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How Main Incubator is exploring the Quantum Future for Commerzbank AG

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In the banking business, there are many combinatorial design options, e.g. in the areas of portfolio or risk management. Main Incubator, the R & D unit of Commerzbank, is interested in the use of modern quantum technologies in order to digitally transform and optimize business processes. The presentation introduces the securitization business and shows the combinatorial aspects of the calculations and its implementation in quantum algorithms. The methodology to find the right subject, the know-how transfer and the cooperation of specialists at Main Incubator and Fujitsu will be presented. Special challenges, their solution and the finally achieved results complete the picture along with an outlook into the future.

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How Main Incubator is exploring the Quantum Future for Commerzbank AG

  1. 1. How Main Incubator is exploring the Quantum Future for Commerzbank AG Augustin Danciu Technology Expert main incubator GmbH Stefanie Lang Principal Consultant Financial Services Dr. Fritz Schinkel Fujitsu Fellow & Distinguished Engineer Digital Incubation
  2. 2. NOV 2019 main incubator R&D Unit for Commerzbank main incubator Augustin Danciu I Prototyping Fujitsu Forum – Break out Session I November 2019
  3. 3. NOV 2019 est. 2013 I offices in Frankfurt & London I 100 % subsidiary of R&D Unit for Commerzbank Group Prototype Ventures Community
  4. 4. NOV 2019 Prototype
  5. 5. NOV 2019 Artificial Intelligence: attempt of a computer to imitate certain tasks which mimic human thought processes Big Data: complex data sets characterized by high volume, velocity and variety Biometrics: authentication method through reading and analysing physical characteristics Blockchain/DLT: distributed ledger technology is a decentralized database Cloud: providing computing services such as servers, storage, databases and network components Machine Learning: the ability of a computer to act without being explicitly programmed Open API: public interfaces that allow access to other softwares etc. Robotics: automation of processes by robotics, already known in manufacturing industry XR: Extended Reality consists of augmented, virtual and mixed reality Wearables: computer technology that is characterized by its mobility Prototyping Labs I 12 Emerging Technologies Quantum Computing: high performing calculation based on qubits IoT: Internet of Things connects computers, smartphones etc. with machines, humans etc., basis for smart home etc.
  6. 6. NOV 2019 Project Quantum
  7. 7. NOV 2019 Business Problem Securitisation Receivables Sad car manufacturer, only with the promises to receive the money in the future Happy people with a new car Leasing / Financing Investors
  8. 8. NOV 2019 Eligibility Criteria • Maximum volume of 100 mio. Euro • Max. 10 % with a contract term of under 2 years • Max. x % from each state / country • … How to select assets for Securitisation? Receivable / asset x • Volume v = € 62,623.54 • Runtime r: 3.2 years • State s: Hesse • … Often hundreds of thousands Volume Runtime Country
  9. 9. NOV 2019 Optimum Selection by Annealing Digital Annealer finds minimum of energy function Energy Search space (state) 1 2 3 4 For set of contracts … 𝐸 𝑋, 𝑆 = ෍ 𝑖∈𝐼 𝑥𝑖 𝑣𝑖 + ෍ 𝑠=0 𝑆 2 𝑠 𝑦𝑠 −𝑉𝑚𝑎𝑥 2 … define a search space and energy function … … whose minimum is the optimum portfolio selection.
  10. 10. Digital Annealer – Asset Selection by Energy Minimization
  11. 11. 16 © 2019 FUJITSU € ------ --------- --------- -------d € ------ --------- --------- -------d € ------ --------- --------- -------d € ------ --------- --------- -------g € ------ --------- --------- -------g € ------ --------- --------- -------g € ------ --------- --------- -------e € ------ --------- --------- -------e € ------ --------- --------- -------e Portfolio Selection – Start Small For given set of gas, diesel, and electro car contracts… … find best rated gas-diesel-electro triple portfolio ? ? ?
  12. 12. 17 © 2019 FUJITSU 2 31 diesel gas electro choice drive 𝑥 𝑑,𝑐 Formalized Decision Model If choice for drive 𝑑 is 𝑐 then 𝑥 𝑑,𝑐 = 1 else 𝑥 𝑑,𝑐 = 0 Selection bit matrix 𝑋 = (𝑥 𝑑,𝑐) € ------ --------- --------- -------d € ------ --------- --------- -------d € ------ --------- --------- -------d € ------ --------- --------- -------g € ------ --------- --------- -------g € ------ --------- --------- -------g € ------ --------- --------- -------e € ------ --------- --------- -------e € ------ --------- --------- -------e 0 0 1 0 1 1 0 0 0
  13. 13. 18 © 2019 FUJITSU 2 31 diesel gas electro choice drive 𝑥 𝑑,𝑐 € ------ --------- --------- -------d € ------ --------- --------- -------d € ------ --------- --------- -------d € ------ --------- --------- -------g € ------ --------- --------- -------g € ------ --------- --------- -------g € ------ --------- --------- -------e € ------ --------- --------- -------e € ------ --------- --------- -------e Evaluation of Rating Rating coefficients 𝑓𝑛,𝑚 (𝑐1, 𝑐2) for 𝑛, 𝑚 ∈ gas, diesel, electro and 𝑐1, 𝑐2 ∈ 1,2,3 Evaluate rating for pairs of contracts 𝑓1,2 𝑓1,2 2,3 = 2 1 5
  14. 14. 19 © 2019 FUJITSU Penalty for double choice of same drive type in selection 𝑋 Overall fit for selection 𝑋 Mathematical Model 𝑃 𝑋 = ෍ 𝑑∈ gas,diesel,electro 𝑥 𝑛,𝑐 𝑥 𝑚, ǁ𝑐𝑓𝑛,𝑚 (𝑐1, 𝑐2)𝐹 𝑋 = ෍ 𝑛≠𝑚∈ gas,diesel,electro =1 if choice 𝑐1for drive 𝑛 and choice 𝑐2 for drive 𝑚 Rating contribution of selected pair 𝑥 𝑑,𝑐1 𝑥 𝑑,𝑐2 =1 if two choices 𝑐1and 𝑐2for same drive 𝑑 Penalty for double choice ෍ 𝑐1≠𝑐2∈ 1,2,3 Rating contribution of 2 combined drives 𝛼෍ 𝑐1,𝑐2∈ 1,2,3 𝑐1≠𝑐2 Penalty for drive 𝑐1 𝑐2 𝑛 𝑚 € -------------------------------d € -------------------------------g1 1 𝑐 ǁ𝑐 𝑑 € -------------------------------g € -------------------------------g X1 1
  15. 15. 20 © 2019 FUJITSU 2 31 diesel gas electro choice drive 𝑥 𝑑,𝑐 € ------ --------- --------- -------d € ------ --------- --------- -------d € ------ --------- --------- -------d € ------ --------- --------- -------g € ------ --------- --------- -------g € ------ --------- --------- -------g € ------ --------- --------- -------e € ------ --------- --------- -------e € ------ --------- --------- -------e Digital Annealer Finds Minimum of QUBO Minimize: 𝐸 𝑋 = 𝑃 𝑋 − 𝐹 𝑋 Solution: 𝑋 𝑚𝑖𝑛= 1 0 0 0 1 0 0 1 0 E 𝑋 = σ 𝑑∈ gas,diesel,electro σ 𝑐1,𝑐2∈ 1,2,3 𝑐1≠𝑐2 𝛼 𝑥 𝑑,𝑐1 𝑥 𝑑,𝑐2 − σ 𝑛≠𝑚∈ gas,diesel,electro σ 𝑐1≠𝑐2∈ 1,2,3 𝑓𝑛,𝑚 (𝑐1, 𝑐2) 𝑥 𝑛,𝑐1 𝑥 𝑚,𝑐2 interpretation
  16. 16. 21 © 2019 FUJITSU Why: Disruptive Performance Boost ~N4* calculated ratings Preparation (classical) Polynomial growth in linear time NN combined ratings Search space (DA): Exponential growth in polynomial time N Bits Partial Combined 3 9 27 27 4 16 96 256 5 25 250 3125 … … … … 90 8100 32.440.500 7,6 10175 * Exact figure: N3(N-1)/2 1 1E+18 1E+36 1E+54 1E+72 1E+90 1E+108 1E+126 1E+144 1E+162 1 100 361 784 1369 2116 3025 4096 5329 6724 Scenarios Bits partial scenarios combined scenarios 7,6177 ∗ 10175 Performance boost 32.440.500 76177348045866392339289727720615561750424801402395196724001565744957137343033038019601000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
  17. 17. 22 © 2019 FUJITSU Constraints for Securitization ◼ Absolute limit: e.g. selected volume less than 10.000.000€ 𝑉𝑠𝑒𝑙𝑒𝑐𝑡 < 𝑉𝑚𝑎𝑥 = 10.000.000€ ◼ Property Constraint: e.g. volume of contracts with certain runtime < 10 % of selected volume 𝑉1𝑡𝑜2𝑦𝑒𝑎𝑟𝑠 < 1 10 𝑉𝑠𝑒𝑙𝑒𝑐𝑡 ◼ Constraint relative to selection: e.g. volume of 10 biggest contracts less than 10% of selected volume 𝑉𝑡𝑜𝑝10 < 1 10 𝑉𝑠𝑒𝑙𝑒𝑐𝑡
  18. 18. 23 © 2019 FUJITSU Constraints in Combination ◼ Combined Constraints ◼ Property Constraint: 𝑉1𝑡𝑜2𝑦𝑒𝑎𝑟𝑠 < 1 10 𝑉𝑠𝑒𝑙𝑒𝑐𝑡 (under average representation) ◼ Absolute limit: 𝑉𝑠𝑒𝑙𝑒𝑐𝑡 < 𝑉𝑚𝑎𝑥 = 10%, 20%, … 90% of total assets ➢ Dropped assets have to concentrate more and more in 1-2 years corridor 𝑉𝑚𝑎𝑥 = 10% of assets 𝑉𝑚𝑎𝑥 = 80% of assets 𝑉𝑚𝑎𝑥 = 90% of assets
  19. 19. NOV 2019 Results Method Vselected Vmax / Vselected Volume runtime >1 and <= 2 in % Runtime of Method (calculation) Digital Annealer € 35,487,203 95.68 % 10 % 0.2 sec annealing 50 sec* preparation Digital Annealer with aggregation € 35,483,071 95.67 % 10 % 0.1 sec annealing 3.7 sec* preparation Current System € 35,466,425 95.63 % 10 % 1-2 sec Monte Carlo € 34,856,552 93.98 % 9,87 % 65 min * QUBO preparation was not optimized in PoC
  20. 20. 25 © 2019 FUJITSU Questions / Discussion ◼ Your questions please ◼ Start boosting your business ◼ Visit our exhibition ◼ Meet our experts ◼ See demonstration of solutions T6 Robot movement optimization T7 Production Time Optimization S13 Mobility as a Service with Digital Annealer S14 Optimization for 5G Mobile Network Investment U5 Financial asset management V8 Retail best fit clothing combination W3 Traffic management S14S13 T6 T7 U5 V8 W3 S14 S13 T6 T7 U5 V8 W3
  21. 21. 26 © 2019 FUJITSU phasetimetopic Timeline for Proof of Concept sprint 1 sprint 2 AprilMarch 22nd May 7th Kickoff Model 1 Model 2 Test on Random Data Acceptance Documentation Digital Annealer Service QUBO implementation (Python) Test on Real Data Test Synthetic Data Optimization Dashboard (Python / Jupyter) Test on Random and Synthetic Data Main incubator Fujitsu

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