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

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

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

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

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

Man vs. machine

Photo from : http://www.jproc.ca/crypto/bombe_turing.html

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“

CUSTOMER BEHAVIOR

(DEBT COLLECTION AND RECOVERY PROCEDURES)

Case I.

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

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.

Soft recovery Contractual

obligations… Sell products Contract

Execute daily tasks

Hard recovery Stop Rescheduling

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

90days

External law firm

Collection and recovery – typical steps in the process

Internal settlement

Development of predictive model

13

Model

Algorithms Cursors Rules

Historical data Machine learning Result

New data for processing The calculation of probability Result

Model

Dev

elo

pm

ent

Dai

ly u

sage

What is the probability, that this Customer will be late with this

payment?

Probability!

## 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

14

Results – statistics

What we predict? Probability of default Preferred channel Next best „offer“ - step Propensity to buy

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 AUC90 actually means?

• Set the baseline value for AUC KPI

• Focus on Retail segment

15

16

17

ROI=16 days

18

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 19

CUSTOMIZED PRICING (SELL SMART)

Case II.

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

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

8 steps to solution

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

Visualization of 4 customer‘s segments in 2D model

Creating typical Customer Personas (SME segment)

Successful IT company (SW) 20+ employees, high added value per employee, salaries are major expenses, shared ownership

01

Fammily farm 2/3 generations, low added value per family member, machinery and material are main expenses, run by the "head of the family“

02

Transport and logistics company 5 vehicles in average, average added value per employee, Truck fleet is main cost, high dependence from foreign markets and partners

03

Consulting service company 2 employees in average, High added value per employee, High cost of business premises

04

24

Price elasticity of demand

Probability of closing the sales deal

Restriction and limitations

Equal for all customer Market

Generic probability function

Credit risk profile Previous experience and behavior Life time value

Customer personal profile

Customer segmentation

Competitor price Minimal % of margin Legal restrictions

Product affinity Demographic characteristics Preferred channel

𝝆 𝒑, 𝒙 = 𝟏

𝟏 + 𝒆𝒂+𝒃∗𝒓+ 𝒘𝒍∗𝒙𝒍𝑳𝒍=𝟏

CILJ: Maksimiziranje dobička.

What are we optimizing?

Our „profit vs. discount “ curve for selling process P

rofi

t m

argi

n

Discount rate

DISCOUNT RATE with maximum profit margin and at

the same time maximum probability of buying the product

for selected customer

PROFIT MARGIN = PRICE – (Cost price) / (Selling Price)*100

Results

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 1..2..5

• Business Cases (10.7)

Assess constraints, assumptions, and risks

30

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

„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/