Since the pre-digitization of industry, the banking sector has been a competitive environment. With growing digitization owing to the impact of financial technology, the competition has become tougher. FinTech companies are to banks, what Uber is to the taxis.

The banking sector is challenged not only by FinTech companies but also by the regulators. Banks are being keenly observed by regulators like FDIC, FRB, and OCC since the 2008 crisis. Banks were made to strictly adhere to the compliance rules adopted post-crash.

All these requirements need to be reflected in competitive business intelligence solutions. The banking sector needs to adapt flexibility and transparency in the competitive and regulatory environment. It takes scalability to keep up with the growing digitization of the industry because most of the clients fail to remember when they last walked into a bank. Modern bankers need to be smart enough to drive feasible operational and financial decisions. The conventional means of spreading out data across multiple graphs, Venn diagrams, and pie charts seem an unfeasible approach.

Certain trends and tools that transform business intelligence in banking sector includes robust toolkit, Artificial Intelligence (AI), specialization, data people in the spotlight, Internet of Things (IoT), security, equilibrium for banking, data sources, managing data, preparing data, building models, managing models, and consuming business intelligence.

The banking industry is being taken over by Artificial Intelligence and particularly machine learning. There is also cost and benefit analysis involved. According to Goldman Sachs, Artificial Intelligence will render up to $43 billion savings by the financial year 2025. Not many banks can pass on that. So, all the major banks in the U.S. are investing heavily in AI and Machine Learning.

Owing to the trends and changes, implementing Business Intelligence isn’t possible without data professionals, and there has been a growing demand for data engineers over the last couple of years. There is a more promising situation for data scientists. Data enthusiasts can embark their career in banking, as more banks are exploring advanced algorithmic trading systems and other tools that can help them in checking their investment risks. These professionals are responsible for the proper data storage, accessibility of data, and analysis.