For the last few years, the professional world has closely watched everything related to Artificial Intelligence (AI) and Big Data. Modern technologies have found particularly fertile ground in finance, which is dynamically developing thanks to implementing new solutions. Numerous concepts in machine learning (ML) and business intelligence (BI) have become particularly important. Some argue that machine learning has become a replacement for business intelligence. However, they are two different approaches that should complement, not replace, each other.
Finance is more and more competitive. Therefore, banks and other financial institutions do not hesitate to invest in the latest technologies and solutions to remain competitive. However, organizations in the financial industry, particularly after Covid-19, faced a new reality. Companies that base their activities on large amounts of data, including historical data, could no longer use traditional analytical techniques. It became necessary to implement new solutions, as the pandemic made many of the models used so far obsolete.
In such realities, the advent of the extended ML-BI approach seems not only welcomed but highly desirable. However, before we check how the combination of these two concepts works in the world of finance, let’s take a look at each of them separately.
Traditional business intelligence
The concept of traditional BI allowed financial institutions to create a defining picture of their organization’s activities. However, it was a very visual vision based on a large amount of information. BI experts mainly used aggregated data to define future trends for this intent.
The first step in any BI is collecting raw data. Next, data engineers use ETL (extract, transform, load) tools to exploit, convert, and organize data in a structured database. These structured databases are called data warehouses, and with their use, businesses and analysts can benefit from visualization techniques to explore the data stored in these warehouses. This tool creates visual dashboards to make information available to non-data professionals. Finally, panels help you analyze and understand past performance and adjust your future strategy to improve KPIs (key performance indicators).
What about machine learning?
Machine learning is an artificial intelligence form where a machine can accomplish jobs without being explicitly programmed. Introducing an ML algorithm typically needs a considerable amount of earlier cleansed, structured, and classified data. Then, the machine builds an algorithm based on the recognized patterns by analyzing the data. Finally, the algorithm is refined over time until it reaches a high accuracy and can be applied to entirely different data sets.
Organizations from the finance industry have used machine learning consulting services more and more in recent years. As a result, ML experts are at their disposal and help to implement solutions that minimize the need to involve people in the activities of banks and other financial institutions. ML consulting also allows you to find solutions that make it almost wholly possible to automate time-consuming ones. Thanks to this, companies can focus on essential aspects of their business, entrusting some tasks to advanced ML algorithms.
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Machine learning in business intelligence
Continuously evolving BI automation is no peculiarity. At the vanguard of this mechanization is the push for AI and ML that has penetrated the business intelligence market and is changing how companies (including finance organizations) think about their data. BI devices vary in process, audience, and technique, but the highest goal of any platform is to deliver users practical information around their data. However, one of the most critical weaknesses in BI tools is that they don’t match nicely into the structure of most organizations.
Business intelligence tools are commonly planned for data analysts and scientists. In some ways, this ubiquitous choice makes sense – after all, data analysts and analysts are best placed to understand data, iterate through insights, and ask targeted follow-up questions to gain a complete understanding of the data landscape. Yet marketers, salespeople, category managers, and people, usually under the umbrella of business, are at the forefront of the decision-making process. As a result, business intelligence instruments can be unnecessarily complicated and unmanageable for these employees.
After all, business intelligence should support its users and help them understand the business data. Machine learning helps with this process, changes how BI is shared across departments, and optimizes data-driven decision-making across all areas.
Machine learning consulting and bi in the service of finance
Business Intelligence offers a practical approach that describes what happened in the past, enables you to understand data in non-analytical business roles with powerful visualizations, and makes decisions based on global trends.
On the other hand, ML is a concept that can notice ways from masses of unique data. Therefore, the development of finance’s predictive applications is one of the key advantages as they facilitate decision-making and continuous learning from the data and enable the automation of tedious processes. It is also worth noting that these systems are constantly fed with new data, thanks to which they learn automatically over time, thus integrating with the company’s development and adapting to changing environments.
Why shouldn’t you ignore BI?
After reading the above text, you may be wondering why not give up BI in favor of ML? The reason is straightforward. BI is a “business” topic, not strictly technical, like machine learning. As the amount of data for daily processing grows, finance companies must demonstrate agility. New BI tools and ML consulting will allow companies to collect efficiently, sort, centralize and structure all raw data at their disposal. By combining machine learning algorithms with predictive BI solutions, finance departments can effectively predict default risk and identify fraud. In combination with ML, the powerful modern BI tools will quickly show the performance indicators that financial institutions need.
By combining ML and BI solutions, companies from the finance industry can implement custom recommendation engines to personalize offers for each client effectively, implement self-service BI for reporting, and use custom ML models for scoring. However, to move from the traditional business intelligence model to ML, you need a good analysis to help you lay the groundwork for your next steps. That is why banks and other financial institutions more and more often use machine learning consulting services. Thanks to the support of experts who know the technologies and the realities of the finance industry, they can efficiently guide the bank through all stages of ML implementation to the existing BI techniques.
