In 2023, the spotlight shone brightly on generative AI and other robust language model-based (LLMs) tools, as they not only proved to be lucrative for the tech sector but also catalysed a transformative wave throughout the global economy.
There isn’t any sector, which got affected by the disruptive innovation. For example, take the banking sector, you have technology changing the game called ‘Customer Interactions’. In 2020, a study on AI in Financial Services conducted by the World Economic Forum in collaboration with the Cambridge Centre for Alternative Finance at the University of Cambridge Judge Business School found that 85% of the surveyed financial services are utilising AI in some form within their company.
Jump forward in 2024, financial institutions have understood the need to adapt to the rapidly evolving technological landscape and gain the market edge. The stakeholders need to carry on the push by keeping the investments time and capital going.
Innovations galore
As per a September 2023 McKinsey study, while corporate and investment banks (CIBs) are using AI at scale and reaping enormous benefits, the overall industry lags very much, when it comes to embracing technology, as many banks are using “bespoke, artisan-like approaches that are inherently less productive”.
“Bankers often see areas across the front, middle, and back offices as too complex to use machine learning. A few leading banks have made AI-related progress in some of these areas, including relationship manager (RM) support and advisory, compliance and risk decisions, and client service on complex bespoke products (think foreign-exchange hedges on forward commodities agreements),” the study commented further.
McKinsey Global Institute (MGI) now estimates that across all of the banking, wholesale, and retail sectors, generative AI will add between $200 billion and $340 billion in value through greater productivity.
In fact, in 2023, we saw Ant Group launching a financial Large Language Model, which is a specialised language model fine-tuned for AI applications in the financial services industry. The innovation also surpassed the existing general-purpose LLMs in key areas like cognition, generation, domain knowledge, professional thinking and compliance.
Ant Group has trained the financial LLM on an extensive dataset, which includes hundreds of billions of token datasets containing Chinese financial documents and over 1,000 billion tokens from general corpus datasets. The new tool has additionally incorporated a dataset of over 600,000 instructions from real-world industry use cases.
Then in November 2023, came a new LLM solution called ‘Slope TransFormer’, specifically trained to understand the language of banks.
In 2023, Swiss enterprise software giant Temenos launched an industry-first secure solution for banks using generative AI to automatically classify customers’ banking transactions, which will help banks provide personalised insights, recommendations, engaging and intuitive digital banking experiences to society, apart from enhancing customer loyalty programmes through more relevant products and offers.
Smart strategy needed?
As the global economy is undergoing its ‘Technological Renaissance’, the question here is ‘Will human jobs end up getting replaced by machines?’ Well, in sectors like banking, the need of the hour is ensuring a ‘Smart Automation’, as reflected in the innovations brought by Ant Group, Temenos and Slope TransFormer, where LLMs are not only taking over the daily mundane tasks but also helping to make functions like customer loyalty programmes ‘smart’ ones for the banks.
Through the tech’s helping hand, human professionals are reading through and classifying customers’ banking transactions, to understand the latters’ banking behaviour and draw up personalised customer loyalty programmes.
As per Chris Tapley, Vice-President of the Financial Services Consulting at the US-based EPAM Systems, financial ventures need to pay attention to the challenging economic environment that is pressuring them to protect the bottom line while delivering the quality and scope of services customers expect.
“Therefore, many banks must take direct and deliberate steps to significantly revise their technology stacks and operational processes to control current costs, optimise near-term revenue and position themselves for future growth,” Tapley stated further in his article, written for the Global Banking and Finance Review.
‘Automated’ road ahead?
Tapley predicts the banking sector’s automation efforts to follow the path called ‘Optimal Implementation’.
While automation will reduce the cost of critical processes, the phenomenon itself will require modernisation, especially in the domain of ‘Underlying Technology Infrastructure’. Tapley believes that there are challenges associated with using AI and automation in the finance sector.
These include ‘regulatory compliance issues’, ‘data privacy concerns’ and the ‘potential for bias/discrimination’. He is pitching for the industry emphasising responsible and ethical usage of the technology.
Tapley says careful planning and execution are the keys to success, when it comes to the banking sector’s automation efforts. The banks need to create essential investment areas to effectively implement automation and create seamless, personalised customer experiences.
Firstly, financial services providers should focus on RPA (Robotic Process Automation) as it will help these ventures streamline repetitive and time-consuming tasks, thus improving operational efficiency and reducing human errors. Tapley sees this course correction automating routine processes like loan processing, account opening, and customer onboarding.
Secondly, banks focussing on RPA will allow their employees to invest more time in strategic tasks like personalised interactions with the customers, apart from analysing market trends, developing new commercial strategies and making decisions to keep the financial services provider competitive.
As per Tapley, artificial intelligence and machine learning will be fundamental to the successful implementation of automation in the financial industry.
“These technologies (AI and ML) can be used in various aspects of banking, including fraud detection, customer service, credit risk assessment and personalisation. Banks should allocate resources for researching and developing in-house AI and ML solutions or partner with dedicated vendors to stay ahead in the swiftly evolving landscape. It is also important to note that all generative AI models should serve as assistive tools, not the sole decision maker,” he stated further.
On the potential of tech transforming the personalisation aspect of banking, Tapley bats for investing in ‘Digital Customer Experience’. Implementing AI-powered customer support chatbots, enhancing the quality of baking apps and leveraging advanced analytics for personalisation will do wonders for the industry.
“After a virtual assistant verifies the customer’s identity, a customer can communicate with these chatbots in real-time and receive details on their accounts that would otherwise require human attention. Financial services can increase customer satisfaction, loyalty and revenue by prioritising the digital customer experience,” Tapley commented further.
The key ‘I’ word
Talking about the banking sector and automation, implementing 21st century breakthrough technologies will require the overhaul of the legacy systems. Having a computer on every desk inside the building (backed by a centralised server) won’t make a financial venture ‘Future Proof’. Their infrastructure game should and must embrace cloud-based technologies, and API-driven architectures, which will be friendly towards the integration of automated solutions like AI-powered personalisation tools.
Also having AI-powered personalisation solutions, for example, will only make sense if the banks back them by investing in data management systems and advanced analytics tools capable of collecting and analysing vast amounts of data in a very short period.
“This will enable them to gain important insights, make informed conclusions, and improve the accuracy of their predictive models, leading to better personalisation and customer experiences,” wrote Tapley.
We all know the impact the fintech (financial technology) companies have made in the market, be it heavily investing in technology or disrupting the financial sector by introducing products and services that are tailor-made as per the customers’ needs (using solutions like AI and ML).
While the fintech ventures have been successful in challenging their legacy counterparts in the last few years, the latter should and must collaborate with these disruptive start-ups and other technology providers to accelerate their automation efforts.
These partnerships will help the banks to benefit from innovative solutions and expertise that may not be available within their existing rulebooks. Fintech companies are known for marketing and diversifying their offering and enhancing customer experiences in the blink of an eye, something which has been only possible due to the ventures heavily investing in breakthrough solutions like AI and data analytics.
Teaming up with fintech companies will only benefit the legacy banks as the latter will be able to improve their AI and personalisation tools, which, in the long run, will help them to offer highly customised and responsive services to their customers.