Computers are good with numbers — in fact, one could argue that computers only understand numbers (even in the cases of natural language processing, commonly known as NLP, words and phrases are converted into numbers). With that in mind, we should not be surprised that the banking industry has had a heavy reliance on computers ever since their introduction and later popularization.
In recent years, as artificial intelligence (AI) and machine learning (ML) evolved and gained popularity—namely, the use of big data and data science as a whole—the banking industry also began to harness the power of AI and ML in their daily operations. You see, banking and AI often go hand in hand together—not only can they provide more in-depth analysis based on the massive amounts of data present, but they were also used in more simple tasks, tasks that even ordinary folks would find themselves interacting with on a day-to-day basis.
This time, we are going to look into the use of machine learning in banking, various banking operations, and what role AI and ML play in said operations.
This is by no means an in-depth article on the specifics of AI financial services or explorations of AI models used in banking analysis. As a mere introduction to AI and ML in banking, we will cover the current banking operations that utilize the technology and what the future might entail in this article.
Types Of Banking Operations
There are numerous bank operations involved in the banking and finance industry, and so are the ways they can be categorized. We are going to take a quick look over the different types of banking operations in this paragraph, what they do, and then move on to how AI and ML have been leveraged in the banking and finance sector in recent years.
As the term implies, investment banking refers to specialized banking operations that focus on making investments and furthering the revenues and returns of the banks. Within the scope of investment banking, their operations include facilitating mergers, acquisitions, stock trade, equity transfers, capital market functions, and debt processing. Their daily operations also involve database maintenance, data analysis, risk management, equity analysis, and data processing—as you might have guessed already, fields where AI and ML can greatly facilitate.
In simple terms, trade support can be seen as operations that involve the documentation and verification of trading activities—say, a trader purchases 500 shares of Stock A for $60 from Bank B. For people who work at trade support, their responsibilities would include:
- Making sure the stocks are accurately documented in the bank’s system
- Sending an email to Bank B to inform them of the quantity and price
- Ensuring the amount involved in the transaction is taken care of
- Verifying the legality of the transactions
As such, trade support requires extreme attention to detail and accuracy, as a single mistake can cost a hefty fortune. Automation is key in trade support operations.
Risk Analysis and Mitigation
Banking operations will inevitably come with risks, be it human errors or intentional fraudulent activities carried out by individuals, thus the need for risk analysis and mitigation arises. As the name suggests, their operations include monitoring, identifying, and minimizing the risks involved in the operations through compliance and protocols.
On a side note, McKinsey released a detailed paper on the future of risk management in banking should you be interested in this field.
Corporate banking normally refers to operations that, well, involve larger corporations and institutions. Such operations range from loans and credits to proper compliance based on the different regulations. As part of their duty, they also ensure that different parties adhere to the applicable policies and regulatory guidelines.
Simply put, any banking operations that involve interacting with clients can fall under this category. From closing and fulfilling loan agreements to resolving customer disputes, client services encompass many operations that involve direct human interactions within the banking sector. In recent years, the banking industry has also seen a new trend where AI is utilized in this field to improve the overall user experience. Again, McKinsey released a report on this topic should you be interested.
In many ways, technical operations within a banking institution can be seen as the backbone of ensuring the smooth operation of a bank. While other teams and operations often work with the regulatory guidelines and compliance, technical operations involve the actual steps within the operations—see them as clogs in the machine, if you will.
Technical operations is a rather loose term as well, as the operations include automation and documentation of the processes, the maintenance of the databases, among many other tasks that ensure the smooth operations of a bank. Technically speaking (no puns intended), human resources (HR) and the accounting department can also be seen as part of a bank’s technical operations.
Examples of Machine Learning in Banking
We hope you’re still with us now that we got over the different banking operations. It might be a long read, but through the different operations mentioned above, you should now have a clearer idea of how a bank function—and, more importantly, how artificial intelligence and finance often worked together in recent years to facilitate the processes to ensure better precision and efficiency.
But how exactly does machine learning come into the picture? In what areas are the banks utilizing AI and machine learning? And what are the fields and operations where they can incorporate these technologies?
For the sake of convenience (and our sanity), in this article, we will separate them into three categories: front office (conversational banking), middle office (fraud detection and risk management), and back-office (underwriting). According to a report from Business Insider, these channels are also places where banks can cut down operational costs significantly by utilizing AI.
Front office operations can be seen as those that require interactions with clients and customers. Imagine that you, as a client, are trying to make a transfer to another individual with a bank account in a different country, yet somehow the transfer didn’t go through, and an error message showed up.
In that case, you will have to contact some sort of support from the bank to rectify the situation. These days, it is likely that you’d encounter chatbots that guide you through some basic troubleshooting—or, if you call the customer support number instead, you’d often hear voice assistants instead of a real support agent. All these are examples of artificial intelligence in banking, and they can be seen as front office AI automation.
Front office AI doesn’t stop here, however. Beyond offering support and mimicking human responses, front office assistants, through the use of machine learning, can also offer insights and recommendations based on the data available, providing you with a much better user experience.
"The big paradox here is that people think technology will lead to banking becoming more and more automated and less and less personalized. But what we've seen coming through here is the view that technology will actually help banking become a lot more personalized."
— Alan McIntyre, Senior Industry Director - Banking at Accenture and co-author of the report, Banking Technology Vision 2017.
As the term implies, middle office operations sit between the front and back office. What does that mean exactly? It means that the middle office sort of works like a middleman between the forward-facing operations (front office) and the actual business operations (back office).
Risk assessment, payment fraud detection and prevention, and anti-money laundering processes — these can all be categorized as middle-office operations, and coincidentally, these are also areas where banks are actively automating using novel AI and machine learning technologies.
In many ways, compliance is a major part of middle office operations. With thousands upon thousands of users, manually going through each operation and transaction would not be ideal. This is where artificial intelligence and finance come together — by utilizing existing data, banks can analyze the numbers and statistics at a glance and suggest appropriate actions right away, cutting down the time required and, along with that, costs.
Within the banking industry, risks can be defined as a deviation from expected outcomes. With the help of AI, banks can easily notice irregularities in the numbers and statistics and take action accordingly. This might sound like an over-simplification of AI-automated risk management, but the idea remains all the same.
Generally speaking, back offices can be seen as the backbone of the operations (the technical operations that we mentioned in the previous section). From payroll management to logging the transactions in the system, back-office operations encompass those that ensure the smooth operation of a bank.
With that being said, a lot of the perks of AI automation in other businesses can also be applied to back-office operations within the banking sector. Payroll and invoice automation, for example, are areas where AI can really come in handy and speed up the processes.
Another aspect is digitalization—while digitalization has been a mainstay within the banking industry over the last few decades, AI and machine learning minimize the human resources required in the process. Previously, optical character recognition (OCR) has been used to digitalize documents for further processing—however, AI can also enhance OCR and handle some of the complexities that OCR was unable to handle previously.
As with many other institutions, some banks also outsource their automation projects to companies that provide software development services.
Machine Learning Use Cases in Banking
As a short recap, here are some examples of machine learning use cases in banking:
AI-powered chatbots can be used to enhance the front office operations of a bank and reduce workload, which in turn reduces costs. It’s also a trend that’s been greatly accelerated by the pandemic—according to research from Cornerstone Advisors, 13 percent of mid-sized banks utilized chatbots in their services by the end of 2020, compared to a mere 4 percent at the beginning of the year.
It is also an area that expects exceptional growth in the coming years. According to a report by
target="_blank" aria-label="undefined (opens in a new tab)" rel="noreferrer noopener">Mordor Intelligence, the global banking chatbot market is expected to reach USD 102.29 billion by 2026, compared to USD 17.17 billion in 2020.
Personalized Banking Experience
With AI, it is also possible to provide clients and customers with more personalized suggestions and recommendations based on their data and, more importantly, the massive amount of market data available. From making the right investment decisions to managing finances, all of these can progress towards a more tailored customer experience.
"As consumers, we expect banks to be smart and understand us as individuals and serve us in a personalized and context-aware manner."
— Kaylan Madala, IBM’s Chief Technology Officer, Technology Business Unit, Asia Pacific.
Compliance and Risk Assessment
One huge advantage of AI is its ability to handle a colossal amount of data and process them within a short period of time (much more efficient than human beings). Instead of manually detecting irregularities in the transactions, machines can look into the data and statistics to detect any irregularities without any biases and then act accordingly.
One potential development with AI-assisted compliance is the ability to leverage NLP to interpret new regulations and adjust the process accordingly. While this is not feasible at the moment, it is certainly an area worth exploring.
Internal Process Automation
A bank, just like any other institution, has to rely on certain operations to ensure its smooth functioning. From payroll management to system maintenance, a lot of the internal processes can be automated with the use of AI and machine learning to significantly cut down costs and reduce human error.
"We use AI in many different businesses—banking included, just that we might not even realize it half the time."
— George Serebrennikov, COO at Proxet (ex - Rails Reactor) – a custom software development services company.
NLP and novel data architecture, such as data lakes, can also be used to facilitate the processes within the institution. NLP can assist in the digitalization of the company, while data lakes can help banks store everything in a central repository for easy access and, more importantly, make more informed and accurate predictive analyses.
Adopting ML in Banking
Finance and artificial intelligence go hand in hand together, and these days, a lot of the processes rely on the prowess of AI and machine learning. Instead of wasting time and money on manual processes, AI for financial services can facilitate the processes and offer a myriad of advantages that are unheard of a few decades prior.
AI integration isn’t a one-step process, however, as digital transformation, especially with AI, sometimes requires a general overhaul of the system. While certain tasks can be easily integrated with AI, a full AI transformation would likely require external resources.
However, depending on the business needs, there are certain areas that can enjoy the benefits of AI without having to redesign the entire platform—chatbots are one good example of that. Proxet is a software development firm with years of experience in AI and machine learning in different sectors. Should you be interested in AI-powered solutions for your business, Proxet is here to provide you with the right options and solutions.
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