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

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Talk Outline

1. Operation Centers

2. Problems

3. Optimization Objective

4. Motivation

5. Results

6. Proposed Method

7. Conclusion

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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

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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

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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

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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

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Problems #2

• Insufficient employee reservation is clearly seen

• Y-axis: Total work and reserved employee ratio

• X-axis: Work hours

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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

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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

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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

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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)

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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

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Workload Forecast Results

• Suppose x is prediction set, y is actual set

• Evaluation metric

• One day’s result for Dec 04, 2015

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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

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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

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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

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Acknowledgements

• Conducted by SoftTech under project number 5059.

• Supported by TEYDEB (Technology and InnovationFunding Programs Directorate ) of

• TUBITAK (The Scientific and Technological ResearchCouncil of Turkey)

• In scope of Industrial Research and DevelopmentProjects Grant Program (1501)

• Under the project number 3150070.

Thank you for your attention!

Grazie per l'attenzione!