DC14: Challenges and opportunities for the uptake of technological development by industry

Objectives

The building blocks of any institutional investor's loan portfolio are cash flows. Using public and proprietary data, the DC will conduct research and develop a machine learning tool capable of performing grouped time series forecasting on a private debt portfolio spanning multiple geographies, sectors, and whose features can also be grouped at other levels, such as loan amount and interest rate. In our innovation-driven industry, we analyse the obstacles and opportunities associated with adopting technological advances.

Expected Results

The project's outcomes will contribute to the expanding body of knowledge concerning the applications of cutting-edge machine learning and artificial intelligence techniques to traditional financial problems. Specifically, the first phase of the project will concentrate on missing value imputation for loan payment time series, while the second phase will adopt a more general predictive approach, that of grouped time series forecasting, possibly incorporating the first step. The anticipated outcome will be three research/conference papers describing the data analysis, modelling approaches, and experimental results.

Secondments (1)

Recruitment & Hosting Details

DC 14 CAR University of Kaiserslautern-Landau M9 36

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DC 14 CAR UKL Month 9 36 months D 2.3

Challenges and opportunities for the uptaking of technological development by industry (WP 2)

Objectives: The building blocks of any institutional investor's loan portfolio are cash flows. Using public and proprietary data, the DC will

conduct research and develop a machine learning tool capable of performing grouped time series forecasting on a private debt portfolio

spanning multiple geographies, sectors, and whose features can also be grouped at other levels, such as loan amount and interest rate. In

our innovation-driven industry, we analyse the obstacles and opportunities associated with adopting technological advances.

Expected Results: The project's outcomes will contribute to the expanding body of knowledge concerning the applications of cutting-edge

machine learning and artificial intelligence techniques to traditional financial problems. Specifically, the first phase of the project will

concentrate on missing value imputation for loan payment time series, while the second phase will adopt a more general predictive approach,

that of grouped time series forecasting, possibly incorporating the first step. The anticipated outcome will be three research/conference

papers describing the data analysis, modelling approaches, and experimental results.

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Project: 101119635 — DIGITAL — HORIZON-MSCA-DN-2022

Planned secondments: Kaiserslautern-Landau, Prof. Dr. Ralf Korn, M33, 12 months, contribution to the theoretical and applied

expertise in machine learning, times-series forecasting and credit portfolio analysis

Fellow Host institution PhD enrolment Start date Duration Deliverables

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Deliverables