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Planning Workforce Management for Bank Operation Centers with Neural Networks

Sefik Ilkin Serengil
Sefik Ilkin Serengil

15th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Databases (AIKED '16), Venice, Italy, January 29, 2016.

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Planning Workforce Managament for Bank Operation Centers with Neural Networks Sefik Ilkin Serengil joint work with Alper Ozpinar AIKED Conference Venice, Italy January 29, 2016
p.2 / 18Sefik Ilkin Serengil AIKED Venice, January 2016
p.3 / 18Sefik Ilkin Serengil AIKED Venice, January 2016 Talk Outline 1. Operation Centers 2. Problems 3. Optimization Objective 4. Motivation 5. Results 6. Proposed Method 7. Conclusion
p.4 / 18Sefik Ilkin Serengil AIKED Venice, January 2016 Money Transfer Orders Customers still tend to use bank branches 35% of bulk transactions tranmitted on branches Mostly commercial customers Faxing instruction, no need to be situated at branch Branch employees validate the signature Scan and deliver instruction to OC
p.5 / 18Sefik Ilkin Serengil AIKED Venice, January 2016 Money Transfer Orders #2 Could include multiple transactions (15% bulk rate) Large amount (Avg 27K USD per transaction) 10M count money transfer order (50% of all) 16M count money transfer transactions Branch operations distribution for last 16 months
p.6 / 18Sefik Ilkin Serengil AIKED Venice, January 2016 Operation Centers Serve to reduce operational workload of branches Centralized management, expert employees Offering faster, high quality service High turnover rate (e.g. 50-300 employees) Digitalizing the hard copy instruction Commit the transaction
p.7 / 18Sefik Ilkin Serengil AIKED Venice, January 2016 Problems OC Managers predict workload by experience Planning the workforce manually Rescheduling when density is observed Deadline is strictly defined by Government (5.00 pm) Service Level Aggrement (90 minutes) Delays cause to suffer customers
p.8 / 18Sefik Ilkin Serengil AIKED Venice, January 2016 Problems #2 Insufficient employee reservation is clearly seen Y-axis: Total work and reserved employee ratio X-axis: Work hours
p.9 / 18Sefik Ilkin Serengil AIKED Venice, January 2016 Optimization Objective Proper and efficient employee planning Preventing excess employee reservation for low transaction volume Avoiding insufficient employee reservation for high transaction volume Machine learning based workload prediction Workforce planning by considering employee skills
p.10 / 18Sefik Ilkin Serengil AIKED Venice, January 2016 Motivation Thought as machine learning problem A function is modeled by historical examples Function forecasts for un-known examples (y) Underfitting for simple complexity function Overfitting for too complex function Function should be derived from affecting factors (x) Historical Data ML Algorithm Mathematical Functionx[] y forecasting
p.11 / 18Sefik Ilkin Serengil AIKED Venice, January 2016 Affecting Factors vs Correlation Factor Scale Correlation Co. Hour [9, 17] 0.0500 Day [1, 31] -0.0557 Month [1, 12] 0.0048 Year [2012, 2016] -0.0767 Weekday [2: Monday, 6: Friday] 0.0728 Is first or last work day [0, 1] 0.1790 Is half day [0, 1] -0.0048 Transaction count (h-1) [-, +] 0.2114 Transaction count (h-2) [-, +] -0.0415 Transaction count (h-3) [-, +] 0.2666 Yearly deviation [-, +] 0.0388 Potential Function Parameters
p.12 / 18Sefik Ilkin Serengil AIKED Venice, January 2016 Neural Networks Ability to learn, remember and predict Multiple inputs and an output Inputs (x) are involved in network through own weight Weight (w) specifies the strength of input on output Adjusting weight values implement learning Assembly function () calculates net input (o) Activation function (f) computes the net output (y)
p.13 / 18Sefik Ilkin Serengil AIKED Venice, January 2016 Neural Network Model 3 layered network with node numbers 11, 8, 1 8 nodes in hidden layer acc. 2/3 rule (Heaton, 2000) Sigmoid for activation, Back-propagation for learning
p.14 / 18Sefik Ilkin Serengil AIKED Venice, January 2016 Workload Forecast Results Suppose x is prediction set, y is actual set Evaluation metric One days result for Dec 04, 2015
p.15 / 18Sefik Ilkin Serengil AIKED Venice, January 2016 Results #2 A sample from long term results for 100 days Historical data obtained for last 4 years. EFT MO MAE 60.95 60.99 MAE / Mean 10.29% 15.19% Correlation Co. 96.47% 93.04% Mean 592.40 401.42 Instances (hour) 548 548
p.16 / 18Sefik Ilkin Serengil AIKED Venice, January 2016 Workforce Planning Employee skill map for 2 months period X-axis: unit perform time in seconds Y-axis: Average completed work count on a hour PN: Expected transaction count (NN result) PQ: Transactions waiting on queue
p.17 / 18Sefik Ilkin Serengil AIKED Venice, January 2016 Conclusion An approach introduced to plan workforce Based on a machine learning discipline Simulated for EFT and Money Order Satisfactory results for workload forecasting Workforce planning by considering skills Future work; workforce optimization on production Thought to be applied in turnover requiring areas
p.18 / 18Sefik Ilkin Serengil AIKED Venice, January 2016 Acknowledgements Conducted by SoftTech under project number 5059. Supported by TEYDEB (Technology and Innovation Funding Programs Directorate ) of TUBITAK (The Scientific and Technological Research Council of Turkey) In scope of Industrial Research and Development Projects Grant Program (1501) Under the project number 3150070.
Thank you for your attention! Grazie per l'attenzione!

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Planning Workforce Management for Bank Operation Centers with Neural Networks

  • 1. Planning Workforce Managament for Bank Operation Centers with Neural Networks Sefik Ilkin Serengil joint work with Alper Ozpinar AIKED Conference Venice, Italy January 29, 2016
  • 2. p.2 / 18Sefik Ilkin Serengil AIKED Venice, January 2016
  • 3. p.3 / 18Sefik Ilkin Serengil AIKED Venice, January 2016 Talk Outline 1. Operation Centers 2. Problems 3. Optimization Objective 4. Motivation 5. Results 6. Proposed Method 7. Conclusion
  • 4. p.4 / 18Sefik Ilkin Serengil AIKED Venice, January 2016 Money Transfer Orders Customers still tend to use bank branches 35% of bulk transactions tranmitted on branches Mostly commercial customers Faxing instruction, no need to be situated at branch Branch employees validate the signature Scan and deliver instruction to OC
  • 5. p.5 / 18Sefik Ilkin Serengil AIKED Venice, January 2016 Money Transfer Orders #2 Could include multiple transactions (15% bulk rate) Large amount (Avg 27K USD per transaction) 10M count money transfer order (50% of all) 16M count money transfer transactions Branch operations distribution for last 16 months
  • 6. p.6 / 18Sefik Ilkin Serengil AIKED Venice, January 2016 Operation Centers Serve to reduce operational workload of branches Centralized management, expert employees Offering faster, high quality service High turnover rate (e.g. 50-300 employees) Digitalizing the hard copy instruction Commit the transaction
  • 7. p.7 / 18Sefik Ilkin Serengil AIKED Venice, January 2016 Problems OC Managers predict workload by experience Planning the workforce manually Rescheduling when density is observed Deadline is strictly defined by Government (5.00 pm) Service Level Aggrement (90 minutes) Delays cause to suffer customers
  • 8. p.8 / 18Sefik Ilkin Serengil AIKED Venice, January 2016 Problems #2 Insufficient employee reservation is clearly seen Y-axis: Total work and reserved employee ratio X-axis: Work hours
  • 9. p.9 / 18Sefik Ilkin Serengil AIKED Venice, January 2016 Optimization Objective Proper and efficient employee planning Preventing excess employee reservation for low transaction volume Avoiding insufficient employee reservation for high transaction volume Machine learning based workload prediction Workforce planning by considering employee skills
  • 10. p.10 / 18Sefik Ilkin Serengil AIKED Venice, January 2016 Motivation Thought as machine learning problem A function is modeled by historical examples Function forecasts for un-known examples (y) Underfitting for simple complexity function Overfitting for too complex function Function should be derived from affecting factors (x) Historical Data ML Algorithm Mathematical Functionx[] y forecasting
  • 11. p.11 / 18Sefik Ilkin Serengil AIKED Venice, January 2016 Affecting Factors vs Correlation Factor Scale Correlation Co. Hour [9, 17] 0.0500 Day [1, 31] -0.0557 Month [1, 12] 0.0048 Year [2012, 2016] -0.0767 Weekday [2: Monday, 6: Friday] 0.0728 Is first or last work day [0, 1] 0.1790 Is half day [0, 1] -0.0048 Transaction count (h-1) [-, +] 0.2114 Transaction count (h-2) [-, +] -0.0415 Transaction count (h-3) [-, +] 0.2666 Yearly deviation [-, +] 0.0388 Potential Function Parameters
  • 12. p.12 / 18Sefik Ilkin Serengil AIKED Venice, January 2016 Neural Networks Ability to learn, remember and predict Multiple inputs and an output Inputs (x) are involved in network through own weight Weight (w) specifies the strength of input on output Adjusting weight values implement learning Assembly function () calculates net input (o) Activation function (f) computes the net output (y)
  • 13. p.13 / 18Sefik Ilkin Serengil AIKED Venice, January 2016 Neural Network Model 3 layered network with node numbers 11, 8, 1 8 nodes in hidden layer acc. 2/3 rule (Heaton, 2000) Sigmoid for activation, Back-propagation for learning
  • 14. p.14 / 18Sefik Ilkin Serengil AIKED Venice, January 2016 Workload Forecast Results Suppose x is prediction set, y is actual set Evaluation metric One days result for Dec 04, 2015
  • 15. p.15 / 18Sefik Ilkin Serengil AIKED Venice, January 2016 Results #2 A sample from long term results for 100 days Historical data obtained for last 4 years. EFT MO MAE 60.95 60.99 MAE / Mean 10.29% 15.19% Correlation Co. 96.47% 93.04% Mean 592.40 401.42 Instances (hour) 548 548
  • 16. p.16 / 18Sefik Ilkin Serengil AIKED Venice, January 2016 Workforce Planning Employee skill map for 2 months period X-axis: unit perform time in seconds Y-axis: Average completed work count on a hour PN: Expected transaction count (NN result) PQ: Transactions waiting on queue
  • 17. p.17 / 18Sefik Ilkin Serengil AIKED Venice, January 2016 Conclusion An approach introduced to plan workforce Based on a machine learning discipline Simulated for EFT and Money Order Satisfactory results for workload forecasting Workforce planning by considering skills Future work; workforce optimization on production Thought to be applied in turnover requiring areas
  • 18. p.18 / 18Sefik Ilkin Serengil AIKED Venice, January 2016 Acknowledgements Conducted by SoftTech under project number 5059. Supported by TEYDEB (Technology and Innovation Funding Programs Directorate ) of TUBITAK (The Scientific and Technological Research Council of Turkey) In scope of Industrial Research and Development Projects Grant Program (1501) Under the project number 3150070.
  • 19. Thank you for your attention! Grazie per l'attenzione!