Artificial Intelligence (AI) is becoming a primary driver of financial innovation hubs in the global economy. These innovation hubs are frameworks developed around the context of interaction between households and firms through financial systems for direct and indirect lending and borrowing.
Artificial Intelligence (AI) is becoming a primary driver of financial innovation hubs in the global economy. These innovation hubs are frameworks developed around the context of interaction between households and firms through financial systems for direct and indirect lending and borrowing.
The interaction between households and firms comes through the language known as Data. Data provides tabular understanding to investment houses and banks to identify consumer trends in taste and fashion along with the systematic restructuring of financial spending and investment. The set algorithms continue the evolution of design, taking into account the behaviour of consumers, and the evolution of market mechanisms.
AI is built with data absorbing all these trends and descriptors that define the new changes in each economy’s mechanics. AI fuses programming frameworks with data collection systems and gives computing machines the ability to make decisions using the data being fed and often the data needs to be cleaned and filtered before being fed to the AI models that host the programmability of the computing processes to identify trends in the data and make predictions on it.
If data is the tabulated key to the way forward then the need to store, analyse, and work with this valuable instrument, comes from the global organisations offering technological support to empower the financial sector through the use of AI. Financial institutions are at the centre of this game at play, their ability to create frameworks to utilise the money for investment and returns is reliant upon how AI systems collect user data and analyse user trends. The utilisation of the mined data insights from financial trends defines the evolution of AI in financial markets using it as a driver for their growth for themselves and other financial institutions.
Today, SAP offers a large catalogue of AI-powered scenarios across all business functions of the financial services industry. Saquib Ahmad, country managing director at SAP Pakistan, Iraq, Bahrain & Afghanistan, sees immense scope for digitalisation in the banking and finance sector. Quoting a few international benchmarks, he says, “The Australia and New Zealand Banking Group Limited is transforming to compete with the new digital banks. They leveraged SAP AI to enable change and 360-degree monitoring and improvement. HSBC adopted SAP Signavio to transform its operating model for robust global operations. First Abu Dhabi Bank (FAB) chose SAP LeanIX based on its ability to visualize the business and IT landscape in a simple, straightforward way.”
With the use of AI, data can be identified to create new possible customer market bases and redesign services based on customer sentiments. It also allows businesses to restructure themselves from the management point of view for cost efficiency. Syed Amin Ur Rahman, chief digital officer at Faysal Bank says, “At Faysal Bank, we recognise that AI will have a transformative impact in potentially reducing costs and enhancing customer experience, hence the focus is now on front-end solutions with ML capabilities. Fraud detection and transaction authorisation solutions with neural capabilities have already been implemented at FBL but amongst other avenues, the future lies in creating a ‘near-human’ experience through NLP-based virtual assistants, something that not only adds efficiency but largely increases the customer experience when interacting with their bank.”
AI collects people’s data through automated data pipelines linked to devices and systems that people use daily. The framework of financial institutions comes under the umbrella of investment management platforms and interactive banking data mediums which would be mobile apps that people use to make and manage investments. All other applications and services linked to people’s bank accounts thus possess the risk of privacy of data.
The importance of ethical use of AI triumphs within this sphere as institutions have to maintain not only reliable customer management relationships but also safeguard the use of people’s data from falling into the wrong hands. This could create social and economic consequences for the customers in the advent of a cyber security compromise.
In 2024, we have much more advanced AI systems integrated and deployed in financial institutions, but the risk of complex human behaviour will never fade.AI recognition of such outlier patterns will always have limitations which is why financial institutions must consider building a framework of human and AI collaboration
AI can currently establish contextual reasoning for client data but it still lacks the computing power and algorithm effectiveness to create contextual quantitative aspects of ‘why does this data need to be used’ or ‘why do clients think like that’ and it still is a long way from becoming a self-sufficient employee in financial institutions while it has progressed to a point to become an effective computational tool for employees to use and manage the business of financial institutions more effectively.
Aleem Masood, head of IT at FINCA Microfinance Bank Limited, says, “AI-based systems are now helping banks reduce costs by increasing productivity and making decisions based on information unfathomable to humans. Also, intelligent algorithms can spot fraudulent information in a matter of seconds.
“At FINCA Microfinance Bank, we aim to use an AI system to monitor payment transactions in real-time, identifying and preventing potentially fraudulent activities. This proactive approach not only protects customers but also builds their confidence in the bank's security measures. Transactional behaviours are also leveraged in loan and credit decisions. In short, such AI initiatives are playing a key role in changing the future of consumer lending.”
AI within financial institutions is forecast to replace many investment analysts and managers with advanced chatbots that interact with customers and manage their portfolios. This creates a conflict between people management and technical automation. Interaction with customers and the creation of customised investment plans can be processed with data alone but convincing the customers to invest and agree with terms depends on the emotional management of clients. The storytelling skills of the investment analysts are where digital automation becomes more of a hindrance than a benefit for financial institutes as it cannot yet cater to that notion with AI systems because they work on limitation of data and algorithms being used.
Data to be used within financial institutions is ‘supervised’ or ‘unsupervised’. Supervised data involves active management of algorithm optimisations to better understand the data being fed to it, whereas unsupervised data is left on its own and the system can do whatever with the data without human supervision. Supervised is more commonly used within financial institutions because active compliance and governance of consumer data keep AI under control and ensure the data never goes out of management compliance. Since national governance of AI deployments does not exist yet in a system, the national institutions monitor what financial institutions are doing with AI and customer data on a live feed.
Javaid Sher Ali, head of IT/engineering at Raqami Islamic Digital Bank, says “AI is revolutionising industries and reshaping the way we interact with technology. Similarly, its usage in banks unlocks efficiency, innovation, and risk management, showing great potential for banks to streamline day-to-day operations and optimise decision-making. At Raqami Islamic Digital Bank (RIDBL), it is our top-line strategy to become a data-driven bank.
“Our strategy includes partnering with existing service providers operating in the market and offering their customers financial services with a personalised experience by using pass-on data from the partner. RIDBL is developing a financial data hub to process data in real-time using advanced algorithms and machine learning. Robotic Process Automation (RPA) is our first-day approach to executing routine processes with precision and speed, freeing human resources from tasks such as account opening, loan processing, etc. RPA streamlines operations and reduces operational costs. However, as a regulated bank, we are cognizant of the need to ensure ethical considerations, data privacy, and regulatory compliance.”
In 2012, Knights Capital Group suffered a loss of $440 million in an hour due to a fault incurred within its algorithm where the deployed AI executed many orders at the wrong prices. The following year, Goldman Sachs lost $100 million due to a similar error where AI made a glitch in the options market during order placement.
In 2024 today, we have much more advanced AI systems integrated and deployed in financial institutions, but the risk of complex human behaviour will never fade. AI recognition of such outlier patterns will always have limitations which is why financial institutions must consider building a framework of human and AI collaboration towards effective data management and business optimisation. Thus R&D within AI implementation within financial institutions may always ensure they stay updated with AI trends and developments for their business marketability.
The writer is a freelance contributor who is interested in technology and education. She can be reached at: hadiazaid2021@gmail.com
