but antiquated technologies. So, much like digging through rock in search of gold, the rigid hardware architectures designed for specific banking functions—based on old programming languages and databases—are holding back innovation. In fact, 60% of executives cite skills gaps as an obstacle to overcome in their digital transformations.
The banking sector may be on the brink of a breakthrough. We’re starting to see more AI-driven chatbots, fraud prevention, and the speeding up of time-consuming tasks such as developing code and summarising reports. However, it’s updating the legacy hardware where the real commercial value lies.
Ironically, GenAI holds the key. For one of CI&T’s leading clients, a large global bank based in South America, CI&T FLOW was able to modernise its systems by supporting the transition from COBOL to Python using a code refiner. This resulted in accelerated developer delivery, a 54% lead time reduction, and a 33% improvement in user story quality. Highlighting the power of strategically harnessing the technology. The challenge is also the solution.
As the world of GenAI transitions from Wild West to civilised modernity, businesses are going to have to get smart about how they look for commercial value. Often, the solution lies in GenAI itself. So, get started, and get started now. And in the immortal words of Clint Eastwood’s Blondie: “Two hundred thousand dollars is a lot of money. We’re gonna have to earn it.”
To learn more about how CI&T can help your business commercialise GenAI, download The Future of Finance: How AI is powering the intelligence era here.
- Artificial Intelligence in FinTech
