APPLYING AI & MACHINE LEARNING TO TRANSFORM CREDIT MANAGEMENT

Scaling the Modern Finance Organization

April 2019 – NPECA

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Agenda

What’s Driving the Need to Leverage Modern Tools?

Building Your AI Strategy

Building Your Decision-Specific Model

Case Studies

SCALING THE MODERN FINANCE ORGANIZATION

FACTORS DRIVING THE NEED TO LEVERAGE

MODERN TOOLS IN FINANCE

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GROWING GLOBALIZATION:

Information is massively connected, created and consumed everywhere

BIG DATA DEMANDS BIG INSIGHT:Companies must sift through

huge quantities of data to arrive at actionable insight DISRUPTIVE

TECHNOLOGICAL CHANGE:Businesses must embrace new but quickly adopted social, mobile, local

and cloud technologies

ECONOMICDISRUPTION:

Continued uncertain financial outlook is forcing businesses

to do more with less

Navigating growing global complexity now means the difference between business success and failure.

BUILDING YOUR AI STRATEGY

Human Intelligence + Advance Technologies = Value2

RO B OT I C AU TO M AT I ON

M AC H I N E L E A R N I N G (ML )

N AT U R A L L A N G UAG E

G E N E R AT IO N & P RO C E S S IN G

C L U S T E R IN G & C L A S S I F I C AT I O N

A N O M A LY D E T E CT I ON

W E B D I S C OV E RY & V E R I F I C AT I ON

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Areas for the Tools of the Future to Connect Across the Organization

Detect and Prevent: Detect and rank information out of Big Data. Use machine learning to automatically detect fraud in money transfers, employee expenses, and more.

Predict: Derive knowledge from historical information to increase the accuracy of predictive scenarios. Augment traditional financial analytics with more powerful data-matching, pattern recognition, etc., and discover the potential of predictive financial closes.

Proactive context-sensitive support: Digital assistants boost the productivity of financial experts using machine learning to improve context-sensitive, self-service access to financial data.

Automating End-to-End Processes: Increase efficiency and reduce costs. For example, machine learning can automate complex, repetitive decisions such as invoice matching; automatically recognize fields from invoices and expenses; automatically discover potential problems in invoices; and much more.

Source: www.digitalistmag.com/finance/2018/01/22/artificial-intelligence-potential-implications-for-finance-leaders-05775213

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AI and RPA can enhance any decision point in the customer lifecycle. Each decision point is unique and must be analyzed on its own merits.

D E C I S I O N P O I N T S T H R O U G H O U T T H E C U S T O M E R L I F E C Y C L E

P R O S P E C T T A R G E T I N G

C U S T O M E R M A N A G E M E N T

C O L L E C T I O N SR E C O V E R I E S

O R G I N A T I O N

Prospecting

Auto-declinesPre-approvals

Underwriting

Fraud

Pricing and termsUp-sell

Cross-sell

Management Usage

Authorizations

Retention

Win-back

Pre-delinquency

Collections

Charge-off

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Areas we have developed and deployed predictive models to accelerate profitable growth.

RECOVERY MODELS are applied to customers you placed in collection. They predict the likelihood of recovery and provide an estimated collection amount

DELINQUENCY MODELS predict the future payment performance of your business customers

BANKRUPTCY MODELS predict the likelihood of a business failing

TRANSFORMATION MODELS provides insight into how an organization will likely change over time, increase demand, decrease demand, expand, contract, or stagnate

FRAUD MODELS identify businesses that have a higher likelihood of being fraudulent

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Evolving the Finance Organization with Modern Tools

JudgmentalModel

Commercial Available Score

Linear LogisticRegression Model

MachineLearning

TRANSACTIONAL PROCESSES

UNIVERSAL SCORINGDEFINED PORTFOLIO

1ST GENERATIONCUSTOM SCORE

INTELLIGENTDESIGN

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When developing a decision strategy for your organization, it is crucial to choose a predictive model aligned with your business’ goals.

LINEAR LOGISTIC REGRESSION MODEL application of statistical modeling based on targeted element testing and experiential inputs

JUDGMENTAL MODEL built by a team of experts based on their combined experience and observations

COMMERCIALLY AVAILABLE MODELS predictions built on larger portfolios leverage statistical modeling

MACHINE LEARNING MODELS allows for complex processing and segmentation based on potentially imperceptible patterns

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53%Lift in

Custom Scorecard

23%Lift in Delinquency Model for Alternative Lenders

35% Lift in

Fraud Score

Applying Machine Learning to Scorecards

We saw firsthand a significant lift with a machine learning approach vs. Traditional Scorecard in many instances when we compared both methods.

Single LearnerTraditional Scorecards and Decision Trees

Ensemble ModelsNew ML Algorithms

Focus ML

innovation

Machine learning is an EQUATION to solve a specific problem based on some example data.– Instead of creating logic to solve the problem (judgmental approach), data is

fed to the generic algorithm and it builds its own logic based on the data.

– While ALL statistical models could be defined as machine learning models, today’s ML models allow the machines much greater autonomy.

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THE BEST USE CASE

Very subtle relationship between predictors and target. Adaptive learning is necessary.

GOOD USE CASE

Well-defined target variable that is the same or very similar across all customers’ applications.

GOOD USE CASE

Numerous segments within the universe with a very different data coverage and complex relationships between predictors and target.

NOT CLEAR CUT

Every company has its own definition of target variable.Traditional scorecard methods work well.

NOT A GOOD CASEDrivers behind score can vary and require nuanced methods to execute.

Some Standard Scores Suit Better for Machine Learning Models than Others

FRAUDSCORE

FAILURESCORE

GLOBAL BUSINESS RANKING

DELINQUENCYSCORE

SUPPLIERS SCORE

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Many parts of custom model development can be automated,reducing time from weeks to days.

C O M P O N E N T S O F A M L E N A B L E D C U S TO M M O D E L B U I L D

PROBLEMDEFINITION

RECEIVECUSTOMER

DATA

APPENDD&B DATA

BUILDMODEL

OUTPUTSCORES +REPORTS

DELIVERRESULTS

These steps are automated in Analytics Model Build PlatformAutomated for D&B Credit users

BUILDING YOURDECISION-SPECIFIC MODEL

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Predictive model scorecards have proven value—using AI techniques to drive continuous valuation of risk exposure.

Statistically-driven scorecards drive automation and help you keep a pulse on your risk exposure at all times:

• Leverage a broader range of data attributes based on empirical evidence.

• Utilize advanced Machine Learning algorithms and capabilities to dynamically tune to changing market conditions.

• Allow for multiple segmented scorecards as needed with ease and efficiency

• Drive higher risk discrimination and performance-based risk mitigation strategy

Identify Risk

Evaluate Risks

Assess Impact

Adjust Scorecard

Monitor

I T E R A T I V E R I S K A S S E S S M E N T F R A M E W O R K

Continuous learning loop

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Machine Learning is very beneficial for custom models.

IMPROVED MODEL PERFORMANCE

IDENTIF IES ALL NATURAL SEGMENTS IN PORTFOL IO

SPEED AND AUTOMATION

In custom modeling, development sample closely represents

sample that will be used for practical application

Does not require judgmental decision on model segmentation, so identifies

all segments – primary and sub-segments in portfolio

Much better suited for automation – to all parts of modeling process including

adaptive learning

1 2 3

B E N E F I T S O F M L F O R C U S T O M M O D E L S

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Engagement Steps - Scorecard Validation Services

Modeler validates current scorecard and creates a POC

optimized scorecard

S T E P 2Turnaround is

3-5 days.The deliverable

is a report.

S T E P 3ROI scenarios are created for

optimized scorecard

S T E P 4Customer sends list of DUNS and

good/bad indicator and copy of current

scorecard

S T E P 1

CRF EXPERIMENT RECAP

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Summary of Activities

A P P R O A C H :Judgmental

T I M E I N V E S T E D :Internal Investment: 1 0 W E E K S

A P P R O A C H :Commercial Model

T I M E I N V E S T E D :M I N I M A L

A P P R O A C H :Linear Regression Model

T I M E I N V E S T E D :Internal Investment:

8 H O U R S

External Investment: 6 W E E K S

A P P R O A C H :Machine Learning Model

T I M E I N V E S T E D :Internal Investment:

2 H O U R S

S U M M A R Y O F E V E N T S

Six team members, manual review and spreadsheets, active discussion, revision

and finalization

S U M M A R Y O F E V E N T S

Off the shelf from Dun & Bradstreet –

using national risk definitions

S U M M A R Y O F E V E N T S

Passed data to statistician, who was

allowed to define strategy

S U M M A R Y O F E V E N T S

Data transferred to Dun & Bradstreet

with short discussion of requirements

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Effectiveness of Model Approaches for Delinquency Prediction

APPLYING AI & MACHINE LEARNING TO TRANSFORM CREDIT MANAGEMENT

Mordellid KS Gini AURTotal Slow Payers in

Bottom 5%Total Slow Payers in

Bottom 10%Total Slow Payers in

Bottom 20%

Machine Learning Model 56.68 0.699 0.851 21.8% 43.1% 70.6%

Judgmental Scorecard Model 23.39 0.290 0.645 10.3% 18.8% 32.4%

Standard Score 14.32 0.158 0.579 6.7% 10.3% 25.5%

SampleMachine Learning Model

D&B Standard Score

Scorecard Model

% Population Captured%

Res

pons

e C

aptu

red

% Population0.1 0.2. 0.3. 0.4. 0.5. 0.6. 0.7 0.8. 0.9. 1.0

1.00 -0.95 -0.90 -0.85 -0.80 -0.75 -0.70 -0.65 -0.60 -0.55 -0.50 -0.45 -0.40 -0.35 -0.30 -0.25 -0.20 -0.15 -0.10 -0.05 -

SampleMachine Learning Model

D&B Standard Score

Scorecard Model

Lift

Lift

Cha

rt

Score Band

4.5 -

4.0 -

3.5 -

3.0 -

2.5 -

2.0 -

1.5 -

1.0 -

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Q A

Thank You