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BA and Beyond 19 Andrej Guštin - Mirror mirror on the wall Who's the wisest of them all

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Recently, machine learning algorithms surpass humans intelligence in many areas (go, chess, poker). Operational optimization (logistic, back-office) and customer behaviour predictions (marketing, sales) are some of the top priorities in companies to digitize their business.

But only a few can remember that it all started in Bletchley Park with the need to break the Enigma Code. Without business analysis techniques they probably wouldn’t have succeeded. BA approaches that were used back in the day are still valuable today.

We will present two real (banking sector) cases and their results to demonstrate analytical phases of designing, developing and using predictive analytics models that process customer data daily and recommend actions, based on predefined business rules and decision points in workflows. From Stakeholder Needs aligned with their Value we will show how to build smart predictive algorithms to determine the “next best action” and “preferred channel” in the Context of better CX.

Defined KPI’s measure VALUE daily and enable BA to monitor effectiveness and efficiency continuously, detect potential issues and take necessary corrective actions. In 5 months we increased the VALUE to 450% and needed 16 days to achieve ROI.

Key takeaways:
- Examples of different approaches we have taken to implement valuable predictive analytics solution, including what works and what doesn't
- How BAs can balance between external Customer eXperience view and internal stakeholders need to maximize the value of the project
- Large quantities of data exist, but the value is in analytics, not only the right algorithms that will work, but that they improve CX and add value.
- Approaches to ensure that algorithms and procedures used within project are "good enough"

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BA and Beyond 19 Andrej Guštin - Mirror mirror on the wall Who's the wisest of them all

  1. 1. Mirror, Mirror on the wall, who's the wisest of them all? BA Perspective on Predictive Analytics and Artificial Intelligence Andrej Guštin, IIBA Chapter Slovenia, Vice President; CREA pro, CEO Agenda: I. Short introduction II. Case – Customer behavior III. Key takeaways
  2. 2. Mirror, Mirror on the Wall, Who's the Wisest of them All?
  3. 3. Queen Magic mirror on the wall, who is the fairest one of all? Magic Mirror Famed is thy beauty, Majesty. But hold, a lovely maid I see. Rags cannot hide her gentle grace. Alas, she is more fair than thee. Queen Alas for her! Reveal her name. Magic Mirror Lips red as the rose. Hair black as ebony. Skin white as snow. Queen Snow White! Photo from: http://disney.wikia.com/wiki/Snow_White Mirror‘s predictive analytics algorithm ?
  4. 4. Man vs. machine Photo from : http://www.jproc.ca/crypto/bombe_turing.html
  5. 5. What „really“ helped - behind breaking the Enigma code BA perspective • Prototyping (10.36) – The first prototype was too slow • Solution performance goals (10.28) – Clear KPI – 24 hours change of Enigma settings • Data mining (10.14) – Finding useful patterns and insights from data („weather“ „nothing to report “) • Estimations (10.19) – Gardening - to encourage a target to use known „plaintext in an encrypted message“ • Risk analysis and management (10.38) – Confidentiality and non-contamination of the „sample“ 7/22
  6. 6. CUSTOMER BEHAVIOR (DEBT COLLECTION AND RECOVERY PROCEDURES) Case Study
  7. 7. Case background – the story • Since economic crisis in 2008, Slovenian banks have been deeply involved in the collection process due to the increased quantity and volume of overdue outstanding receivables. Growth of non-performing loans Decline in the number of employees • Operational efficiency optimization led them to decrease the number of employees, so collectors were overloaded with tasks and documents.
  8. 8. Recovery process – From need to value • Need: how to optimize collection process and increase the volume and amount of collected payments. • Stakeholder: back-office, customer service, call center, clerk, middle management • Context: economic situation, as described • Change: from human to machine decision making. • Solution: predictive model (R) for probability calculations. Selectively targeting the right debtors with the right collection strategies at the right time was proposed by the Solution and integrated processes. • Value: optimal allocation of resources to maximize the amount collected while minimizing collection costs.
  9. 9. Soft recovery Contractual obligations…Sell products Contract Execute daily tasks Hard recovery StopRescheduling Customer status Overdue receivables DW Bill of exchange Letter Write-off Call 1. DEFINE OPTIMAL STEPS 2. EXECUTE OPTIMAL STEPS Call Internal compensation Letter (Reminder) Write offs 3. DASHBOARD Daily transaction 90 days External law firm Collection and recovery – typical steps in the process Internal settlement 1 day
  10. 10. Development of predictive model Model Algorithms Cursors Rules Historical data Machine learning Result New data for processing The calculation of probability Result Model DevelopmentDailyusage What is the probability, that this Customer will be late with this payment? Probability!
  11. 11. ## Confusion Matrix and Statistics ## ## Reference ## Prediction default no-default ## default 9 1 ## no-default 2 180 ## ## Accuracy : 0.984 ## 95% CI : (0.955, 0.997) ## No Information Rate : 0.943 ## P-Value [Acc > NIR] : 0.0041 ## ## ## 'Positive' Class : default ## 98,4% Behavior prediction index 13/22 Results – statistics What we predict? ➢Probability of default ➢Preferred channel ➢Next best „offer“ - step ➢Propensity to buy
  12. 12. How do we measure the results? • We used survival curve to present the results. • We chose only one (1) KPI to measure Solution performance (AUC) • Observation time interval from 0 to 90 days of overdue • Understand what AUC90 actually means? • Set the baseline value for AUC KPI90 • Focus on Retail segment 14/22
  13. 13. ROI=16 days
  14. 14. What works? BA approaches to implement valuable predictive analytics solution • Prototyping  2-4 months for experimenting - poor results • Solution performance goal  Clear KPI – AUC90[Retail] • Data mining  Useful patterns in data exists („Pay day“; „Strong Days“; „ Friends“) • Risk analysis and management  CX: be professional, be honest, be compassionate
  15. 15. Key takeaways: How to implement valuable predictive analytics solution? How to evaluate what works and what doesn't? How to balance between CX and internal project goals? How to understand data? How to ensure "good enough" algorithms and procedures used? STEP BY STEP, EVOLUTIONARY AGREE ON SINGLE KPI KNOW YOUR CUSTOMER FIND USEFUL PATTERNS AND INSIGHTS FEEDBACK LOOP
  16. 16. „Computers are our mirrors: whether we marvel or shudder at the latest AI, we’re merely looking at ourselves.“ Source: https://www.newscientist.com/article/mg23130803-200-how-alan-turing-found-machine-thinking-in-the-human-mind/
  17. 17. Andrej Guštin is a cofounder and CEO at CREA pro, a leading Slovenian consulting company focused comprehensively on business process management and innovation. Vice president of IIBA CHAPTER SLOVENIA since 2009

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