Artificial Intelligence (AI) has had a profound impact on a wide range of industries, and the banking industry has started a significant digital transformation journey since its introduction. AI modelling refers to the development and training of machine learning (ML) algorithms that make logical decisions based on given data. These models serve as a basis to enable sophisticated techniques, such as real-time, predictive, and augmented analytics. Its ability to analyse vast amounts of data and identify patterns that would be difficult for humans to detect has revolutionised the industry.
The integration of AI into banking operations has led to significant changes in the way customers interact with banks and financial institutions (FIs), resulting in substantial improvements in customer service, fraud detection, and risk management. The technology has enabled banks to provide personalised services and tailor their products to meet the unique needs of individual customers, while also anticipating their customers’ needs and offering them relevant products and services.
However, there are still challenges that the banking industry must overcome in adopting AI. These include data quality, legacy system compliance, and ethical concerns to remain competitive in the market. Let’s explore the applications and challenges of adopting AI, and how the banking industry can address them to maximise the potential benefits of AI.
What Are the Applications of AI Models in Banking?
Fraud Detection and Prevention
Financial fraud, such as credit card fraud and online bank payments, is a huge issue for banks and FIs. In 2021, the US Federal Trade Commission recorded more than 2,864,250 cases of financial fraud, with credit card fraud topping the list, followed by payment app and debit card fraud. Banks can utilise ML algorithms to identify patterns and detect fraudulent activities, preventing financial losses and protecting customers’ data.
With AI and ML, banks can also enhance their anti-money laundering efforts. It is estimated that the European financial sector spends around €100 billion to uncover dirty money in the global financial system, but this amount is largely unproductive, with less than 1% of the money being seized. When paired with high-quality datasets, AI and ML can efficiently help banks detect suspicious activities by identifying behaviours, patterns, and connections with greater precision, at a much lower cost. Not only that, AI models are capable of learning and adapting to the latest criminal activities. This can help banks prevent money laundering and other financial crimes.
AI-powered chatbots can provide customers with 24/7 support and assistance. These chatbots can answer frequently asked questions, provide personalised recommendations, and help customers with their banking needs. They can also escalate complex issues to human agents, reducing the workload on customer service teams.
AI-powered Investing Advisors
J.P. Morgan reports that AI and ML technologies are now capable of independently identifying high-performance trading strategies using raw data, enabling fund managers to identify and make specific investment predictions much earlier as compared to traditional investment models. Moreover, investment banking is undergoing a significant change with the increasing adoption of ML technologies. Leading firms such as Bank of America, J.P. Morgan, and Morgan Stanley are investing heavily in the development of automated investment advisors, highlighting the industry’s shift towards automation.
Alternative Credit Scoring
AI-powered credit scoring systems can unearth hidden relationships between variables that are not always apparent to legacy credit scoring systems, which look at one variable at a time. Banks can now expand their reach to underbanked and unbanked customers with the help of AI-powered credit scoring systems. These systems can analyse alternative data sources like utility payments, rental payments, shopping history, mobile data, and more to evaluate customers’ creditworthiness. With these advanced systems, banks can make more informed lending decisions and provide more accurate credit scores.
What are the Challenges of Adopting AI Models in Banking, and How Are Banks Addressing Them?
The accuracy and efficacy of AI models are reliant on the quality of the data they are trained on. Banks need to ensure that their data is accurate, up-to-date, and relevant to the tasks at hand. Moreover, data siloes and fragmentation can hinder businesses’ ability to fully utilise their data, as modern businesses generate vast amounts of data.
According to a 2021 study by Gartner, decision-making has become more intricate as compared to the previous 2 years of the study. The study shows decision-making increasingly involves more stakeholders and options, with 65% of decisions being more complex. The present state of decision-making is not feasible in the long run. Decisioning engines can help address this issue by leveraging vast amounts of data to make decisions that meet the three essential criteria of effective decision-making: connectedness, contextuality, and continuity.
The use of AI in banking raises ethical concerns, such as bias and discrimination. Banks need to ensure that their AI systems are fair and unbiased. For example, the term ‘black box’ is used to describe AI as opaque and immune to scrutiny or responsibility, due to the complex nature of AI systems. This is where explainable AI (XAI) fits like the missing piece of a puzzle. XAI refers to systems and tools developed to enhance the transparency of the AI process for humans.
Integration with Existing Legacy Systems
Integrating AI with existing banking systems can be challenging. Banks need to ensure that their AI systems are compatible with their existing systems and that the implementation process is seamless. However, integrability should not be the sole focus for banks and FIs, as legacy systems can be expensive in the long run, due to maintenance and servicing costs. Additionally, a phenomenon known as ‘spaghetti systems’ can occur, which is one of the main driving factors behind the increasing maintenance costs. The emergence of digital banks, which are entirely digital right from the beginning, was a response to these problems. This has put traditional banks in a position where they must decide whether to embark on their own digital transformation journey and offer digital banking services to stay competitive.
What are the Benefits of AI Models in Banking?
AI can automate repetitive tasks, allowing bank staff to shift their focus on other aspects of their work, which in turn empowers banks to reduce their operational costs and improve efficiency. This can help banks provide faster services to customers and reduce the likelihood of errors.
AI can enable banks to provide personalised experiences to customers. Banks can also recommend products and services based on the customers’ transaction history and preferences. This can improve customer satisfaction and loyalty, resulting in customer retention.
AI-powered fraud detection systems can provide enhanced security to customers. As aforementioned, banks can detect and prevent fraudulent activities in real-time, reducing the likelihood of losses and protecting customers’ data.
In A Nutshell
AI is transforming the banking industry by improving efficiency, enhancing customer experiences, and providing enhanced security. Globally, AI technologies are estimated to bring about $1 trillion of additional revenue each year within the banking industry, as per McKinsey’s findings. However, the implementation of AI comes with its challenges. Banks need to ensure that their AI systems are fair, unbiased, and compatible with their existing systems. The use of AI models in banking is still in its early stages, and there is much more to explore in terms of its potential applications. As the technology continues to evolve, we can expect to see even more improvements in banking operations and customer experiences.
Artificial Intelligence has firmly established itself in the banking industry and is set to shape its future. By adopting AI, banks can gain an edge over their competitors and deliver superior services to their clients. With its potential to enhance efficiency, improve customer satisfaction, and mitigate financial losses, AI and ML combined is a powerful tool that is transforming the banking sector.
JurisTech, Your Preferred Partner
JurisTech has been in the Fintech field of credit management, digital banking, and artificial intelligence for over two decades, and counting. Our explainable automated ML and AI platform that leverages advanced ML techniques to create potent AI models automatically is an excellent example of how we use our expertise in artificial intelligence to enable banks and FIs to make intelligent business decisions and gain insights to solve real-world problems. We also have an auto-decisioning engine that enables businesses from various industries to make complex, crucial decisions confidently and with precision using data-driven insights in real-time. Geared up for more? Check out our Solutions Page to explore our extensive suite of innovative AI-driven software solutions that powers the economy.
Our team of technical experts and business consultants are passionate about helping clients achieve their digital transformation goals. We believe in working closely with our clients, taking their unique project requirements and organisational needs into account to navigate their systems with ease. To ensure a smooth transition to our clients’ newly enhanced systems, we provide top-quality support that extends beyond project completion, offering comprehensive post-implementation support and training to meet our clients’ ongoing needs.
If you’re interested in learning more about how JurisTech can help you achieve your digital transformation goals, don’t hesitate to contact us today. Our team is always here to support you on your journey towards a more efficient and effective digital future.
JurisTech (Juris Technologies) is a leading Malaysian-based fintech company, specialising in enterprise-class software solutions for banks, financial institutions, and telecommunications companies in Malaysia, Southeast Asia, and beyond.