These four new solution accelerators help financial services and insurance firms solve complex business challenges by discovering meaningful relationships between events that impact one another (correlation) and cause a future event to happen (causation). Following the success of Synechron's AI Automation Program – Neo, Synechron's AI Data Science experts have developed a powerful set of accelerators that allow financial firms to address business challenges related to investment research generation, predicting the next best action to take with a wealth management client, high-priority customer complaints, and better predicting credit risk related to mortgage lending. The Accelerators combine Natural Language Processing (NLP), Deep Learning algorithms and Data Science to solve the complex business challenges and rely on a powerful Spark and Hadoop platform to ingest and run correlations across massive amounts of data to test hypotheses and predict future outcomes. The Data Science Accelerators are the fifth Accelerator program Synechron has launched in the last two years through its Financial Innovation Labs (FinLabs), which are operating in 11 key global financial markets across North America, Europe, Middle East and APAC; including: New York, Charlotte, Fort Lauderdale, London, Paris, Amsterdam, Serbia, Dubai, Pune, Bangalore and Hyderabad. With this, Synechron's Global Accelerator programs now includes over 50 Accelerators for: Blockchain, AI Automation, InsurTech, RegTech, and AI Data Science and a dedicated team of over 300 employees globally.
On November 15th, my credit risk analytics course will be available as e-Learning. Send me an email at Bart.Baesens@gmail.com Bart Baesens holds a master's degree in Business Engineering (option: Management Informatics) and a PhD in Applied Economic Sciences from KU Leuven University (Belgium). He is currently an associate professor at KU Leuven, and a guest lecturer at the University of Southampton (United Kingdom). He has done extensive research on data mining and its applications.
Credit risk analytics in R will enable you to build credit risk models from start to finish. Accessing real credit data via the accompanying website www.creditriskanalytics.net, you will master a wide range of applications, including building your own PD, LGD and EAD models as well as mastering industry challenges such as reject inference, low default portfolio risk modeling, model validation and stress testing. This book has been written as a companion to Baesens, B., Roesch, D. and Scheule H., Credit Risk Analytics: Measurement Techniques, Applications, and Examples in SAS, John Wiley & Sons, 2016. Bart Baesens is a professor of Big Data and Analytics at KU Leuven (Belgium) and a lecturer at the University of Southampton (United Kingdom). He has written more than 200 scientific papers and 10 books.
Machine learning technology is poised to be huge thing in financial services. In fact, two-thirds of UK-based firms are already using it. That is according to two of the UK's top financial regulators. The Financial Conduct Authority (FCA) and the Bank of England have taken a deep dive into how the financial services industry in the country is using machine learning. The research is based on a survey sent out to 300 firms, including banks, credit brokers, e-money institutions, financial market infrastructure firms, investment managers, insurers, non-bank lenders and principal trading firms.
If you don't prepare, it might vanish forever. LONDON -- Christine Lagarde, the head of the International Monetary Fund, has a message for the world's central bankers: Don't be Luddites. Addressing a Bank of England conference, Lagarde conceded that digital currencies, such as bitcoin, don't currently pose a major threat to the status quo, as they are "too volatile, too risky, too energy-intensive." Some have also been hacked, she noted. But in time, she argued Friday, technological innovations could address some of these issues and as such it "may not be wise to dismiss virtual currencies."