University of Twente and the ING Group have put their signatures to a five-year collaboration agreement covering artificial intelligence and data science in the financial world. The partnership was celebrated at FinanceCom 2022, a leading-edge conference hosted by UT. It marks the first time that this international congress in finance and fintech has been held in the Netherlands. Jos van Hillegersberg, Professor of Business Information Systems at UT, and recently appointed Academic Director of Jheronimus Academy of Data Science, is looking forward to the collaboration between UT and ING. "We in the Netherlands have been pioneering and innovating applications involving artificial intelligence for quite some time. There are lots of opportunities in the financial sector. But we also ran into a problem: there's an enormous demand for talent in the business community and academic world. Our partnership will help alleviate this shortage. The fact that ING will be actively investing in UT's academic knowledge already says a lot."

How well do you really know your bank's customers? Have you met them at all? Do you know where and how they shop, what their interests and aspirations are, or their general state of mind and personal circumstances? But of course, no one has psychoanalysis tools or magical algorithms to find such data on each consumer, to see exactly where each cent of their money goes to help them in their financial lives. But this data is important. Why? Consumers are desperate for financial help.

However, some people believe that there are some negative aspects that come along with the adoption of AI in specific work environments. They think that the problems that arise are more severe than the improvements that will be made to the current'working-life': Since the data that is being handled is overly sensitive, the case of errors, faults, and failures will inevitably end up costing the bank a lot of money and time. Although AI might increase the productivity of the specific job that is being replaced, this may lead to lower morale and effort from the other employees, as they will also begin to feel'replaceable'. In turn, this will cause a depreciation in their efficiency (resulting in firing/termination) or voluntary resignation. This will place an overall burden on places such as banks, as AI can replace the tellers, CSEs, and various officers.

Machine Learning and Data Science have been buzz words for a long time. While its inherent advantages have come to light only in the recent decade, you will learn why it wasn't popularized prior to the development of modern Graphics Processing Units(GPU) and Computer Processing Units(CPU). Even veteran Data Scientists struggle to come up with a precise definition of the topic. Most machine learning algorithms map from input to output by adapting and learning the correlation between the different categories in the given data. The reason it is referred to as Machine Learning is its characteristic ability to learn patterns in data and apply it to real-world scenarios. Some of its popular applications range from tailored advertisements on social media to product recommendations on online marketplaces like Amazon.

Life sans credit is impossible to imagine. Since the cost of living is rising and income is stagnant, people are relying on borrowing to meet their unexpected expenses. It is hard to set aside money for a rainy day when the cash stream flowing in is very thin. With each passing year, the number of borrowers are rising and therefore hinging on credit score and repaying capacity for the loan approval have become more requisite than ever. Banks and other financial institutions need to consider numerous factors when it comes to signing off on loan.

Until recently, artificial intelligence has struggled to gain a foothold on Wall Street. In the last few years, large investment banks like Goldman Sachs and JP Morgan have hired artificial intelligence specialists away from academia and put them in charge of their internal AI divisions. Financial technology start-ups have begun using machine-learning algorithms to model credit ratings and detect fraud. And hedge funds and high-frequency traders are using AI to make investment decisions. Politicians are starting to take notice.

Until recently, artificial intelligence has struggled to gain a foothold on Wall Street. In the last few years, large investment banks like Goldman Sachs and JP Morgan have hired artificial intelligence specialists away from academia and put them in charge of their internal AI divisions. Financial technology start-ups have begun using machine-learning algorithms to model credit ratings and detect fraud. And hedge funds and high-frequency traders are using AI to make investment decisions. Politicians are starting to take notice.

Financial technology (Fintech) companies are finding new ways to meet consumer demands and create more financial inclusion on a global scale. While Fintechs are on the rise, these companies still have to manage the same problems traditional financial institutions face: fraud. And while fraud permeates throughout nearly all aspects of a financial transaction, one particular area of concern is onboarding. Client onboarding is when a new client begins their relationship with the fintech. Companies naturally want to make this process easy and simplified, but in the financial world, this can be complicated.

Diego Caicedo is the Co-Founder and CEO of OmniBnk, a neobank that provides financial services to small businesses in Latin America. Artificial intelligence and machine learning are said to revolutionize the financial world, changing the banking experience for the better. The implications of the technology are vast, though most banks are still in the early stages of adopting AI technologies. A survey by Narrative Science and the National Business Research Institute found that 32% of financial services executives confirmed that they are already using AI technologies such as predictive analytics, recommendation engines, and voice recognition. One major hindrance to AI adoption is legacy systems.

They can only give you answers." Big data utilization in economics and the financial world has increased with each passing day. In previous reports, we have discussed issues and opportunities related to big data applications in economics/finance. This piece is a quick summary of a more-detailed report that outlines a framework to utilize machine learning and statistical data mining tools in the economics/financial world with the goal of more accurately predicting recessions. Decision makers have a vital interest in predicting future recessions in order to enact appropriate policy.