HSE Opens Laboratory of Financial Data Analysis
Part of the Centre of Deep Learning and Bayesian Methods and another partner project between Sberbank and HSE University’s Faculty of Computer Science, the laboratory will focus on applying machine learning methods to financial services.
The lab’s research agenda includes the interpretation of complex neural network models, reinforcement learning, natural language processing, and competing networks (GAN) in directional information removal from samples. The laboratory will be headed by Evgeny Sokolov, Deputy Head of the Big Data and Information Retrieval School at HSE. According to Sokolov, the idea of creating a new research unit was a natural continuation of joint projects with Sberbank—a bank HSE has been collaborating with for years.
There is a great need for specialized research, insofar as methods—such as credit risk assessment, support service automation, and marketing personalization—often require refinement when applied to banking.
Now, large banks are introducing machine learning more and more, both for basic tasks and for new areas such as creating chat bots
One of the most common areas machine learning is applied to is credit scoring. A computer model processes data about people who have already paid a loan—their gender, age, marital status, income level—and finds patterns in them. The combination of these factors is then used to determine how much the bank risks when issuing a loan.
But it is not enough to teach the neural network to qualitatively predict the borrower's creditworthiness based on the available data. The bank must be able to justify its final decision—this is a requirement of the Central Bank of the Russian Federation, which regulates credit institutions. Due to the difficulties in interpreting the predictions of complex neural networks, simplified, less accurate models are still used in practice.
If banks were allowed to use neural networks, the quality of credit risk assessment would increase dramatically
According to Sokolov, the lab plans to recruit students from the Master’s Programme ‘Financial Technologies and Data Analysis’, which was launched jointly with Sberbank in 2017, to work as interns in the lab. Interns will be able to participate in developing solutions to challenges in machine learning with the guidance of experienced researchers, as well as get to know the inner workings of the bank and interact with its developers and data scientists.
Part of the laboratory research will involve not only banking but other financial spheres. A major business trend is automatizing customer support services. Every day customer service call centers receive tens of thousands of calls from clients with problems that are similar to one another. About 80% of responses to these requests can be provided by a template, and a chat bot can handle them. Once you accumulate enough data, you can begin to automate processes by using machine learning and natural language processing.
The Master's Programme in Statistical Learning Theory was launched in 2017. It is run jointly with the Skolkovo Institute of Science and Technology (Skoltech). The programme trains future scientists to effectively carry out fundamental research and work on new challenging problems in statistical learning theory, one of the most promising fields of science. Yury Kemaev and Maxim Kaledin, from the first cohort of programme graduates, sat down with HSE News Service to talk about their studies and plans for the future.
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Researchers at HSE’s Laboratory of Methods for Big Data Analysis (LAMBDA) and the Yandex School of Data Analysis have significantly reduced the cost of CERN’s future SHiP detector. The detector will search for particles responsible for still unexplained phenomena in the Universe. With use of modern machine learning methods, LAMBDA and Yandex scientists came up with very effective configuration of magnets which protect the detector from background particles. As a result, the cost of the entire structure was reduced by 25%.
Samsung-HSE Laboratory will develop mechanisms of Bayesian inference in modern neural networks, which will solve a number of problems in deep learning. The laboratory team will be made up of the members of the Bayesian Methods Research Group — one of the strongest scientific groups in Russia in the field of machine learning and Bayesian inference. It will be headed by a professor of the Higher School of Economics Dmitry Vetrov.
On February 20, the first online stage of the International Data Analysis Olympiad (IDAO) was completed. IDAO was organised by the Faculty of Computer Science of the Higher School of Economics in partnership with Harbour.Space University (Barcelona), Yandex and with the Gold sponsor, Sberbank.
The IDAO (International Data Analysis Olympiad), created by leading experts in data analysis for their future colleagues, aims to bring together analysts, scientists, professionals, and junior researchers from all over the world on a single platform. This is the first time an event of this scale will be held in Russia. The HSE Faculty of Computer Science, Yandex and Harbour. Space University organize the Olympiad with the support of Sberbank.
HSE students from the Faculty of Computer Science and the Institute of Education have taken part in the ‘Put Together Your 2035 University’ hackaton. The theme of the hackaton was concerned with developing and designing AI solutions (with the use of machine learning) in education. HSE students took third place and made it through to the finals!