How Banks Are Utilizing AI-Driven Business Intelligence Practices
The evolution of Business Intelligence and how AI-driven BI is impacting FinTech
This past year, we’ve held various conversations with various financial stakeholders about the current advancements in FinTech. Topics such as regulatory pressure, impact on profitability, credit ratings, future of payments, Robo-advisors, Neo-Banks, and the overall increasing amount of data available, have given us insights on the current financial ecosystem and where it is potentially heading.
One thing is sure, with 2.5 quintillion bytes of data produced every single day, new opportunities for data in Fintech appear with each new sunrise on the horizon.
DataSeries has been watching the evolution of Business Intelligence closely. To better understand BI, we decided to take a look at how ING is currently utilizing Big Data, integrating Artificial Intelligence, and using Business Intelligence techniques and tools to enhance their business practices. Most well-established organizations are trying to harness the power of data analytics to surpass their competitors and gain value like never before.
While competition is fierce in today’s markets, extracting valuable BI will allow your organization to stay ahead and play the innovation game. From a conversation with ING, we have understood that by combining data with a variety of other data sets, a paradigm move is currently present, shifting from a node view to a network view.
So what does that mean? Before we can answer this, let’s understand the development of the BI term and how it has evolved into a common practice in today’s business world.
Finance is becoming automated
The finance function globally has transformed and evolved into an automation playground, but is that it?
Digital transformation has paved the way in financial industries and is now showing its radical transformation power. Current trends such as AI, Blockchain, and Advanced Analytics are indeed challenging the relevance of traditional skills while creating transformative opportunities for organizations and individuals.
Transformative opportunities are great, but what’s even more important is to extract the true value out of a continuously changing environment, and you need to do it quick. We are moving from cost to value; hence, you have got to go beyond insights to drive impact, beyond limits to deliver solutions, and beyond expectations to create value.
By studying the evolution of BI, we can understand how the development of AI-driven BI has a bright future and will most certainly bring many practices to the next frontier.
In 1865, Richard Millar Devens presented the phrase “Business Intelligence” in the Cyclopaedia of Commercial and Business Anecdotes. He was using it to describe how Sir Henry Furnese, a banker, profited from information by gathering and acting on it before his competition. More recently, in 1958, an article was written by an IBM computer scientist named Hans Peter Luhn, describing the potential of gathering BI through the use of technology. As it is understood today, modern BI is defined as follows:
A set of methodologies, processes, platforms, applications, architectures, and technologies that transform raw data into meaningful and useful information used to enable more effective strategic, tactical and operational insights and decision-making.
Before computing became a commercial success, only individuals with an extremely specialized skill-set could translate data and extract useful insights. Data was stored in silos, and the outcome was typically presented in a scrappy, disorganized report that was open to interpretation. Things have rapidly changed, and Decision Support Systems became an area of research of its own in the middle of the 1970s, before gaining intensity in the 1980s.
The rise of Decision Support Systems
DSS was the first information system that supported business or organizational decision-making activities. These support systems primarily served management, operations, and planning levels of an organization and helped people make decisions about problems that may be rapidly changing and not easily specified in advance — i.e., unstructured and semi-structured decision problems.
In the 1980s, the number of BI vendors grew as businesses discovered the potential value it entailed.
Consequently, this move helped in developing the use of Big Data, as data warehouses started becoming popular. Many businesses would begin analyzing their data, which was often done after the regular 9–5 times and on weekends, as of the limitations of computer systems at the time.
In 1988 at “The Multiway Data Analysis Consortium” in Rome, Business Intelligence, as a technological concept, was laid in stone. In the late 1990s and early 2000s, BI services began providing simplified tools, allowing decision-makers to become more self-sufficient. Terms such as descriptive analytics, predictive analytics, prescriptive analytics, streaming analytics, etc. have been born since then).
Analytics, as a whole, began receiving more attention as computers became decision-making systems. With the development of big data, data warehouses, the cloud, and a variety of software and hardware, data analytics has evolved. It is now refreshing the BI’s innovation curve through the emergence of AI. For many business leaders, AI in BI has been top of the mind for quite some time. AI is deeply integrated into the financial industry, where some standard practices include AI for Personal Finance and Insurance, but also AI for Cross-Industry.
Some of the applications of AI in the Personal Finance and Insurance space are more understanding digital financial advisors, searching for transactions and visualizing them, assessing a client’s risk profile, or automating the claiming process. AI for Cross-Industry reaches a broader scope, and it can be used for analyzing contracts, predicting churn, trading algorithmically, valuating models, and many more.
How’s AI impacting the industry
AI is making a substantial difference in our analytics world as it helps to democratize data and improve adoption.
AI helps organizations to distribute and organize millions of data efficiently while it facilitates the way to understand that info accurately. AI applications aren’t new, and smart applications powered by machine learning algorithms and data science have been applied to several sectors, including, of course, the financial industry.
Core AI applications and techniques such as predictive analytics and machine learning have opened the door to a new generation within BI. Since AI can analyze massive quantities of data faster and deliver recommendations based on that data more effectively than before, a standard change is already happening, and its impact will be standardized across various verticals.
Insights will be more accessible and understandable to the average user and with such an upgrade, assist business leaders in rethinking both their strategy and outlook.
How’s AI-driven BI shaking up the industry
We see AI-driven BI applications at every corner; some of them are in the form of dashboard and visualization tools, predictive analytics, reporting, data mining, to name a few.
Using these applications to automatize business activities can lead to a gain in efficiency. Reports can be shared automatically with clients or suppliers, eCommerce activity can be tracked at the spot, inventory and sales can be tracked in real-time, you name it.
We held a conversation with ING on how AI-driven BI is affecting the industry:
Maxim: What is a general BI use case at ING?
ING: At ING, we have a Wholesale Banking Advanced Analytics department that has developed a product that combines a vast amount of payments, client, and credit data to empower two key areas.
We are bringing critical insights to the sales department to understand clients better, and we are providing valuable data to the risk department so they can improve the quality and efficiency of risk management.
Maxim: What about AI? Have you integrated it into any business processes as of yet?
ING: Yes, we have.
For example, we use core algorithms that automatically link massive data sets when there is no standard key between databases and remove data points that are not useful
We also use algorithms that generate differentiating insights for, for example, a peer analysis that uses the customers’ buyer/supplier network as a feature of comparison as opposed to size, sector, and geography.
Maxim: What insight does the general study of data give you?
ING: By combining payments data with a variety of other data sets, we have moved from a node view, looking at one client individually, to a network view, which takes into account how clients interact
What to expect in the future
With today’s computing power and the advancement of algorithms to extract value out of large data sets, the overall perspective on how we are looking at combinations of data sets has changed. As ING’s representative has accurately mentioned, we have advanced from a singular analytical POV to a network POV that enables us to study interactions on a macro level and therefore tap into new insights, patterns, forecasting options, and so on.
The current business environment has grasped the importance of trying to leverage data-driven insights and improve business practices. Existing data applications in BI has already started to automate many repetitive tasks and are now moving towards more complex tasks with the help of AI and further impacting strategic decision making within organizations.
The article was written in collaboration with CITY.AI