banking 
Analytics Vidhya
JUNE 22, 2023
Macquarie’s Banking and Financial Services Group has joined forces with Google Cloud to harness the power of artificial intelligence (AI) and machine learning (ML) in an exciting collaboration to revolutionize the banking industry.
Analytics Vidhya
MARCH 27, 2023
The recent advancements in the banking and finance sector suggest an affirmative response to this question. The keys to business success are sophisticated, intelligent security systems […] The post Applications of Machine Learning and AI in Banking and Finance in 2023 appeared first on Analytics Vidhya.
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Analytics Vidhya
DECEMBER 31, 2021
This article was published as a part of the Data Science Blogathon Financial Companies and Banks have a lot of data. Nowadays, customers do a lot of online transactions, make purchases with their card, others use the Bank’s mobile app, website, and so on. Banks have a lot of data about their customers. The data which […].
Analytics Vidhya
FEBRUARY 24, 2023
Thanks […] The post Deep Learning in Banking: Colombian Peso Banknote Detection appeared first on Analytics Vidhya. This process could be time-consuming for everyday business professionals and individuals dealing with cash. This calls for a need to achieve this goal via automation.
Advertiser: Data Robot
Banks have always relied on predictions to make their decisions. Today, banks realize that data science can significantly speed up these decisions with accurate and targeted predictive analytics. Today, banks realize that data science can significantly speed up these decisions with accurate and targeted predictive analytics.
Analytics Vidhya
DECEMBER 3, 2021
The post Bank Note Authentication using Keras Sequential Model appeared first on Analytics Vidhya. Keras is an open-source library that can be used to implement artificial neural networks in Keras. Keras has all the major features required to build and train artificial neural networks. […].
Analytics Vidhya
SEPTEMBER 27, 2022
The post Bank Customer Churn Prediction Using Machine Learning appeared first on Analytics Vidhya. For many companies, this is an important prediction. This is because acquiring new customers often costs more than retaining existing ones. Once you’ve identified customers at […].
Speaker: Alex Jiménez, Managing Principal, Financial Service Consulting for EPAM
From a budget standpoint, US banks are feeling the pinch. Many US banks are bracing for increased defaults and lower demand for mortgages and other loans as interest rates have increased. The largest banks have increased reserves to protect against deteriorating economic conditions. Global economic conditions are soft at best.
Speaker: Ryan McInerny, CAMS, FRM, MSBA - Principal, Product Strategy
May 18th, 2023 at 9:30 am PDT, 12:30 pm EDT, 5:30 pm BST
Speaker: Elizabeth "Paige" Baumann, Founder and CEO of Paige Baumann Advisory, LLC
Paige Baumann will walk us through the impacts of the AMLA, and you'll leave this session with a new understanding of: The catalysts for this new law and how it amended the Bank Secrecy Act of 1970 Rulemaking and other actions taken by the U.S. The AMLA represents the most significant changes in U.S.
Advertiser: Data Robot
AI has the power to transform countless industries — including the healthcare, banking, insurance, and public service sectors, to name just a few — by introducing new efficiencies and revealing new opportunities for companies to solve problems.
Advertiser: Data Robot
Banks have always relied on predictions to make their decisions. Today, banks realize that data science can significantly speed up these decisions with accurate and targeted predictive analytics. Today, banks realize that data science can significantly speed up these decisions with accurate and targeted predictive analytics.
Speaker: William Hord, Senior VP of Risk & Professional Services
Does our bank understand and support the importance of a strong ERM program to continue to position our company for growth? How are we measuring and rating our risk impact, likelihood, and controls to mitigate our risk? Are we leveraging risk velocity and vulnerability to obtain more granular residual risk results?
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