How big data and AI helping the FinTech business operate remotely.
Thanks to the pandemic, we have become accustomed to lockdowns and corresponding restrictions in resuming ‘normalcy’ at work, i.e. working physically in the office from 9 to 5. The emergence of WFH/remote working has defined the new normal, which has panned out well for many companies, especially those in the FinTech industry. As FinTech organisations deal with an enormous amount of sensitive information data, they have had to use the best technology for seamless flow of operations – from processing customer information to systematic transactions and the like. Big Data and AI have been harnessed in this manner by FinTech companies to great success. The use of the latest and cutting-edge technology is crucial for the FinTech industry, whether there is a looming pandemic or because customers and/or potential partners will expect them to use the best of everything to do business with them.
The major areas where Big Data and AI have played a significant and conventional role are fraud detection, algorithmic trading as well as steady monitoring of markets. When fraud is detected in a particular transaction or finance portfolio, AI models can flag them for further investigation or reject them altogether. AI can also predict clients/customers’ usual behaviour and alert the company if something suspicious is taking place. It does not require the physical presence of an employee to go through every detail in depth – the AI system is ‘smart’ enough to do this in an automated fashion. In the case of monitoring markets, AI and Big Data allows for an analysis of the current market trends and draw up visual schematic reports that will put the routine physically prepared reports to shame. Trading is also made easy by AI as past, and current value along with future stock value predictions can be drawn up with ease.
Another rallying point for the employment of AI and Big Data in FinTech is that it increases the overall security of the systems in place, especially when operations are happening remotely in case of an unprecedented lockdown. Chatbots can convert customer chats into whole simulated conversations, help them retrieve lost passwords, and grant access only when they have entirely verified all the details. As mentioned earlier, this is also a form of fraud prevention that does not require human interference. It is to be noted that customer service, in general, has been enhanced by the use of AI via Machine Learning. The ability of these chatbots to reduce the time taken in understanding and responding to basic queries has enabled FinTech companies to reduce helpline and front office costs.
We know that lockdowns had put immense pressure on people working from home as they had to juggle both office work and the house without domestic help. AI played a significant role in making the job of FinTech employees much easier in many ways. For example, expenditure reports could be automated and directly sent to the inbox of stakeholders. AI also made employees’ work more undemanding by helping with data entry and compliance processes. The most important factor here is that AI significantly reduces the margin of error as well, making them highly reliable in unfavourable conditions.
Credit scores can also be efficiently calculated using AI, including judging the potential client’s entire credit history using credit variables like job profile, web history, social media activity, and geo-location. It makes FinTech companies work far more actively than traditional financial institutions like banks during lockdowns – the work keeps on running without a glitch and more or less can be surmised as ‘business as usual. AI has enabled both FinTech companies as well as their customers and investors to make better decisions. In the continuing paradigm of remote working, there is scope for AI and Big Data to become an undeniable and inseparable component of fintech. For this to happen, major players in the FinTech industry should be able to collaborate and partner with a range of AI designers, developers, and other ‘tech people’ to ensure that better systems can be devised. Along with commercial success, the sanctity of such systems will have more significance – how human physical operations can go hand-in-hand with AI technology will define the future of FInTech.