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Process Mining-Driven Optimization of a Consumer Loan Approvals Process

Process Mining-Driven Optimization of a Consumer Loan Approvals Process. The BPIC 201 2 Challenge. Outline. 1 、 Introduction 2 、 Materials and Methods 3 、 Understanding the Process in Detail 4 、 Assessing Process Performance 5 、 Discussion 6 、 Conclusions 7 、 Homework. Introduction.

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Process Mining-Driven Optimization of a Consumer Loan Approvals Process

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  1. Process Mining-Driven Optimization of a Consumer Loan Approvals Process The BPIC 2012 Challenge

  2. Outline 1、Introduction 2、Materials and Methods 3、Understanding the Process in Detail 4、Assessing Process Performance 5、Discussion 6、Conclusions 7、Homework

  3. Introduction • In BPIC 2012 on the loan and overdraft approvals process of a real-world financial institution in the Netherlands. • Attempted to investigate following areas in detail： • Develop thorough understanding of the data • Develop a detailed understanding of the underlying process • Understand critical activities and decision points • Understand and map life cycle of a loan application from start to eventual disposition as approved, declined or cancelled • Identify any resource level differences in performance one can discern based on available data • Identify opportunities for “process interventions”：places in the process based on likelihood of success

  4. Materials and Methods • The data captures process events for 13,087 loan / overdraft applications over a roughly six month period from October 2011 to March 2012. • The event log is comprised of a total of 262,200 events within these 13,087 cases. • Starting with a customer submitting an application and ending with eventual conclusion of that application into an Approval, Cancellation or Rejection(Delined).

  5. Log Description • An application is submitted through a webpage. Then, some automatic checks are performed, after which the application is complemented with additional information. • This information is obtained trough contacting the customer by phone. If an applicant is eligible, an offer is sent to the client by mail. • After this offer is received back, it is assessed. When it is incomplete, missing information is added by again contacting the customer. • Then a final assessment is done, after which the application is approved and activated.

  6. Developing Thorough Understanding of the data

  7. Tools used for analysis • Disco • Preprocessing and exportation of data into formats suitable for Microsoft Excel analysis. • Microsoft Excel • Used Excel alongside Disco, which helped us visualize, rationalize and refine observations in real time. • CART Implementation from Salford Systems • Conducting preliminary segmentation analysis of the loan applications to assess opportunities for prioritizing work effort.

  8. Discoregistration • 使用下列網址下載Disco： http://fluxicon.com/disco/ • Disco首頁進行Mail註冊： 學校信箱

  9. Setup Disco-1 點Next 點Install

  10. Setup Disco-2 點Iaccept 點Finish

  11. Setup Disco-3 學校信箱 點Register

  12. Setup Disco-4 學校信箱註冊碼 點Complete

  13. Setup Disco-5 點OK

  14. 起始畫面 開啟log檔

  15. 載入Log檔 選擇檔案 點開啟舊檔

  16. 主畫面-1

  17. 主畫面-2 開始檔案 匯出Log檔 過濾器 Case動畫

  18. Simplifying the Event Log

  19. Simplifying the Event Log

  20. Filter 點此按鈕 選Attribute 選剔除掉的屬性 按Apply

  21. 過濾後畫面 匯出CSV

  22. 匯出CSV-1 選Event log 選CSV 選Export

  23. 匯出CSV-2 點存檔

  24. Simplifying the Event Log

  25. Result Activities 拉到0%

  26. Determining Standard Case Flow

  27. Understanding Eventual Outcomes for Each Application

  28. Understanding Eventual Outcomes for Each Application 1/4立即被拒絕 開始到後續大約剩下1/4被拒絕

  29. Case-Level Analysis

  30. Case-Level Analysis 分成965、966個case來看 申請人往往會選擇一個round number。EX：5000、10,000、15,000

  31. Case-Level Analysis 正在處裡 取消 核准

  32. Performance of the Top 5 Resources based on Time Spent 最有經驗的人所花的時間>平均 各領域中花最多時間的五人

  33. Leveraging Behavioral Data for Work Effort Prioritization Salford Systems (http://www.salfordsystems.com) Node 14：818 Case Node 1：200 Case

  34. Discussion • Managing Event Complexity in Data • The event log would also benefit from consolidation of events that happen concurrently, such as those that occur when successful applications are approved (A_APPROVED, A_REGISTERED and A_ACTIVATED).

  35. Conclusions • More extensive work in this area would be greatly aided by the inclusion of additional data points, such as customer information, policies that govern the process, operating costs for the process and eventual customer value. • The bank would find significant additional benefits from exploring such additional areas, for example , social network analysis.

  36. Homework • 使用本篇銀行log檔，依照您的觀點，提出”簡化流程”的方法。 • 將簡化後的流程結果，匯出JPG及轉存成CSV檔。 • 繳交信箱：engineer0710@gmail.com • 繳交日期：2013/11/15(五)

  37. Thanks For Your Listening!!

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