AI News, AI and Machine Learning in Fintech. Five Areas Which Artificial ... artificial intelligence

RegTech Universe

RegTech, the “new Fintech” as we have called it, promises to disrupt the regulatory landscape by providing technologically advanced solutions to the ever increasing demands of compliance within the financial industry.

You can follow us on LinkedIn and Twitter to be sure to catch our next updates, and we would of course be more than happy to hear your take on RegTech, so please do not hesitate to get in touch with us.

Can AI Turn Banks Into FinTech Firms?

There are no incumbent banks that are not paying attention to what the challenger banks are doing. What might have been written off even five years ago as a fad for digital financial services companies, or a niche product for a subset of consumers, is now “top of mind” for banks of all sizes, Jim Priestley, chief revenue officer at Feedzai, told PYMNTS in a recent conversation.

“A lot of them have been saying it for a while, but more and more of them are actually building data science and technology teams that really get it.” What they are getting is that consumers want, and increasingly demand, a suite of services on the front end that make the financial management of their lives easier and more seamless. That includes, at the most basic level, mobile and digital banking.

For banks to provide those services, it is going to take more than desire and will to get it done. Legacy banks, he explained, need to get free of their legacy systems, as well as embrace artificial intelligence (AI) and machine learning technologies that build a more secure experience on the back end, and a more accessible experience on the front end.

“The faster your fraud model works is a differentiator.” What AI and machine learning bring to the table for financial institutions (FIs) — particularly the kind of unsupervised AI that Feedzai favors in its own products — is being able to take the information from the data sets they have been trained on, and incorporate (and iterate off of) new data as it comes in. What that essentially means (from a consumer banking point of view) is that, in real time, the system can make a determination transaction by transaction, instead of being handcuffed to a set of rules.

“They just aren’t as mature.” That is costly for banks in terms of fines, which are steep — and also in terms of the reputational damage that comes with the adverse media that tends to follow money laundering events. Better systems can stop the fraudulent depositing so that the AML violations aren’t happening, and help banks better track the adverse media.

It is another bit of evidence to add to the mounting pile that the world of financial services is rapidly evolving into a different type of technologically moderate ecosystem — where legacy and challenger banks have much to learn from each other still. Legacy banks need to develop more of an ability to act like fast-acting, fast-interacting tech companies.

How Artificial Intelligence is Revolutionizing the FinTech Industry: Jaya Vaidhyanathan, Bahwan CyberTek

Industry 4.0 is driving the quest to enable technological interventions to see transformations resulting in automation of backend business processes which in turn reduces the turnaround time creating a seamless customer experience.

AI and ML brought in automation to mundane tasks and enables predictive analysis to mitigate risks, blockchain and cloud allows sensitive data to be safe yet accessible at all times, and most importantly all this also enhanced customer experience with chatbots, virtual assistance and assurity that customers’ money is safe.

Combining our domain expertise and understanding of industry challenges and regulatory nuances, BCT has developed a suite of digital products and industry solutions that are designed to address business challenges, increase margins, improve customer experience and help organisations seize new growth opportunities by leveraging our experience in analytics, machine learning, AI, IoT and other digital technologies.

In a digital era where customers and other stakeholders expect lively experience to be delivered even by by brands in “serious” industries like banking or manufacturing, BCT’s suite of digital experience solutions comprising DropThought, Pulse, and CueTrack provides a range of solutions like real-time feedback tracking and analytics, social media sentiment analytics and end-to-end case management.

BCT’s Digital journey management suite for Oil and Gas and Supply Chain verticals – CueTrans enables organisations keep internal operations in sync through end-to-end planning, optimization and tracking of logistics operations Kindly shed some light on the Evolving regulatory landscape for financial institutions and how technology can help in coping with these challenges?

Financial institutions today should look at adopting predictive analytics solutions allowing them to create an EWS that alerts bankers to indicators of incipient stress in accounts much before they reach the default stage, so that corrective action can be taken, and the loan amount recovered.

Machine Learning Automation – What Kind of Tasks Can Be Improved and Automated in the Near Future

Machine learning brings a wide range of new possibilities to nearly all industries.

Let’s dive deeper and try to establish factors that enable a specific task to be improved with ML, so we can understand better how your business and processes can leverage artificial intelligence.

see four main sources or processes that will make new kind of data available: Internet of Things is a hot topic right now, and it will bring a great new pool of data that are not being processed right now.

When you hear this and realize that you’re sitting on a pot of gold, you start to look for parties interested in paying you for the access to the data that you on your side cannot make use of.

For years a great example of this have been retailers who had a lot of information about the consumers and their shopping behaviour that they shared with brand owners and marketing agencies so that they could gain insights about customers.

That’s where machine learning comes in handy – there are already some algorithms being developed that can transform pictures into meaningful data that can be used in the further analysis, be it a traditional one or an analysis involving machine learning.

Let’s look at the most interesting (in my opinion) methods that are being developed, or are about to migrate from the papers to reality in the near future: When people started to use language, a new era in the history of humankind started.

definition from Wikipedia may shed some light on what it really is: reinforcement learning is an ‘(...)area of machine learning concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward.’ So it’s all about creating programs that interact with the environment (a computer game or a city street) to maximize some reward, taking feedback from the environment.

Here, I’d also like to shed some light on the constraints that can hamper the machine learning revolution in your business: If you want to get a personalized assessment of your business against machine learning, fill out this form.

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