How banks are utilizing AI-driven Business Intelligence practices
A short introduction to the evolution of Business Intelligence and how AI-driven BI is impacting FinTech
Based on recent 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, neobanks etc, and the overall increasing amount of data available, have given us insights on the current financial ecosystem and where it is potentially heading to. One thing is certain, with 2.5 quintillion bytes of data produced every single day and that number is no where close of reaching a plateau — opportunities are appearing with each new sunrise on the horizon.
We’ve been looking closely at the evolution of Business Intelligence (BI) and were curious how a multinational such as ING (a Dutch multinational banking and financial services corporation) is currently utilizing Big Data, integrating Artificial Intelligence (AI) and Business Intelligence (BI) techniques & tools to enhance many of 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. Whilst 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.
[INTRO]
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. Buzzwords 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 right 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 are able to understand how the evolution of AI-driven BI has a bright future and will most certainly bring many practices to the next frontier. Let’s dive in:
In 1865, Richard Millar Devens presented the phrase “Business Intelligence” (BI) 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 could be explained by being: “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”
But before computing became a commercial success, only individuals with an extremely specialized skill-set could translate data and extract useful insights. Data was usually stored in silos and the outcome was typically presented in a scrappy, disorganized report that was open to interpretation. Things have changed with an increase of research and Decision Support Systems became an area of research of its own in the middle of the 1970s, before gaining intensity in the 1980s.
Decision Support Systems (DSS) was the first information system that supports business or organizational decision-making activities. These support systems essentially served management, operations and planning levels of an organization and help 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 start 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 and 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 and with the development of big data, data warehouses, the cloud, and a variety of software and hardware, data analytics has evolved and 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 common practices include:
AI for Personal Finance and Insurance
- Digital Financial Coach/Advisor
- Transaction search & visualization
- Client Risk Profile
- Underwriting, Pricing & Credit Risk Assessment
- Automated Claims Processes
AI for Cross-Industry
- Contract Analyzer
- Churn Prediction
- Algorithmic Trading — the most advanced ML you will ever see
- Augmented research tools
- Valuation Models
etc. etc..
AI is evidently a mega trend and is already making a substantial difference in our analytics world as it helps to democratize data and improve adoption. Simply put, AI aids organizations to distribute and organize millions of data efficiently while it eases the way to interpret that info correctly. AI applications aren’t new at all and smart applications powered by machine learning algorithms and data science have been applied to a number of 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 paradigm shift is already in the 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 to rethink both their strategy and outlook.
So what about AI-driven BI? — We are seeing AI-driven BI implications at every corner, some of them are in the form of (see link for the whole list & explanations):
- Dashboards
- Visualizations
- Reporting
- Predictive Analytics
- Data Mining
- ETL (Extract, Transform, Load)
- OLAP (online analytical processing)
- Drill-Down
Here are some example use-cases to make it easier to understand:
- Schedule regular automated reports
- Automatically share reports with clients
- Visualize inventory and sales in real-time
- Pill data from multiple databases
- Analyze eCommerce sales in real-time
- Integrate with cloud computing services like AWS
- Pull and analyze data from a CRM
- Data mining for deep layers of analytics
- Create embedded dashboards in a separate internal system
- Provide historical analysis on payroll, benefits and other employee HR data
- and many more…
So, what about BI practices at a multinational bank and financial service corporation such as ING?
A quick conversation with ING
ING has been kind enough to share with us some info to help us get an understanding of how such an organization is utilizing BI tools and how AI is now being integrated as well.
“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 huge amount of payments, client and credit data in order to empower:
1) The sales department with insights that they can bring to clients and
2) the risk department with insights that enables them to increase the quality and efficiency or risk processes
“What about AI? Have you integrated it into any processes as of yet?”
ING: We have, for example:
1) Core algorithms that automatically link massive data sets when there is no common key between databases and remove data points that are not needed for usage and
2) algorithms that generate differentiating insights for e.g. a peer analysis that uses the customers buyer/supplier network as a feature of comparison as opposed to size, sector and geography normally used
“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 (one client) to a network view (how clients interact).
[CONCLUSION]
With today’s computing power and 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 discovers insights, patterns, forecasting options, etc. in a more accurate manner. Not only that, but the current business environment has grasped the importance of trying to leverage these insights received and improve their practices. Existing data practices in BI have already started to automate many repetitive tasks and are now moving towards more complex task with the help of AI and essentially impact strategic decision power on many layers within organizations.
Written by Maxim Matias
Curated by DataSeries
