The future of banking: Strategic integration of AI in financial services
Over the last couple of decades, the term “Artificial Intelligence” (AI) has transitioned from being the premise of a sci-fi thriller to becoming the most strategic component in a banking technology stack. I have had the privilege of working with an array of technology teams ranging from data science, machine learning, digital products, and IT […]
Over the last couple of decades, the term “Artificial Intelligence” (AI) has transitioned from being the premise of a sci-fi thriller to becoming the most strategic component in a banking technology stack. I have had the privilege of working with an array of technology teams ranging from data science, machine learning, digital products, and IT operations across different teams in the banking sector. It has been exciting to witness AI initiatives driving a complete transformation of the financial services industry from the bottom-up. In a digital-first era, banks are finally beginning to fully grasp the strategic significance and potential value of AI beyond simple automation and labour cost-savings. The next era of financial services is witnessing a transition in value; one where data and information is fast becoming the new money.
In a money-is-information world, a strategic shift in the technology that banks use is underway to drive how they create strategy, design financial services, build new relationships, and manage risk. The “digital transformation” race is intensifying in all areas of banking as part of the operational survival of the fittest in the global economy. From market to market, financial institutions now need to be digitally-ready to meet consumer demand for real-time digital-first customer experiences, increased speed and operational agility to take on new services, while also keeping pace with regulatory pressure for smart and cost-effective compliance frameworks that enable faster times to market for new products and services. To become digitally-ready and maintain market-relevance, banks and financial institutions need to incorporate the right balance of artificial intelligence, emerging technologies, and data-driven systems into the long-term DNA of their institution.
On the back-office side of banking, AI has already begun automating routine clerical operations such as document processing and preparation, system-wide compliance audits, and data cleansing operations. These improvements in AI automation minimise manual errors, help with faster service delivery times, and save costs on human resources for various compliance and documentation functions. Robotic Process Automation (RPA) married with machine learning is also starting to streamline complex repetitive manual processes such as loan applications, trade and payment validations, and transaction monitoring operations in many banks and financial institutions.
On the customer front of banking and financial services, AI is playing a pivotal role in helping to reinvent the way banks relate to their customers. Intelligent chatbots and conversational assistants with natural language processing are now able to handle basic customer requests and inquiries, guide and connect users to different financial services and lead generation funnels, and answer some basic financial advice to customers in real time. Furthermore, with a combination of pattern recognition and machine learning, AI systems are starting to become capable of drawing insights from customer behaviour and transaction history to inform customers and make more intelligent and personalised product recommendations. With mobile-banking and digital wallets exploding in recent years, customers now demand to be able to access banking and financial services from anywhere at any time, and AI is helping banks to deliver on that. With AI-augmented digital interfaces, banks are able to ensure accuracy, quality and high-standard of customer support, across all customer interaction points such as bank websites, mobile apps, and social media.
Security and fraud prevention are two more use cases. The risk of fraud is higher than ever before with more sophisticated schemes that old rule-based engines cannot easily identify. AI models can detect unusual behaviour based on real-time data and highlight potential risks. Some systems even use behavioural biometrics to strengthen cybersecurity and advanced anomaly detection techniques to stay ahead of new and emerging risks. Similarly, AI can improve regulatory compliance, such as Know Your Customer (KYC) and Anti-Money Laundering (AML) checks, through automation to reduce manual workload and improve risk assessments. The same AI technologies that help banks win are being used to secure and manage their compliance functions to identify higher risks, pinpoint specific indicators and to lower false positives so compliance teams can focus on high-impact cases.
Credit risk management is another example of this type. AI will enable banks to use alternative data for credit scoring such as transaction patterns, mobile usage, social behaviour, and much more. This could lead to a more fair and complete picture of a borrower’s ability to repay and lead to a more informed and accurate view of creditworthiness. By using AI to gain a more complete view of a customer’s financial health, banks may be able to reduce loan losses and extend credit to underbanked populations. The early warning signs of financial distress are easier to spot and track which means AI can also help banks better manage and control lending portfolios and head off future delinquencies. Proactively identifying customers who are on a trajectory for default, well before the 30 or 60-day mark for missed payments, will empower banks to take proactive steps to reduce losses by offering timely and appropriate interventions to customers such as payment restructuring or financial counseling while also deepening and improving customer relationships.
The future of AI in banking is almost certain but it is up to individual institutions to work out how to unlock its potential and do so quickly, safely and ethically. Those that are willing to look ahead and innovate with an intelligent strategy, a focus on purpose and a commitment to responsible innovation, will lead in the future of banking. Human and machine will work together to create a smarter, safer and more accessible financial services ecosystem that can be more agile, creative and innovative in meeting the needs of a digital economy. The benefits of AI can allow banks to gain operational efficiencies, tap into new markets, and drive better business outcomes for the entire organisation and for the communities and customers they serve.
Eseoghene is a Digital Innovator in the Banking Industry