RETRAiN: A REcommendation Tool for Reconfiguration of RetAil BaNk Branch

  • Rakesh Pimplikar
  • Sameep Mehta
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7636)


Customers in many developing regions (like India) use physical bank branch as primary and preferred banking channel, resulting in high footfall in the branch. This results in high wait time of customers and high pressure on organization’s resources, impacting customer satisfaction (CSAT) as well as employee satisfaction (ESAT) adversely. A naive solution to handle this is to increase the service personnel to cater to the customers. However, this is an unviable alternative because this impacts top and bottom line of the bank. Therefore, organizations are strategically looking for intelligent systems which can help in fine tuning the overall business process to maximize their business objectives while incurring zero or very less investments. Towards this end, we present a system RETRAiN to enable such calibration of various components of bank operations. Based on real time data like waiting customers, service requests, availability of service personnel and business metrics, the system provides recommendations for reconfiguration of the operations. The reconfiguration includes selection of scheduling policy, number of service personnel and configuration of service personnel. We present the overall system along with analysis and optimization algorithms for generating the recommendations. To showcase the efficacy and usefulness of our system, we present results based on data collected over a period of four months from multiple branches of a leading bank in India.


Applications and Experience Retail Banking Services Quality 


  1. 1.
    Mehta, S., Chafle, G., Parija, G.R., Kedia, V.: A system for providing differentiated qos in retail banking. In: IJCAI, pp. 2494–2499 (2011)Google Scholar
  2. 2.
    Lenstra, J.K., Shmoys, D.B., Tardos, É.: Approximation algorithms for scheduling unrelated parallel machines. Math. Program. 46, 259–271 (1990)MathSciNetzbMATHCrossRefGoogle Scholar
  3. 3.
    Martello, S., Soumis, F., Toth, P.: Exact and approximation algorithms for makespan minimization on unrelated parallel machines. Discrete Applied Mathematics 75(2), 169–188 (1997)MathSciNetzbMATHCrossRefGoogle Scholar
  4. 4.
    Jansen, K., Porkolab, L.: Improved approximation schemes for scheduling unrelated parallel machines. In: Proceedings of the Thirty-First Annual ACM Symposium on Theory of Computing, STOC 1999, pp. 408–417. ACM, New York (1999)CrossRefGoogle Scholar
  5. 5.
    Efraimidis, Spirakis: Randomized approximation schemes for scheduling unrelated parallel machines. In: ECCCTR: Electronic Colloquium on Computational Complexity, technical reports (2000)Google Scholar
  6. 6.
    Efraimidis, Spirakis: Approximation schemes for scheduling and covering on unrelated machines. TCS: Theoretical Computer Science 359 (2006)Google Scholar
  7. 7.
    Gairing, M., Monien, B., Woclaw, A.: A faster combinatorial approximation algorithm for scheduling unrelated parallel machines. Theor. Comput. Sci. 380(1-2), 87–99 (2007)MathSciNetzbMATHCrossRefGoogle Scholar
  8. 8.
    Verschae, J., Wiese, A.: On the Configuration-LP for Scheduling on Unrelated Machines. In: Demetrescu, C., Halldórsson, M.M. (eds.) ESA 2011. LNCS, vol. 6942, pp. 530–542. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  9. 9.
    Chudak, F.A.: A min-sum 3/2-approximation algorithm for scheduling unrelated parallel machines. Journal of Scheduling 2, 73–77 (1999)MathSciNetzbMATHCrossRefGoogle Scholar
  10. 10.
    Sgall, J.: On-line Scheduling. In: Fiat, A., Woeginger, G.J. (eds.) Online Algorithms 1996. LNCS, vol. 1442, pp. 196–231. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  11. 11.
    Vazirani, V.: Approximation Algorithms. Springer (2001)Google Scholar
  12. 12.
    Karger, D., Stein, C., Wein, J.: Scheduling algorithms. In: Handbook of Algorithms and Theory of Computation. CRC Press (2010)Google Scholar
  13. 13.
    Deng, Q., Lv, M., Yu, G.: Selecting a Scheduling Policy for Embedded Real-Time Monitor and Control Systems. In: Wu, Z., Chen, C., Guo, M., Bu, J. (eds.) ICESS 2004. LNCS, vol. 3605, pp. 494–501. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  14. 14.
    Adobe, IBM: Solutions for bank branch transformation,
  15. 15.
  16. 16.
  17. 17.
    Galil, Z.: Efficient algorithms for finding maximum matching in graphs. ACM Computing Surveys 18(1), 23 (1986)MathSciNetzbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Rakesh Pimplikar
    • 1
  • Sameep Mehta
    • 1
  1. 1.IBM ResearchNew DelhiIndia

Personalised recommendations