AArtificial intelligence (AI) is becoming one of the key drivers of financial innovation hubs in the global economy. These innovation hubs are frameworks developed in the context of interaction between households and businesses through financial systems of direct and indirect lending and borrowing.
The interaction between households and businesses happens through the language known as data. The data provides tabular understanding for investment companies and banks to identify consumer trends in taste and fashion as well as systematic restructuring of financial spending and investments. The defined algorithms continue the evolution of the design, taking into account consumer behavior and the evolution of market mechanisms.
AI is built with data absorbing all these trends and descriptors that define new changes in the mechanisms of each economy. AI merges programming frameworks with data collection systems and gives computing machines the ability to make decisions using the fed data. Often, data needs to be cleaned and filtered before being fed to AI models that host IT process programmability to identify trends. into the data and make predictions about it.
If data is the key to the way forward, then the need to store, analyze and work with this valuable instrument comes from global organizations offering technological support to empower the financial sector through the use of AI . Financial institutions are at the center of this game, their ability to create frameworks for using money for investment and returns depends on how AI systems collect user data and analyze trends in users. The use of data extracted from financial trends defines the evolution of AI in financial markets, using it as a driver of their growth for themselves and other financial institutions.
Today, SAP offers a broad catalog of AI-driven scenarios for all business functions in the financial services industry. Saquib Ahmad, country general manager at SAP Pakistan, Iraq, Bahrain and Afghanistan, sees immense opportunities for digitalization in the banking and financial sector. Citing some international references, he said: “Australia and New Zealand Banking Group Limited is transforming to compete with new digital banks. They leveraged SAP AI to enable change, 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 due to its ability to visualize the business and IT landscape in a simple and straightforward manner.
Through 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 from a management perspective with profitability in mind. Syed Amin Ur Rahman, Chief Digital Officer at Faysal Bank, said: “At Faysal Bank, we recognize that AI will have a transformative impact by potentially reducing costs and improving customer experience, which is why the focus is now focused on front-end solutions with ML capabilities. Fraud detection and transaction authorization solutions with neural capabilities have already been implemented at FBL, but among other avenues, the future lies in creating a “near-human” experience through virtual assistants based on NLP, which not only adds efficiency but significantly increases the number of clients. experience when interacting with their bank.
AI collects people’s data through automated data pipelines linked to the devices and systems people use every day. The framework of financial institutions falls under the umbrella of investment management platforms and interactive banking data carriers which would be mobile applications that people use to make and manage investments. All other applications and services linked to people’s bank accounts therefore present a data confidentiality risk.
The importance of ethical use of AI triumphs in this area, as institutions must not only maintain reliable relationships with customer management, but also prevent the use of people’s data from falling into the wrong hands . This could have social and economic consequences for customers if cybersecurity is compromised.
By 2024, we will have much more advanced AI systems integrated and deployed in financial institutions, but the risk of complex human behavior will never subside. AI recognition of these aberrant patterns will always have limitations, which is why financial institutions should consider building a human intelligence framework. and AI collaboration
AI can currently do contextual reasoning for customer data, but it still lacks the computing power and algorithmic efficiency to create contextual quantitative aspects of “why should this data be used” or “why do customers think that way” and that still takes a lot of time. far from becoming an autonomous employee in financial institutions, as it has progressed to the point of becoming an effective IT tool for employees to use and manage the activities of financial institutions more effectively.
Aleem Masood, Head of IT at FINCA Microfinance Bank Limited, said: “AI-based systems are now helping banks reduce costs by increasing productivity and making decisions based on information unfathomable to humans. Additionally, intelligent algorithms can detect fraudulent information in seconds.
“At FINCA Microfinance Bank, our goal is 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 behaviors are also leveraged in lending and credit decisions. In short, such AI initiatives play a key role in changing the future of consumer credit.
AI within financial institutions is expected to replace many analysts and investment managers with advanced chatbots that interact with customers and manage their portfolios. This creates a conflict between people management and technical automation. Interacting with customers and creating personalized investment plans can be handled with data alone, but convincing customers to invest and agree to the terms depends on emotional management of customers. The narrative skills of investment analysts are where digital automation becomes more of a hindrance than a benefit for financial institutes, as it cannot yet address this notion with AI systems as they work on limiting data and algorithms used.
Data for use within financial institutions is “supervised” or “unsupervised”. Supervised data involves actively managing algorithm optimizations to better understand the data fed to it, while unsupervised data is left to its own devices and the system can do whatever it wants with the data without human supervision . Supervised is more commonly used within financial institutions because active compliance and consumer data governance keeps AI in check and ensures that data never falls outside the scope of management compliance. Since there is no national governance of AI deployments in a system yet, national institutions are monitoring live what financial institutions are doing with AI and customer data.
Javaid Sher Ali, Head of IT/Engineering at Raqami Islamic Digital Bank, says: “AI is revolutionizing industries and reshaping the way we interact with technology. Likewise, its use in banks unlocks efficiency, innovation and risk management, demonstrating great potential for banks in streamlining daily operations and optimizing decision-making. At Raqami Islamic Digital Bank (RIDBL), our core strategy is to become a data-driven bank.
“Our strategy includes partnerships with existing service providers operating in the market and offering their customers financial services with a personalized experience using data transmitted by the partner. RIDBL is developing a financial data hub to process real-time data using advanced algorithms and machine learning. Robotic process automation (RPA) is our day-one approach to executing routine processes accurately and quickly, freeing human resources from tasks like account opening, loan processing, and more. RPA streamlines operations and reduces operational costs. However, as a regulated bank, we are aware 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 an error in its algorithm where the deployed AI executed many orders at wrong prices. The following year, Goldman Sachs lost $100 million due to a similar error in which AI glitched in the options market while placing orders.
Today, in 2024, we have much more advanced AI systems integrated and deployed in financial institutions, but the risk of complex human behavior will never subside. AI recognition of these aberrant patterns will always have limits. This is why financial institutions should consider creating a collaboration framework between humans and AI for effective data management and business optimization. Thus, R&D in the implementation of AI within financial institutions can always ensure that they stay abreast of the trends and developments in AI for the commercialization of their business.
The writer is an independent contributor interested in technology and education. She can be contacted at: hadiazaid2021@gmail.com