How do I reward my members while remaining profitable?
Mortgage Price Optimization for Building Societies
Neil Jennings
Senior Consultant, Applied Optimization
FICO
© 2017 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.
Executive Summary
•
A structured analytic approach to retaining
customers is critical in such a transparent and
competitive market
•
Strategic mortgage offers that target customer
retention, rather than profit, are consistent with
member-centric goals
•
Relationship-based pricing can be built into
optimization frameworks, further underpinning a
positive member experience
© 2017 Fair Isaac Corporation. Confidential.
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Challenging pre-conceptions around price optimization
© 2017 Fair Isaac Corporation. Confidential.
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Price optimization
means Profit
Maximization
Price Optimization can
target non-profit goals
Price optimization will
be bad for our members
Retention-oriented
optimization should
result in good outcomes
for members
Optimization cannot
take account of nonprice factors
Anything that can be
measured can be
optimized
Price optimization
punishes member
loyalty & tenure
Measuring true lifetime
value will ensure loyal
members get the best
deals
Mortgage Price Optimization: Traditional Approach
© 2017 Fair Isaac Corporation. Confidential.
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Traditional Principals for Mortgage Price Optimization
Leverage Elasticity: Achieve pricing efficiency by aligning
rates with customer rate response to simultaneously drive
improvements in volume and margin by aligning rates with
customer rate response
Active Pricing: Actively track & evaluate client rate response,
competitive changes, & rate strategy adherence. Regularly
reconcile expected vs. actual volume & margin, and review
pricing often
Compliance-Aware optimization: Ensure rate & offer
decisions explicitly take regulatory constraints into account
during the strategy optimization process
© 2017 Fair Isaac Corporation. Confidential.
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Typical Decision Model Framework for Loans & Mortgages
3. Adverse
Selection
1. Existing
Customer
Applications
Accepts
1. New-toBank
Applications
© 2017 Fair Isaac Corporation. Confidential.
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x
Average
Amount
x
Profit per
Customer
2. Takeups
Volume-Margin Measurement is Core to Pricing Loans & Mortgages
Margin vs. Rate Offered
3.5%
£35m
3.0%
£30m
2.5%
£25m
2.0%
£20m
1.5%
£15m
1.0%
£10m
0.5%
£5m
0.0%
£0m
3.00%
3.25%
3.50%
3.75%
4.00%
4.25%
4.50%
4.75%
5.00%
5.25%
5.50%
5.75%
3.00%
3.25%
3.50%
3.75%
4.00%
4.25%
4.50%
4.75%
5.00%
5.25%
5.50%
5.75%
£4.0bn
£3.5bn
£3.0bn
£2.5bn
£2.0bn
£1.5bn
£1.0bn
£0.5bn
£0.0bn
Key Capabilities:
► Understanding drivers of retention
► Simulation & optimization capabilities
► Scenario comparison & stress-testing
► Optimized rates & fees
© 2017 Fair Isaac Corporation. Confidential.
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Revenue vs. Rate Offered
Optimum
Price?
3.00%
3.25%
3.50%
3.75%
4.00%
4.25%
4.50%
4.75%
5.00%
5.25%
5.50%
5.75%
£s Retained vs. Rate Offered
Key Benefits:
► Improved overall portfolio P&L
► Transparency
► Auditability
► Scalability
Product Choice & Cannibalization also needs to be modelled
Acquisitions
(New-to-Bank, Existing Client)
Retention
(Back-book)
Renewals
(Fixed Rate, Interest Only)
Attrition & Prepayment Model(s)
Acquisition model(s)
Renewals model(s)
Variable
Variable
P(Attrite)
Fixed 1 Yr
Customer/
Prospect
Customer
Fixed 2 Yr
Customer
P(Prepay)
…
Application
Data
P(Switch)
Fixed 1 Yr
Fixed 2 Yr
…
Fixed n Yr
Fixed n Yr
Not Take-up
Attrite
Competitive
Data
Existing
Customer
Data
© 2017 Fair Isaac Corporation. Confidential.
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Existing
Customer
Data
Competitive
Data
Maturing
Fixed Home
Loan Data
Competitive
Data
Through-The-Door Applications
X
X
X
X
Retained
Volume
Monthly Applications
Competitor
Representative
APR
3.60%
3.70%
3.80%
Representative APR
© 2017 Fair Isaac Corporation. Confidential.
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APR
►
Predicts application
volume in core segments
►
Measures impact of own
APRs & Fees, competitor
APRs & Fees and ranking
in the best-buy tables on
application volume
Adverse Selection Modelling
• Relationship between rates, fees, rank and risk
• Competitive position shifts the distribution of applications
• Impacts approval rates, take-up rates & risk profile
Mean Application Score by Market Rank
As Competitive Rate position improves, we attract
higher scoring / lower risk clients
© 2017 Fair Isaac Corporation. Confidential.
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The Mortgage Pricing Optimization Process at Work
Monitor
Portfolio
Health
Scenario
Analysis
Contrast
Scenario
KPIs
Decision
Review &
Approval
Publish &
Distribute
Rates
Scenario Driven Analysis, Scenario Comparison, Drill-Down, Adjust & Simulate
Data
Governance
© 2017 Fair Isaac Corporation. Confidential.
Model
Governance
11
Operational
Governance
Mortgage Price Optimization: A Framework for Building Societies
© 2017 Fair Isaac Corporation. Confidential.
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How do I price? What offers should I make available to which members?
© 2017 Fair Isaac Corporation. Confidential.
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How do I reward my members for their loyalty while remaining profitable?
How can the latest available analytics help to drive mortgage offers?
• How can we target offers so that we can achieve all of our conflicting objectives?
• Retain existing members
• Profit & RoC hurdles
• Treating the Customer Fairly
• How can we use differentiated pricing and optimisation to reward our members
for loyalty?
•
© 2017 Fair Isaac Corporation. Confidential.
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Price Optimization for Building Societies: Objectives & Constraints
Banks
Building Societies
Objective: Profit
Maximization
Objective: Member Retention
Constraints
• Profit Hurdles
• £ Retained Volume
• # Members Contacted
• # Members Retained
• Member Satisfaction
• Return on Capital (RoC)
Dimensions
• Reward loyalty and/or
tenure
• Product
Dimensions
• Loan-to-Value
• Product
• FTB vs Existing vs Switch
• Loan-to-Value
• Proposition
• FTB vs Existing vs Switch?
Constraints
• Proposition?
• Member lifetime value?
© 2017 Fair Isaac Corporation. Confidential.
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Relationship-Based Pricing
Price optimization means pricing loyal, tenured members up?
Only a narrow-minded view that accounts for the differential in sensitivity only
leads to such an outcome…
Margin vs. Rate Offered
£4.0bn
£3.5bn
£3.0bn
£2.5bn
£2.0bn
£1.5bn
£1.0bn
£0.5bn
£0.0bn
3.5%
£60m
3.0%
£50m
2.5%
£40m
2.0%
£30m
1.5%
£20m
16
0.5%
£10m
0.0%
£0m
3.00%
3.25%
3.50%
3.75%
4.00%
4.25%
4.50%
4.75%
5.00%
5.25%
5.50%
5.75%
3.00%
3.25%
3.50%
3.75%
4.00%
4.25%
4.50%
4.75%
5.00%
5.25%
5.50%
5.75%
1.0%
© 2017 Fair Isaac Corporation. Confidential.
Revenue vs. Rate Offered
3.00%
3.25%
3.50%
3.75%
4.00%
4.25%
4.50%
4.75%
5.00%
5.25%
5.50%
5.75%
£s Retained vs. Rate Offered
Relationship-Based Pricing
Loyal, tenured members, with expected revenue streams properly adjusted for
future expected lifetime revenue, will usually have lower optimized prices
Margin vs. Rate Offered
£4.0bn
£3.5bn
£3.0bn
£2.5bn
£2.0bn
£1.5bn
£1.0bn
£0.5bn
£0.0bn
£90m
£80m
£70m
£60m
£50m
£40m
£30m
£20m
£10m
£0m
6.0%
5.0%
4.0%
3.0%
2.0%
1.0%
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3.00%
3.25%
3.50%
3.75%
4.00%
4.25%
4.50%
4.75%
5.00%
5.25%
5.50%
5.75%
3.00%
3.25%
3.50%
3.75%
4.00%
4.25%
4.50%
4.75%
5.00%
5.25%
5.50%
5.75%
0.0%
© 2017 Fair Isaac Corporation. Confidential.
Revenue vs. Rate Offered
3.00%
3.25%
3.50%
3.75%
4.00%
4.25%
4.50%
4.75%
5.00%
5.25%
5.50%
5.75%
£s Retained vs. Rate Offered
Relationship-Based Pricing
Mainstream banks offer
relationship-based discounts via
the concept of product proposition
An alternative is to overlay discounts
based on member lifetime value,
tenure, or similar Strength of
Relationship indicator
Account
Type
Rate
Standard
1.99%
£999
Strength of
Relationshi
p
Platinum
1.89%
£999
Very Strong
1.69%
£999
Premier
1.79%
£999
Strong
1.79%
£999
Medium
1.89%
£999
Weak
1.99%
£999
© 2017 Fair Isaac Corporation. Confidential.
Fee
18
Rate
Fee
Conclusions
© 2017 Fair Isaac Corporation. Confidential.
19
Conclusions
•
A structured analytic approach to retaining
customers is critical in such a transparent and
competitive market
•
Strategic mortgage offers that target customer
retention, rather than profit, are consistent with
member-centric goals
•
Relationship-based pricing can be built into
optimization frameworks, further underpinning a
positive member experience
© 2017 Fair Isaac Corporation. Confidential.
20
Thank You
© 2017 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.
Appendix
© 2017 Fair Isaac Corporation. Confidential.
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Data Requirements
Competitor
Information
AccountLevel
Data
Product
Information
CustomerLevel
Information
Treasury
Data
Risk Data
(CoF / FTP)
(PD / LGD)
Credit
Bureau Data
Modelling
Dataset
Macroeconomic
Data
Solution
Data Feed
Assumptions
FICO Optimization Solution for Pricing
© 2017 Fair Isaac Corporation. Confidential.
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Data Requirements - Mortgages
►
Historical Account-Level Data
► Account ID, Customer ID, Product ID
► Application & Booking Date
► Client Renewal Decisions (Redeem/Renew)
►
Product Offer Data
► Product ID
► Mortgage Balance, Amortization Period
► APR, Rate Term & Booking Fee
►
Historical Customer-Level Information
► Customer ID
► Maturing Mortgage Balance, Historical Payments
► Maturing Mortgage APR, Rate Term & Booking Fee
► Savings, Cards, Loans & Other Product Info
► Property Value, Income, LTV Ratio
► Age, Postal District, Strength of Relationship
© 2017 Fair Isaac Corporation. Confidential.
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►
Treasury & Risk Information
► Cost of Funds
► PD%, LGD%, Capital Allocation %
►
Competitor Information
► Date, Institution
► Caveat Group Parameters (LTV,
Balance, Region)
► Advertised Rate & Booking Fee,
Aggregator Ranking
►
Credit Bureau Information
► Customer ID, Month/Quarter
► # Inquiries Last 3, 6, 12 Months
► Cards, Loans, Mortgage Balances
► Revolving Utilisation & Open-To-Buy %
Mortgage Optimization Decision Models:
Matching Analytics to Market-Specific Products & Processes
•
•
UK
•
•
•
•
France
•
•
•
•
USA
•
•
Australia
© 2017 Fair Isaac Corporation. Confidential.
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Rates usually fixed over mortgage term, typically 15 years
Short-term fixed deals uncommon
High costs of early redemption and re-registering limits switching
Variable-rate mortgages usually capped
Market-driven (open) rates drive acquisition & refinance activity
Most Mortgages securitised & sold via secondary markets, not held as
assets on lender balance sheet
Strong focus on originations, efficiency & customer experience
•
Closed rates for fixed term Mortgages, flexible payment options for
variable rate terms
Proactive offers (term/rate/fee) for existing variable rate clients must
account for net impact via careful action-effect modelling
Relative price competition strongly influences client price response
•
•
•
•
Term-based closed rates for fixed & variable products
Local pricing discretion typically used for non-advertised rates
Term renewal events enable client-level retention pricing strategies
Clients expect negotiated pricing for originations & renewals
•
Canada
Term-based rates with booking fees, fixed vs. tracker rates
No local discretion: rates advertised via “Best Buy” tables
Segmented pricing by product/LTV tier
Relative price competition strongly influences client price response
Grid-Based Pricing Strategy Optimization & Simulation:
Discovering Optimal Rates & Fees by Portfolio Segment
© 2017 Fair Isaac Corporation. Confidential.
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Acquisition Model #1:
Estimating Application Volumes
Model 1: Application Volumes
•
Application volume prediction models built
independently for a defined set of core “macro”
segments (region/product)
•
Predictors include own & competitive rates, and bestbuy table rankings
•
The model will capture sensitivity to rate changes as
well as seasonal effects
© 2017 Fair Isaac Corporation. Confidential.
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X
!
X
Approval
Rate
X
Take-up
Rate
X
" # $%&
Acquisition Model #2:
Accounting for price-driven changes in approval rates
Model 2: Approval Rates
•
•
•
•
Relative rate competitiveness drives changes in
application score distributions, which can impact
approval volumes
To account for this, we model expected distribution
of application scores, and expected approval
volumes given rates relative to competition
This approach accounts for adverse selection when
estimating approval volumes
During optimization process, predicted application
scores for each observation in the baseline dataset
used to estimate approval rates & volume
© 2017 Fair Isaac Corporation. Confidential.
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X
!
X
Approval
Ratei
X
Take-up
Ratei
X
" # $%&
Application Score by Relative Rate
Higher Rates
Lower Rates
Acquisition Model #3:
Customer Take-up Model
Model 3: Customer Take-Up
X
!
X
Approval
Ratei
X
Take-up
Ratei
X
•
Predicts the take-up probability for
each approved application
•
Measures impact of the final
customer rate (including discretion)
on customer take-up probabilities
•
Estimates derived from model built
using customer-level data, scaled
by application and approval
volume estimates from models 1 &
2
•
Multi-model approach captures
impact of competition and own
rates on applications, approvals,
and price elasticity to drive flexible
grid-level discretion policies
•
Pending discussion of policy &
data availability, existing clients
may be modelled separately
Customer Take-up Rate
" # $%&
Pricing
Grid
Cell
E
C
A
Final APR
© 2017 Fair Isaac Corporation. Confidential.
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Retention Model:
Impact of Indicator Rate changes on back-book margin and attrition risk
Retention Model
•
Focus is on attrition risk and variable rate backbook margin impact caused by changes to rates
•
Rates defined at a product/segment/LTV band level,
but impact on back-book is assessed at an
individual customer-level
•
Typically built as a binary models with rate
interactions, focusing on the net impact of potential
new rate strategies on the overall customer
relationship
•
Additional action-effect predictors must be built in to
support the development of proactive retention offer
strategies
© 2017 Fair Isaac Corporation. Confidential.
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"1 # &
X
X
" # $%&
Renewals Model: Discretion Strategies to Retain $Dollars-at-Risk
Fixed Rate & IO Renewal Models
•
Measures the impact of changes to rates on retention of
fixed-term and interest only renewals at a customer level
•
Since maturity dates are known ahead of time, discretion
policies may be defined at an individual customer level,
or at a grid level if required by front-line systems
•
Typically built as a multinomial logit (mlogit) models with
rate interactions, enabling the measurement of take-up
probabilities across all available products
•
“mlogit” structure enables the capture of cross-response
elasticities for more accurate assessment of the impact
of new rate strategies
•
FICO recommends building separate models for the
Fixed Rate and Interest Only populations
© 2017 Fair Isaac Corporation. Confidential.
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" # 7&
X
X
" # $%&