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From insurance to healthcare to finance, Artificial Intelligence and Big Data significantly impact various fields of life. For the financial industry especially, the opportunities of AI and Big Data are immense since they affect one of the most vital areas and directly transform people’s lives.
IHS Markit foresees that the use of AI in world banking will increase up to 300 bln dollars by 2030, while McKinsey supports these predictions by reporting the actual AI state in its Global AI Survey – almost 60% of organizations leverage at least one AI tool in their daily operations.
In this article, we will dig deeper into the benefits of AI and Big Data for the financial sector, observe the key challenges companies face while adopting these tools, and observe several successful use cases of AI and Big Data in finance.
Have you ever thought about how much data your devices transmit daily to the data processing systems? Or how does your search history or journey on the web affect your user experience? All this massive amount of data is called Big Data. Being characterized by 3 key features – high volume, high diversity, and high velocity – this data is too large and complex to be managed by traditional data processing tools.
Here is when Artificial Intelligence comes in handy. AI tools can deal with massive data sets the way an ordinary human can never do. Here are several ways AI tools can help in processing Big Data:
Though AI commonly grabs all the attention when it comes to data processing, it is worth noting that only when combined with Big Data can AI lead to more efficient and substantial results. And the more data an AI system or tool has to work with, the more qualified and trustworthy results it may bring to the table.
Behind the glamor of the visible effect of AI and Big Data on the financial industry, there are still numerous challenges organizations face while implementing AI tools. Below are the most common ones:
It is quite common for financial organizations to have unstructured data or data silos accessible for one department and isolated for others. Such a non-systematic approach may cause multiple conflicts for AI tools. Apart from that, the lack of cohesion in data storage and management can cause trouble in the objectivity of research handled by AI tools and create obstacles to the scalability of research results.
One of the latest studies in AI shows that around 25% of companies failed in their AI projects. One of the key reasons for such gloomy statistics is the lack of appropriate organizational skills and talents. Moreover, the absence of long-term strategic thinking in the hiring and onboarding process exacerbates the situation.
According to the latest report by Tortoise Intelligence, worldwide investment in AI companies has increased by 115% since 2020. However, deciding on the source of money for AI projects and prioritizing investments in AI and Big Data projects may still be troublesome for most companies in finance.
Let’s take a closer look at several common use cases where AI and Big Data can make revolutionary changes in the financial industry.
The abundance of chatbots and virtual assistants in banking has marked the past decade. These digital friends significantly reduced wait times and streamlined customer service. Virtual assistants have become game-changers in the financial industry by responding to common user requests such as checking accounts, getting regular bank statements, or making automated payments.
But there is more. Banks and financial institutions can suggest personalized financial recommendations to their customers through AI by processing users’ data and activities on third-party applications or websites.
Another great option offered by AI and Big Data is the automated saving process. One of the latest examples is NOMI, a Royal Bank of Canada platform that helped users save around 2 billion dollars by rounding up their expenses. The solution, based on a machine learning algorithm that rounds up slight differences in purchased items’ prices (such as coffee prices rounded up from $2.55 to $2.80) and transfers this difference to a user account, has become groundbreaking for RBC customers.
Big Data analysis held by AI tools serves multiple functions, from suspicious events to potentially fraudulent activities. Thus by operating users’ data and actions, AI can help companies in their long-term planning and risk management and prevent any problem-causing situations.
In banking, AI-based systems can predict fraud probability or reject transactions by allowing humans to deal only with higher-level cases where human involvement is vital.
Very often, AI and Big Data tools speed up compliance tasks. Just like with the JP Morgan Chase case, where implementing an ML algorithm helped a team of legal specialists reduce the time spent on their mundane tasks related to customer agreements’ reviews from over 300 thousand hours to several minutes.
Advanced financial companies and banks use AI and Big Data analysis to review customers’ data to streamline loan decision-making.
Natural Language Processing (NLP) tools assist in assessing a customer’s wealth and ability to timely pay loans. Additionally, Optical Recognition, as part of NLP, can reduce hours of human work spent on customer document processing by extracting valuable data from scanned documents and papers. In such cases, AI makes a big difference in a loan decision-making process since, apart from a human factor, bank workers can rely on a wide range of objective insights provided by machines.
We’ve observed the benefits of AI and Big Data in the financial sector, got acquainted with the challenges faced by organizations adopting these tools and reviewed several common yet impactful use cases of AI and Big Data in finance.
In a nutshell, implementing AI and Big Data is no longer a nice-to-have option for the financial industry. With ever-rising financial crime rates all across the globe, such sophisticated solutions should go hand in hand with ordinary human efforts, and those financial institutions that won’t lose their grip will win the most in the end.
Kanda’s Big Data Development and AI services are tailored to meet unique customer demands. Our skilled teams of Data Scientists, ML engineers, Big Data developers and QA specialists work with a wide range of companies in the financial field – from dynamic startups to well-established enterprises. By carefully handling the complexity of a client’s data, Kanda’s team will propose the best-possible solutions according to the company’s business objectives.
Talk to an expert to kick-start your modernization process with Kanda.
