In Fintech, technology and finance meet as new technology evolution in financial solutions take root. The strong embrace between technology and financial services is very strong resulting in synergies and apparent disruptions. Diverse critical elements are helping accelerate the uptake and incorporation of AI (Artificial Intelligence) today, such as the growth of open source systems, data storage dipping costs and huge distribution of computing power.
According to research, AI could actually double the economic growth of more than 20 nations around the world by 2035. This indeed would see productivity of labor activities rise by 40%. It’s also worth noting that AI is expected to have a huge impact on financial organizations, particularly finance based institutions. In essence, AI’s potential could see finance functions transformed forever in banks after a decade or much earlier.
The robustness and potential of AI is due to the fact that it’s not just a single type of technology. AI represents diverse interconnected group of technologies that include processing of natural languages towards human-computer interactions improvement and machine learning where once data is exposed to computer programs they’re able to “learn” from it. Another area of AI include offering advice-based software programs in expert systems, which basically aid machines in understanding, sensing and acting in different yet similar ways like the brain of a man.
These technologies are already in use and seen in various segments of the economy such as virtual online agents, where animated characters are generated by the computer and serve as customer care experience agents on the web.
AI is also seen in identity analytics in areas where solutions that blend advanced analytics and big data aid in the management of user certification and access as well as recommendation processes where algorithms are able to help match providers of commodities and users; a development that has already changed the way companies approach customer interaction and experience.
One critical way AI could bring banking to the twenty-first century is by offering better ways of restructuring and reimagining their models and processes of operation. The larger the bank the huge amount of data that need processing if financial reports are to be generated and requirements for compliance and regulatory satisfied.
They are generally formulaic and largely standardized processes yet still require a huge manpower to work on tasks that add little value usually in consolidation and reconciliation; so many of these tasks are under RPA (robotic process automation) consideration. RPA uses bots that could be coded to satisfy a number of exceptions and rules.
Nevertheless, with machine learning even most complicated challenges and ever-changing tasks could be well taken care of through a blend of AI and RPA.
It’s expected that in the coming years AI will be able to change lots of the central yet critical finance functions. These include intercompany finance reconciliations, including reporting on quarterly earnings while engaging in very strategic actions such as forecasting, asset allocation and monetary analysis. In all these, AI will mean accurate and fast processing of financial disclosures and reporting that can be carried out in real-time or much faster.
AI empowered finance teams don’t have to wait until quarters are concluded. The technology is able to identify glaring issues and come up with adjustments really fast boosting accuracy and eliminating period-end rush evident in lots of banks today.
In Fintech, AI has remained a buzz word for years but very little development has been monetized and scaled up. What has been observed are very advanced modeling methods like machine learning, which has been critical in supplementing customary Fintech analytics. Even so, the great potential of AI may be obvious but should be seen as an evolving game changer and not a giant leap into better data methods and sources.
A good example is the use of AI by credit underwriters to make sense of huge substitute data sources right from typical social media usage to mobile phone activity. However, even these haven’t displaced the old-fashioned methods of credit underwriting still in use. For instance, repayment history still remains a better way of predicting creditworthiness than mobile phone use and social media activity.
It means that financial companies such as lenders may continue blending machine-learning methodologies to enhance their immediate performance and don’t need to make gigantic leap into AI just yet but grow with it.