Artificial intelligence (AI) is rapidly transforming how we work and manage our tasks across various industries, and banking has been no exception. Banking is at its watershed moment, and the disruption and opportunities that came with the rise of these new technologies pave the way to new operating models for the industry.
Yet AI is nothing novel to the banking industry, as it has maintained a quiet presence for years. From data analysis for fraud detection to bill prediction and cash flow forecasts, these banking technologies have always been assisted by AI. However, as the development of deep machine learning technology has rapidly advanced in recent years, so too has the role of AI in banking. Today, AI is now making strides in assisting in areas such as risk assessment to personalized banking for customers. AI is not necessarily new per se, but it is transforming.
AI is hard to keep up with due to the sheer pace it is evolving. If you are in the finance industry or a holder of a Master's in Cybersecurity with no experience and looking to enter banking or finance in general, acquiring knowledge on the role that AI has in the industry these days is absolutely essential - to ensure that you can upskill and maintain your competitive advantage in the field. The growth of AI is evident, as just in 2023 alone the use of AI in the banking industry saved an estimated US$447 billion.
Risk Management
It is no surprise that AI banking made its leap and bounds during and after the pandemic. Such unprecedented events and volatile times severely affect the functioning of financial services, and, fundamentally, businesses need to make decisions with insight. While AI has not been an all-out solution, it has been a practical assistant for banks and financial services to assess whether they are making the right decisions, and with the appropriate amount of caution.
AI's deep machine learning capabilities mean that AI has predictive analysis capabilities. AI analyses large consolidated blocks of data and information of customers and can very quickly identify behavior patterns to transpose them into risk predictors. This process plays a critical role in fraud detection, credit scoring, and even in circumstances where banks decide whether they should offer a loan or not to a customer.
It makes it much easier to identify suspicious behavior such as fraud attempts, or explicitly illegal activities such as theft or money laundering. By this banks can preemptively prevent fraudulent transactions and protect customer assets. Obviously, AI is a technology that should ultimately be concentrated in the hands of experienced experts to make such decisions in the first place.
Personalization: From Planning to Advisory Services
It is the age of hyper-personalization, and for banks, AI easily understands individual customer behavior and preferences. It ensures that each customer's individual financial needs are tailored to.
Traditional banking usually consists of generalized financial advice and investment options that do not fully align with the more particular and unique financial objectives that customers may have. The rise of data-driven technologies however enables far more intelligent offers, personalized solutions, and smart servicing that are specifically tailored to customers with their individual preferences and goals.
Data elements that AI may collect for this personalized experience include income, expenses, assets, liabilities, investment records, and long-term aspirations. Leveraging AI algorithms on this data can give banks a proper (and most importantly, comprehensive) conception of the individual financial profiles of each customer. Not only do they assist banks, but can also serve as a guide to customers on their investment portfolios, to be more money-smart and manage their finances more efficiently.
Better Customer Support
Chatbots have always existed, and you may have even used some before. However, these non-AI-based chatbots had significant limitations such as not being available 24/7.
AI has very quickly been incorporated into chatbots for digital banking in recent years. Today, these AI-based assistants now handle customer inquiries in real-time, with fast and accurate responses to frequently asked questions (FAQs). And most importantly, the majority of these bots are available 24/7.
For customers, it allows a far greater degree of autonomy and the capacity to engage in self-servicing. For banks, this boosts quality and eliminates high-cost, laborious, and time-consuming processes by augmenting human agents behind the screen.
Operational Efficiency: Streamlining the Process
Besides predictive analysis, automation is the foundational factor that makes AI such an attractive solution for businesses.
It is clear that AI has its uses for things such as enhanced customer service and effective predictive analysis - but it also has its uses for mundane internal tasks such as compliance checks, data entry, document verification, and transaction processing. Automating these repetitive tasks minimizes manual errors, processing time, and not to mention, operational costs. As such, automating and freeing up these tasks allows employees to shift focus toward more complex or high-value tasks.
Outside of interest and loan deposits, banks usually also extract profits through investment and trading. Data acquired from internal tasks and activities by machine-learning AI algorithms can also allow banks to refine internal trading strategies, and improve investment decisions and returns.
Data and Privacy: Managing the Threats from AI
Safety, safety, safety - it should always be the most fundamental priority for banks using AI. Financial data is sensitive data, and any data breach would have disastrous consequences not only for the customer but also for the financial institution in its entirety.
Loss of customers, reputational damage, and financial losses are just some of the consequences - and, ideally, they should be avoided at all costs. Regulatory bodies become central here to ensure that customers have confidence and that their data is protected.
Equity: Avoiding Discrimination and Keeping Ethical Standards
A key ethical issue with AI is algorithmic bias in decision-making. It must be noted that it is only because of humans that there is AI, and naturally, we have our biases and prejudices. And it is only under a loop proceeding conducted by a person that AI becomes deployed. Reducing, and even if possible, completely avoiding bias should be a foundational principle for banking in the age of AI.
For example, if an algorithm was trained with data that is skewed or biased, it would lead to discriminatory decisions. These decisions may include denials of loans or mortgages based on demographic factors. Bias in AI is admittedly quite difficult to detect, and usually, it is only after the damage has been done that it becomes visible. However, Implementing comprehensive policies and processes, such as monitoring AI models for data drifts or keeping an employee as an overlooker of the AI, would reduce the chances of bias or discrimination.
Without these, there is always the risk of discrimination, which can lead to ugly situations such as lawsuits, which have consequences equal to that of direct data breaches.
The level at which banks will incorporate AI depends on the level of investment they have made into it. Nevertheless, the transformation of AI in the banking industry is accelerating, offering multitudes of amenities such as increased operational efficiency and risk management. Undoubtedly, AI will occupy a pivotal place in the future of banking.