Fintech companies and other businesses throughout the world have spent a lot of money altering to meet the demands of the new normal – remote working, social alienation, and a corporate environment that has altered forever.
The economic consequences from the epidemic, on the other hand, has expedited the schedule for financial services organizations to embrace AI and use its predictive capabilities sooner rather than later. This provides an opportunity for digitally native fintechs, many of which have already embraced AI and its possibilities, to invest even more in the technology.
Fintechs all across the world are grappling with the consequences of Covid-19, and they face an uphill battle in limiting its influence on the financial system and the broader economy. Individuals and businesses are grappling with debt as a result of growing unemployment and stagnant economies, while the globe as a whole is drowning in credit risk.
To make matters worse, criminals are taking advantage of the vulnerabilities created by the post-Covid-19 move to remote operations, raising the risk of fraud and criminality. Building and maintaining strong defenses has therefore become a top responsibility for fintechs.
Fintechs’ reaction to the crisis has been driven by technical improvements in data analytics, artificial intelligence (AI), and machine learning (ML), which has accelerated the automation journey many had already begun.
A fresh approach
Until recently, fintechs relied on traditional data analysis approaches for a variety of applications that involve complicated and time-consuming investigations, such as fraud detection and default prediction.
The financial sector as a whole can also train ML algorithms that can automate many of their operations and AI for operational resilience by leveraging the massive quantities of data sets already on record from various sources across many business units.
Kubeflow, for example, is a simple, portable, and scalable open source application designed to coordinate AL and ML processes operating on Kubernetes. It’s made for TensorFlow, a rich network of tools, libraries, and community resources that makes it simple for developers to create and deploy machine learning applications.
Apache Kafka, like any other large-scale application, demands extensive preparation and customization on the network, hardware, operating system, and application levels. Maintaining a big deployment may be difficult and time-consuming, and it demands ongoing monitoring and maintenance.
With so much uncertainty in the business as a result of Covid-19 and Brexit, open source tools and partners help fintech companies better deal with the repercussions while also allowing them to adapt and develop.
Fintech’s Future
Experts predict that as AI becomes more prevalent in finance, its use will extend across additional industries, resulting in increased crossovers and, as a result, conflicts, particularly in the area of data access.
The epidemic has expedited the transition away from physical to digital communication, which has impacted the whole banking industry.
Because of the rising usage of AI by cyber defense tech businesses, proactive techniques for combating assaults and securing important data from hackers will be available.
