Process mining

1. Process mining At Rabobank Frank van Geffen 19-9-2013

2. Outline • Introduction

3. Outline • Introduction • My experience with process mining at Rabobank

4. Outline • Introduction • My experience with process mining at Rabobank • Paradigm shift of working with process mining

5. Outline • Introduction • My experience with process mining at Rabobank • Paradigm shift of working with process mining • Lessons learned and pittfalls

6. Frank van Geffen

7. Profile of Rabobank Group International financial services provider foundedonco-operativeprinciples • Retail banking, wholesale banking, asset management, leasing, realestate and insurance • 10 millioncustomersworldwide • Active in 47 countries • 761 foreignplaces of business • 59,670 FTE Co-operative core business • 139 independent Local Member Rabobanks • 7.6 million customers • 1.9 million members • 872 branch offices • 27,272 FTE Rabobank has been awarded the highest credit rating for private banksby S&P, Moody’sand DBRS

8. Our values Putting the interests of our customers and members first • Providing the best possible financial services for customers • Offering continuity in our services in the customer’s long-term interest • Ensuring the bank’s involvement with the client and his or her environment • Rabobank Group CoreValues • Respect • Integrity • Professionalism • Sustainability • Rabobank Brand Values • Involved • Nearby • Leading

9. Our mission Responsible banking in the environmental, social and governance fields • To be the largest, best and most innovative financial services provider in the Netherlands • To be the best food & agri bank internationally with a strong presence in the world’s main food & agri countries

10. Organigram 10 millioncustomers 1.8 million members 141 Local Member Rabobanks with 892 branch offices Rabobank Nederland Support Services Rabobank Group Support Local Member Rabobanks Rabobank International Subsidiaries and equity investments Corporate Rembrandt Mergers & Acquisitions Asset Management Robeco Schretlen & Co • Insurance • Achmea (31%) • Interpolis • Real Estate • Rabo Real Estate Group • Bouwfonds Property Development • MAB Development • FGH Bank • Bouwfonds REIM • Public Fund Management Netherlands Partner Banks Banco Terra (31%) BancoRegional (40%) BPR (35%) NMB (35%) Zanaco (46%) URCB (9%) BancoSicredi (25%) • Leasing • De Lage Landen • AthlonCarlease • Freo International retail ACC Bank Bank BGZ (59%) Mortgages Obvion (70%)

11. Group ICT Communication, HR and Control

12. Application Development & Maintenance Opbouw portfolio

13. Outline • Introduction • My experience with process mining at Rabobank

14. My process mining experience at Rabobank Aware Aware/Interested Interested Evaluating Adopting 2010 2009 2013 2011 2012

15. Outline • Introduction • My experience with process mining at Rabobank • Paradigm shift of working with process mining

16. Rabobank’svision op processmining Process mining is a paradigm shift, that changes decision-making and organizational change processes • Facts (objective), decisions are not based on assumptions or subjective analysis

17. Rabobank’svision op processmining Process mining is a paradigm shift, that changes decision-making and organizational change processes • Facts (objective), decisions are not based on assumptions or subjective analysis • Full (complete), decisions are not based on samples or assumptions

18. Rabobank’svision op processmining Process mining is a paradigm shift, that changes decision-making and organizational change processes • Facts (objective), decisions are not based on assumptions or subjective analysis • Full (complete), decisions are not based on samples or assumptions • For real (true comparison), comparisons between departments are not based on debatable industry benchmarks

19. Rabobank’svision op processmining Process mining is a paradigm shift, that changes decision-making and organizational change processes • Facts (objective), decisions are not based on assumptions or subjective analysis • Full (complete), decisions are not based on samples or assumptions • For real (true comparison), comparisons between departments are not based on debatable industry benchmarks • Fast (digital data), decisions are not based on interviews, but digital transaction data

20. Rabobank’s visie op processmining Process mining provides faster and cheaper process intelligence to initiate changes • Traditional (process) analysis has long lead time and is labor intensive Preps Process Analysis • Process mining takes a lot of time initially to get data. Overall faster and cheaper. Preps(Data Mining) ProcessAnalysis

21. Outline • Introduction • My experience with process mining at Rabobank • Paradigm shift of working with process mining • Lessons learned and pittfalls

22. Lessons Learned • Process mining delivers what you expect • Quick insight into current proces • Quick insight into bottlenecks • Quick insight into conformance issues

23. Lessons Learned • Process mining delivers what you expect • Quick insight into current proces • Quick insight into bottlenecks • Quick insight into conformance issues • Further cause analysis, guided by process mining functionality, leads to concrete solutions

24. Lessons Learned • Process mining delivers what you expect • Quick insight into current proces • Quick insight into bottlenecks • Quick insight into conformance issues • Further cause analysis, guided by process mining functionality, leads to concrete solutions • Weakest link is data collection, preparation and interpretation

25. Lessons Learned • Process mining delivers what you expect • Quick insight into current proces • Quick insight into bottlenecks • Quick insight into conformance issues • Further cause analysis, guided by process mining functionality, leads to concrete solutions • Weakest link is data collection, preparation and interpretation • Analysis is performed effective and efficiently through using a professional tool and tool expert

26. Lessons Learned • Process mining delivers what you expect • Quick insight into current proces • Quick insight into bottlenecks • Quick insight into conformance issues • Further cause analysis, guided by process mining functionality, leads to concrete solutions • Weakest link is data collection, preparation and interpretation • Analysis is performed effective and efficiently through using a professional tool and tool expert • Keep on digging and you will eventually reach a “usable” data-source (and sometimes not)

27. Pitfalls • "Garbage in" is "garbage out" (design, registration behavior, and (business) interpretation of data in information systems)

28. Pitfalls • "Garbage in" is "garbage out" (design, registration behavior, and (business) interpretation of data in information systems) • Degree of (process) logging of data in today's information systems (process aware systems / lack of logging due to performance or storage issues)

29. Pitfalls • "Garbage in" is "garbage out" (design, registration behavior, and (business) interpretation of data in information systems) • Degree of (process) logging of data in today's information systems (process aware systems / lack of logging due to performance or storage issues) • (Event) logs are often confidential (e.g. customer / employee data, privacy laws)

30. Pitfalls • "Garbage in" is "garbage out" (design, registration behavior, and (business) interpretation of data in information systems) • Degree of (process) logging of data in today's information systems (process aware systems / lack of logging due to performance or storage issues) • (Event) logs are often confidential (e.g. customer / employee data, privacy laws) • Distribution of data across different systems and the lack of data in data warehouses (difficult / impossible to find a suitable case-id, which links data across multiple systems)

31. Pitfalls • "Garbage in" is "garbage out" (design, registration behavior, and (business) interpretation of data in information systems) • Degree of (process) logging of data in today's information systems (process aware systems / lack of logging due to performance or storage issues) • (Event) logs are often confidential (e.g. customer / employee data, privacy laws) • Distribution of data across different systems and the lack of data in data warehouses (difficult / impossible to find a suitable case-id, which links data across multiple systems) • No objective facts on manual intervention in business processes (e.g. consultation with clients)

32. Pitfalls • "Garbage in" is "garbage out" (design, registration behavior, and (business) interpretation of data in information systems) • Degree of (process) logging of data in today's information systems (process aware systems / lack of logging due to performance or storage issues) • (Event) logs are often confidential (e.g. customer / employee data, privacy laws) • Distribution of data across different systems and the lack of data in data warehouses (difficult / impossible to find a suitable case-id, which links data across multiple systems) • No objective facts on manual intervention in business processes (e.g. consultation with clients) • Large databases like SAP, Oracle Siebel (customized configuration, which tables contain which data?)

33. Pitfalls • "Garbage in" is "garbage out" (design, registration behavior, and (business) interpretation of data in information systems) • Degree of (process) logging of data in today's information systems (process aware systems / lack of logging due to performance or storage issues) • (Event) logs are often confidential (e.g. customer / employee data, privacy laws) • Distribution of data across different systems and the lack of data in data warehouses (difficult / impossible to find a suitable case-id, which links data across multiple systems) • No objective facts on manual intervention in business processes (e.g. consultation with clients) • Large databases like SAP, Oracle Siebel (customized configuration, which tables contain which data?) • (Internal) cost allocation for transport of data (transporting data from A to B costs money)

34. Questions?