Practical AI for Collection and Recovery…2020/02/27 · Decision Optimization across customer...
Transcript of 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.
© 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
© 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.
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
© 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.
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
© 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.
© 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
© 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.
© 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. 5
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
© 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.
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
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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
© 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.
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
© 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.
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
© 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.
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
© 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.
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
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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)
© 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.
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
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Now to the demo!!
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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
© 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.
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
© 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.
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