practical ai for collection and recovery…2020/02/27 · decision optimization across customer...

© 2020 Fair Isaac Corporation. Confidential. This presentation is provided for the recipient only and cannot be reproduced or shared without Fair Isaac Corporation’s express consent.


FICO Collections Forum27th February 2020 | Stockholm Office

Practical AI for Collection and RecoveryThink Big. Start Small.

Ulrich WiesnerPrincipal Consultant, FICO Analytics


What is Artificial Intelligence? And why would we care in Debt Collections and Recovery

AIMachine Learning

Supervised Learning Lin/Log RegressionSupport Vector MachinesDecision Trees/ForestsNeural Networks

Unsupervised Learning ClusteringAnomaly DetectionDimensionality reductionAssociation rule learning

Automated Planning & Scheduling

Robotics

Machine perception

Computer vision

Speech recognition

Machine touch

Natural language processing

Predictive Models

Segmentation

(Next) Best Action / Best Offer

Chat bots

Voice bots

Capacity Management

Campaign Management


Debt Collection and Recovery Life Cycle

Early Collections Late Collections Recovery

• Maximise recovery• Resolve payment issue• Prevent charge-off / write-

off

• Remind• Self-cure• Prevent deterioration• Avoid voluntary churn

• Low conversion rates• Low recovery rates• High legal costs

• High risk provisions• Write-offs

• High volumes• Operational Expense• Service Level

• Legal action?• Work / Park /

Re-activate?• Sell?

• Whom to restructure?• How to restructure?• Pre-approved offer?

• How to contact? (Call, Text, Letter, CCS)

• When to contact?

FocusChallenge

Decisions

• ECA, NPV• Expected Collection Amount (ECA)

• Re-default after restructure

• Behaviour (PD)• Propensity to roll• Propensity to cure

Models


© 2019 Fair Isaac Corporation. Confidential. This presentation is provided for the recipient only and cannot be reproduced or shared without Fair Isaac Corporation’s express consent. 4

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% R

isk

% Accounts

Why Segment?

LR

MRHR

Low Risk: 50% of accountscarry <15% of thebalances at risk

High Risk: <20% of accountscarry 50% of thebalances at risk



Segmentation Challenges

Criteria? Treatments? Operationalisation?

Analytics Collections System

Medium Risk High Risk

Tona

lity:

Rem

indi

ng

Tona

lity:

Firm

200% 350%

SMS

SMS

SMS

SMS

SMS

Intensity Intensity


Descriptive

Profiling & Segmentation

Predictive Models or “Scores”

Multiple Model Trade-Off

Data Driven Decision Tree Development

Decision Optimisation

Establishes broad segments based on customer profile data

Rank-orders prospects on a single dimension

Creates micro segments by matrixing 2 or 3 predictive models

Creates many micro segments by combining policy, predictive models and segmentation focused on one or more profit driversTypically uses judgmental assignment of actions against micro-segments

Brings all predictive analytics into a single decision frameworkAssigns the optimal action for each prospect/account given specific business constraints

What is the appropriate level of complexity for your decision problem?

Predictive Prescriptive

Evolution of Analytics


Supervised learning

Should I use Scorecards or Machine Learning?

Deep Learning Neural Networks

Gradient Boosted Trees

Random Forests

Support Vector Machines

Segmented Scorecards

Scorecards

• Efficient• Predictive• Transparent• Trustworthy• Engineerable• Palatable• Simple• Fast• Insight sharing

• Highly automated

• Remarkably thorough

• Maximally predictive

• Inescapably complex

• Insight hoarding

EXPLAINABIL ITY à

ACCURACY

à


Do we have the required data?

Core SystemsCustomer Services, Billing

CollectionsSystem

CommunicationsDialler, Text Messages,

Automated Voice

Operational Stack Data Warehouse / Lake

Account Situation

Actions &Results

Actions &Results

Snapshots: Balance, Limits, Arrears amount, Days past due, Payment method, Months on book, Original Balance, Product, Exposure,Score Values, Block codes, Type of available contactsTransactions: Payments, Debits, Reversals, etc.

Channel, Message, time of contact, physical outcome

Segment/Strategy assigned, Contacts,Payment agreements taken/kept/broken,

Examples:Snapshot: Balance, Utilisation, Cash UtilisationCounters: # months in 30+/60+dpd, # broken promisesMin/Max last 6/12/24 mths: Worst dpd, longest lime in collectionsAverage last 6/12/24 mths : Average payments last Trends: Current value / last 6 month average: Utilisation, Cash utilisation

Characteristics at time of event

cycle date or more frequently

Not storing historical snapshots and action/result data is likely going to cost you more money then it saves

Characteristics can be generated when needed


C&R Lifecycle – Use of Predictive Models

Current Bucket 1 Bucket 2 Bucket 3 (Bucket 4) Recoveries

30 dpd 60 dpd 90 dpd 120 dpd0 dpd

Collection Score

Probability to Roll

Collection Score

Probability to self-cure Collection Score

Probability to Roll

Late Collection Score

Expected Collection Amount (ECA)

Recovery Score

Expected Collection Amount (ECA)

Pre-delinquency Score Probability to Roll


Data Driven Strategy Development• Uses historic data and historic outcomes to grow a decision tree

• Combines data driven identification of most relevant decision keys (“next best split”) with expert judgment. Tree development will involve periodic discussions and reviews.

• Initial stages of the strategy focus on excluding certain accounts based on generally accepted policies or operational needs (e.g. no phone, no contact, bankruptcy, fraud, etc)

• At any time during development, splits can be evaluated based on performance data and edited based on data or human expertise

• Output: Segmentation Tree


What is Decision Optimization?

• Example: Set lending amounts & prices to grow portfolio while maintaining profitability and control over losses

• FICO has been a leader in mathematical modelling & optimization for over 30 years - with over 15 years experience in Decision Optimization

Optimizationis the mathematical

process of finding the best decision for a given

business problem

By “best” we usually mean highest profit, or

lowest cost, within a defined set of

constraints (= restrictions on possible values).

Objective Function (Maximize or Minimize)

Decision Variables (Decision Impact Model)

Constraints (On the Decision Variables)

Algorithm (Solvers)

Data


Collection & Recovery

Marketing for Acquisition

Account Origination

Account Management

Customer Retention

• Treatment strategies for early stage• Restructuring and

settlement strategies for late stage• Allocation

strategies (work / place / sell) for recoveries

• Pricing strategies for new customers• Product cross-sell

strategies for existing customers

• Product migration strategies for existing customers

Decision Optimization across customer Lifecycle

Decision• Credit origination

strategies (initial line assignments) for revolving products• Credit origination

strategies (score cut-offs, LTV cut-offs) for installment products

• Credit adjustment strategies (line increase, line decrease) for revolving products

• Attrition risk assessment• Retention

strategies for at risk customers (mid-term, at-term, post-term)


Predictive scores

Account Data

Customer Data

Payment

Voluntary Churn

Balances Cured

Cash Collected

Provision Release

Predictions: Unknown customer

reactions which drive objective

Objectives &Constraints:

Primary & Secondary goals

Decisions:Possible actions

taken

Inputs:

Known information used to make decision

Contact History

Arrears History

Treatment Response

Cure / RollTiming

Balances Cured

Outcomes: Key Metrics

Capacity Requirements

Operational Costs

Charge-Off

Action Effect Models

Channel

Customer Segments

Bureau Data

Tonality

Operational Expense

FTE Requirements

Decision Impact Model: Early Collection Treatments


Now to the demo!!


Collections Treatment Optimization - Testimonials

Jim Bander, Toyota Financial ServicesNational Manager, Decision Sciencehttps://www.youtube.com/watch?v=pvA8AG1v0Mg

Mark Harrison-North, Shop DirectHead of Credit Risk Strategyhttps://www.youtube.com/watch?v=YeuxFszazvc

https://www.youtu.be/watch%3Fv=pvA8AG1v0Mg

https://www.youtube.com/watch%3Fv=YeuxFszazvc


C&RLifecycle – Collection Strategies

Current Bucket 1 Bucket 2 Bucket 3 (Bucket 4) Recoveries

30 dpd 60 dpd 90 dpd 120 dpd0 dpd

Restructure Optimization

Collection Treatment

Optimization

Data Driven Segmentation

Collection Scores

ECA Models ECA Models

Placement Optimization(Work/Place/Sell)

Data Driven Segmentation


Next Steps• Store your data properly – Push to DWH • Cutting corners will likely be expensive in the long run

• Be pragmatic• Understand benefits impact – what if you can improve your

KPI by 5%?• Prioritize – attack your high value problems first• Not every decision problem needs an analytic cannon

• If you are not using Analytics yet• Start simple – data driven segmentation can be a first step• Improve treatments using A/B-Testing

• If you want to improve your Analytic approach• Identify additional decision areas where models can help• Consider Decision Optimisation for your “expensive”

decisions

practical ai for collection and recovery…2020/02/27 · decision optimization across customer...

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