Technology advancement is the result of human labor. However, developing concepts in artificial intelligence, automation, and machine learning has done a significant amount of the job for us. Changes in customer service, workflows, and business procedures open up new possibilities, deal with outdated habits, and ultimately pave the way for a more secure and confident future. The banking and financial industry is a fantastic illustration of how businesses may adapt to contemporary concepts. This article will look at the relationship between machine learning and FinTech, its motivations in the industry, and its potential applications.

The financial services industry has seen a great deal of disruption from digital-based alternatives. Many of these challengers use advanced technology and expanded data sets to offer apps that provide financial solutions at a lower cost, with less friction and greater personalization than traditional bank or credit union offerings. Toronto-based startup Flybits believes that the best way to compete in the future is not just by developing innovative products and services, but by becoming the repository of choice for data in addition to money. "I definitely see that banks are in a perfect position, if they innovate right, to be the perfect data vaults for the future – managing the privacy and also the data of their customers," says Hossein Rahnama, CEO and Co-Founder of Flybits, in an exclusive interview for Banking Transformed, a new podcast from Jim Marous and The Financial Brand. "Using AI and machine learning, there is the potential to build a'data marketplace' for banks, fintechs and other data providers to partner and build more services together."

In a galaxy [not at all] far, far away… was a commonly accepted idea that the bigger banks could hire the best talent, implement state of the art technologies, and ultimately enhance bottom line revenues via improved risk modeling, novel product offerings, etc. This concept is the proverbial'black box' of large banking institutions and how their success has been perceived for many years, which in broad strokes, is fairly accurate. But what has changed in the recent years and why are smaller institutions so keen on getting their hands on this sexy new tech? For starters, those huge banks have made extraordinarily large investments over the past decade in order to continue validation of their claim to the top rungs of industry and to service the largest clients available. However, thanks to their valiant efforts of progressivism, and some well-placed IPO funding rounds of promising AI unicorns, they managed to provide an industry fresh off its second'AI winter' with the funding necessary to inspire entirely new solutions applicable to the broader public. The other important aspect of this boom in a large bank's successful employment of data science applications is attributed to the vast quantities of data amassed at a scale that's exponentially larger than that of smaller banks.

The global banking industry is witnessing accelerated growth in terms of increasing adoption of digitization, rising use of AI enabled solutions and rising digital transformations. The opportunities to implement AI solutions across the finance industry are increasing with time and changing circumstances. Banks and other financial institutions are highly aware of the benefits offered by these AI solutions. The infusion of AI solutions is one of the latest trends in the banking industry. The banking industry generates a huge amount of data and AI solutions will help in connecting the dots between various data points and would alter the approach of acting with their users, therefore, enhancing the overall customer experience. Certain AI solutions have already gained a lot of popularity across banking institutions.

They say nature abhors a vacuum, and apparently so do predatory and payday lenders. As people hit by COVID-19 loss of jobs or businesses have struggled to make ends meet and experienced credit rejections or delays in government support, those lenders have stepped in to fill the gap. For many consumers, what looks like a quick fix for their finances ends up a debt trap that's incredibly tough to escape. Predatory lenders provide unsecured bridging loans, at high interest rates, which are due for repayment only weeks later. During COVID-19, these lenders have been aggressively pitching their products to the millions of consumers in need of cash.

With plenty of post-recession anti-banking sentiment still lingering, it's common to see fintech and traditional banks framed in oppositional terms. There's some truth to that, especially with disruption-minded digital-only banks, but technological innovations have transformed banking of all stripes -- and nowhere is that clearer than with artificial intelligence. AI has impacted every banking "office" -- front, middle and back. That means even if you know nothing about the way your financial institution uses, say, complex machine learning to fend off money launderers or sift through mountains of data for fraud-related anomalies, you've probably at least interacted with its customer service chatbot, which runs on AI. Like fabric softener and football, banks -- or at least banks as physical spaces -- have been cited as yet another industry that's being killed by those murderous Millennials.

In case you missed the first round of our AI Assistants vs Chatbots blog series, here's a brief recap of the bout. With the rise of artificial intelligence, we have seen the widespread use of two automated conversation tools: AI assistants and chatbots. The differences might not be clear on first glance, but they are vast. Let's look at what happened in the first round. On the one hand, we have the chatbots.

With plenty of post-recession anti-banking sentiment still lingering, it's common to see fintech and traditional banks framed in oppositional terms. There's some truth to that, especially with disruption-minded digital-only banks, but technological innovations have transformed banking of all stripes -- and nowhere is that clearer than with artificial intelligence. AI has impacted every banking "office" -- front, middle and back. That means even if you know nothing about the way your financial institution uses, say, complex machine learning to fend off money launderers or sift through mountains of data for fraud-related anomalies, you've probably at least interacted with its customer service chatbot, which runs on AI. Read on to learn how else AI is transforming the way banks operate, from investment assistance and consumer lending to credit scoring, smart contracts and more.

The financial services industry has seen a great deal of disruption from digital-based alternatives. Many of these challengers use advanced technology and expanded data sets to offer apps that provide financial solutions at a lower cost, with less friction and greater personalization than traditional bank or credit union offerings. Toronto-based startup Flybits believes that the best way to compete in the future is not just by developing innovative products and services, but by becoming the repository of choice for data in addition to money. "I definitely see that banks are in a perfect position, if they innovate right, to be the perfect data vaults for the future – managing the privacy and also the data of their customers," says Hossein Rahnama, CEO and Co-Founder of Flybits, in an exclusive interview for Banking Transformed, a new podcast from Jim Marous and The Financial Brand. "Using AI and machine learning, there is the potential to build a'data marketplace' for banks, fintechs and other data providers to partner and build more services together."

With plenty of post-recession anti-banking sentiment still lingering, it's common to see fintech and traditional banks framed in oppositional terms. There's some truth to that, especially with disruption-minded digital-only banks, but technological innovations have transformed banking of all stripes -- and nowhere is that clearer than with artificial intelligence. AI has impacted every banking "office" -- front, middle and back. That means even if you know nothing about the way your financial institution uses, say, complex machine learning to fend off money launderers or sift through mountains of data for fraud-related anomalies, you've probably at least interacted with its customer service chatbot, which runs on AI. Read on to learn how else AI is transforming the way banks operate, from investment assistance and consumer lending to credit scoring, smart contracts and more.