Mirror, Mirror on the Wall, Who's the Wisest of them All?

Transcription

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

2 Mirror, mirror on the wall, who's the wisest of them all? BA perspective on predictive analytics and artificial intelligence Agenda: I. Short introduction II. Case I. Customer behavior III. Case II. Sell smart IV. Key takeaways

3 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

4 Mirror, Mirror on the Wall, Who's the Wisest of them All?

5

6 Mirror s predictive analytics algorithm 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:

7 Man vs. machine Photo from :

8 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

9 CUSTOMER BEHAVIOR (DEBT COLLECTION AND RECOVERY PROCEDURES) Case I.

10 Case background the story Since economic crises in 2008, Slovenian banks have been deeply involved in the collection process due to the increased quantity and volume of overdue outstanding receivables. Operational efficiency optimization led them to decrease the number of employees, so collectors were overloaded with tasks and documents. Growth of non-performing loans Decline in the number of employees

11 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.

12 Collection and recovery typical steps in the process 1. DEFINE OPTIMAL STEPS Call Internal compensation Write offs Letter (Reminder) Customer status Execute daily tasks 3. DASHBOARD Sell products Contract Contractual obligations Overdue receivables Soft recovery Rescheduling Hard recovery Stop 2. EXECUTE OPTIMAL STEPS DW Call 90days Internal settlement Bill of exchange External law firm Write-off Letter Daily transaction

13 Daily usage Development Development of predictive model Historical data Machine learning Result Algorithms Cursors Rules Model New data for processing The calculation of probability Result What is the probability, that this Customer will be late with this payment? Model Probability! 13

14 Results statistics ## Confusion Matrix and Statistics ## ## Reference ## Prediction default no-default ## default 9 1 ## no-default ## ## Accuracy : ## 95% CI : (0.955, 0.997) ## No Information Rate : ## P-Value [Acc > NIR] : ## 98,4% Behavior prediction index What we predict? Probability of default Preferred channel Next best offer - step Propensity to buy ## ## 'Positive' Class : default ## 14

15 How we see the results? We used survival curve to present the results. We choose only one KPI to measure Solution performance (AUC) Observation time interval from 0 to 90 days of overdue Understand what AUC 90 actually means? Set the baseline value for AUC KPI Focus on Retail segment 15

16 16

17 17

18 ROI=16 days 18

19 What works? BA approaches to implement valuable predictive analytics solution Prototyping 2-4 months for experimenting - poor results Solution performance goal Clear KPI AUC 90 [Retail] Data mining Useful patterns in data exists ( Pay day ; Strong Days ; Friends ) Risk analysis and management CX: be professional, be honest, be compassionate 19

20 Case II. CUSTOMIZED PRICING (SELL SMART)

21 Case background - story Back in 2015, a bank s branch management started a process-reengineering project with the main goal to increase efficiency and effectiveness of sales process. Sell to fast and to easy Not charging all eligible costs that they actually can Significant differences between sales agents and units

22 Sell SMART From need to value Need: to maximize revenue from sales process and not loosing clients (SME segment). Stakeholder: sales, back-office, customer service, middle management Context: high competition on the market, high loyalty of clients Change: from human to machine decision making Solution: predictive models (R) for probability, demand and discount calculations. Selling to the right customer, with right discount strategy, at the right time. Value: maximized revenue with optimal given discounts and preserved good CX. 22

23 8 steps to solution Visualization of 4 customer s segments in 2D model I. Segmentation (SME) II. Definition of Personas III. Demand curve IV. Probability to sell V. Definition of KPI VI. Development and deployment VII. Model testing VIII. Analyzing and improving

24 Creating typical Customer Personas (SME segment) 01 Successful IT company (SW) 20+ employees, high added value per employee, salaries are major expenses, shared ownership Consulting service company 2 employees in average, High added value per employee, 04 High cost of business premises 02 Fammily farm 2/3 generations, low added value per family member, machinery and material are main expenses, run by the "head of the family Transport and logistics company 5 vehicles in average, average added value per employee, 03 Truck fleet is main cost, high dependence from foreign markets and partners 24

25 Price elasticity of demand

26 Probability of closing the sales deal Customer segmentation Product affinity Demographic characteristics Preferred channel Restriction and limitations Competitor price Minimal % of margin Legal restrictions Generic probability function Equal for all customer Market Customer personal profile Credit risk profile Previous experience and behavior Life time value ρ p, x = e a+b r+ L l=1 w l x l

27 What are we optimizing? CILJ: Maksimiziranje dobička.

28 Profit margin Our profit vs. discount curve for selling process PROFIT MARGIN = PRICE (Cost price) / (Selling Price)*100 DISCOUNT RATE with maximum profit margin and at the same time maximum probability of buying the product for selected customer Discount rate

29 Results

30 What works? BA approaches to implement valuable predictive analytics solution Decision Modeling (10.15) Combine data and knowledge to design decisions Financial Analysis (10.20) Understand value realization Metrics and Key Performance Indicators (10.28) Define, prioritize...principle of Business Cases (10.7) Assess constraints, assumptions, and risks 30

31 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? STEP BY STEP, EVOLUTIONARY AGREE ON SINGLE KPI KNOW YOUR CUSTOMER How to understand data? FIND USEFUL PATTERNS AND INSIGHTS How to ensure "good enough" algorithms and procedures used? FEEDBACK LOOP

32 Computers are our mirrors: whether we marvel or shudder at the latest AI, we re merely looking at ourselves. Source:

33