Prof. Dr. Thorsten Koch

Zuse Institute Berlin & Technische Universität Berlin, Germany

Solving QUBOs on digital and quantum computers
Combinatorial optimization is to find optimal solutions to efficiently allocate limited resources, which has been widely used in almost all fields of e.g., finance, marketing, production, scheduling, inventory control.  It is regularly claimed that quantum computers will bring breakthrough progress regarding the solution of challenging combinatorial optimization problems relevant in practice. In particular, Quadratic Unconstraint Binary Optimization (QUBO) problems are said to be the model of choice for the use in (adiabatic) quantum systems. We explain some of the meaning and implications, review the state of affairs, and give some computational results to underpin our conclusions.

Junye Huang & Radha Pyari Sandhir

IBM

Tutorial: Hands-on Introduction to Quantum Finance
This hands-on workshop introduces you to the world of quantum finance. You will learn how to use quantum computers to solve problems in the finance services sector such as portfolio optimization and option pricing as well as the basic concepts of quantum computing through programming with Qiskit (open source quantum SDK developed by IBM) and IBM Quantum Services.

Prof. Dr. Wolfgang Karl Härdle

Humboldt Universität zu Berlin, Germany

代 DAI - the Digital Art Index: Cryptos, Blockchains, NFTs
Non fungible tokens (NFTs) are digital assets, mostly derived on an ETH Blockchain, have recently gained huge interest from investors, and the market sentiment went to a climax when some artwork sold at unforeseeable level. We create 代 DAI - a price index for the digital art market by collecting over 300,000 transactions from the top 10 collections of NFTs ranked by transaction volume. The BRC blockchain-research-center.de data is collected jointly with artnet.com and covers over 80% of the total volume of the market, and we apply hedonic regression in a panel setting and thereby we identify which factors drive the price of NFTs.

Dr. Xinxin Fan

IoTeX, USA

MachineFi: Unlocking Trillion-Dollar Machine Economy
In this talk, Dr. Xinxin Fan will introduce MachineFi, an IoTeX's initiative for unlocking the emerging trillion-dollar machine economy and enabling people to truly own their devices and data. After explaining the basic concept of MachineFi, Dr. Fan will present the MachineFi technology stack that faciliates developers to bring a wide range of smart devices onboard for building innovative decentralized applications. Finally, Dr. Fan will discuss some typical use cases of MachineFi.

Kelvin Tan

DBS (Singapore)

DBS’ perspective on Blockchain, Crypto, Digital Assets and Digital Currencies
Blockchain / Distributed Ledger Technology introduced by bitcoin has created innovations in many areas, including a crypto market that is now worth US$2T to US$3T. How is DBS approaching this new technology as well as the opportunities and threats brought about by it? In this talk, I will present the recent advances and industrial implementations on Blockchain, Crypto, Digital Assets and Digital Currencies from DBS' perspective.

Prof. Stefan Lessmann

Humboldt Universität zu Berlin, Germany

Fairness in Credit Scoring: Assessment, Implementation and Profit Implications
The rise of algorithmic decision-making has spawned much research on fair machine learning (ML). Financial institutions use ML for building risk scorecards that support a range of credit-related decisions. Yet, the literature on fair ML in credit scoring is scarce. The paper makes three contributions. First, we revisit statistical fairness criteria and examine their adequacy for credit scoring. Second, we catalog algorithmic options for incorporating fairness goals in the ML model development pipeline. Last, we empirically compare different fairness processors in a profit-oriented credit scoring context using real-world data. The empirical results substantiate the evaluation of fairness measures, identify suitable options to implement fair credit scoring, and clarify the profit-fairness trade-off in lending decisions. We find that multiple fairness criteria can be approximately satisfied at once and recommend separation as a proper criterion for measuring the fairness of a scorecard. We also find fair in-processors to deliver a good balance between profit and fairness and show that algorithmic discrimination can be reduced to a reasonable level at a relatively low cost.