Case Study: Rule-based Technology in the Mortgage Domain (special focus on Automated Underwriting)
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Transcript of Case Study: Rule-based Technology in the Mortgage Domain (special focus on Automated Underwriting)
Business Rules Forum 2007
Gil Ronen
Jeff Adler
Case Study:
Rule-Based Technologies
in the Mortgage Domain
(special focus on Automated Underwriting)
[email protected] http://www.linkedin.com/in/gilronen
2
Roadmap
Overview of Mortgage Value Chain and Life Cycle
Origination
Processing
Servicing
Lessons Learned
Questions and Answers
Mortgage industry deconstructed into a set of processes/tasks
represented using business rules then folded back into a
coherent structure across the mortgage value chain
3
Sample Clients
Countrywide
Saxon
Wells Fargo
Chase
Aegis Mortgage
ACT Mortgage
GMAC
HomeBanc
NetBank
AmNet (Wachovia)
Ownit
Fifth Third Bank
First Greensboro
First Preference
Ford Motor Credit
Ocwen
Equifax
American Express
Complete Size and Product Spectrum
United Mortgage
Option One
First Horizon
PHH
UBS
Realty Mortgage
Paul Financial
Taylor Bean Whitaker
Citibank
Mortgage Value Chain
Mortgage Life Cycle
5
Mortgage Lifecycle
6
Mortgage Origination Timeline
7
Key Accounting Dates
8
Value and Risk across the Lifecycle
Best - Fit Deal Structuring
Mortgage Suitability
Risk - Based
Pricing
Cross - Sell
Origination
Borrower Credit
Risk
Pipeline Risk Management
Fraud Detection
Processing
Loss Mitigation
Servicing
9
The Secondary Mortgage Market
Depository
Institutions
Non-
Depository
Institutions
Hold in Portfolio
Sell Loan Investor
Secondary
Market
Conduit
Sell Whole Loans
Pool Loans into
Mortgage Backed
Securities
Issue bonds
backed by LoansSell Bonds
Sell MBS
Agencies
Pension Funds
Life Insurance Companies
Commercial Banks
Thrifts
FNMA
FNMA
FHLMC
GNMA
Private Investment Banks
Commercial Banks
Mortgage Banks
Mortgage Brokers
Loan
Originators
Origination
11
Optimize Borrower Experience
During Sales and Origination Lenders have one shot at capturing a
customer.
Moving knowledge about products & UW criteria to POS guarantees
quick turnaround time, accuracy and information at critical juncture.
Business rules define the offerings, facilitate the interaction.
Right PriceRight
Product(s)
Customer
Profile Preferences
Assets and Debts
Credit
Market
Conditions Risk-based pricing
Profitability
Credit
Point of
Contact
Touch Points
Business Channels
Interface Mode
12
Adding Value to Customer Interaction
Interact with customers consistently and profitably through all delivery channels and touch points
Identify the best products to meet borrower needs and objectives (e.g. “Build Equity”, “Lowest Payment”)
Offer a competitive price point and pricing options
Determine conditions needed to process and close the loan
Identify opportunities to cross-sell products that would be of interest/benefit to the borrower
Business Rules Applications
Manage products and pricing across touch-points
Deal Structuring – Rules can describe strategies to configure a loan similar to the best Loan Officers.
Mortgage Suitability
Cross-sell rules
13
Alternative Loan Configurations
Requested Loan
Purchase $ 540 , 000
Loan Amount $ 440 , 000
LTV 82 %
Goal Lowest Payment
Alternative
Configurations
75 - 7 - 18 ( $ 415 / 25 K )
80 - 0 - 20 ( No MI )
85 - 0 - 15
80 - 10 - 10
IO No 82 % with IO = “Y”
Combo No
LTV 90 %
14
Bringing Intelligence to the Sales process
Control product offerings Low down payment loans – objective-based
Low Income borrowers – FHA loans
Relationship customers – bank accounts, HELOCs
Special properties – construction loans, co-ops
Strategies for creating deals and offering features Mortgage Insurance
Interest Only
Combo loans
Cross-sell opportunities Home equity seconds
Life Insurance
Mortgage Protection insurance
15
Alternate Scenarios
Alt. Scenario Description When Created
Interest Only (IO)
Interest Only Objective = Minimize Monthly Payment AND IO
= No
Non-IO Create scenario with non Interest Only
Objective != Minimize Monthly Payment AND IO
= Yes
LTV/COMBO Change LTV to 80-20 If requested LTV [85-100]
Loan Amount Modify Loan Amount Based on price hits [60K, 500K]
Stated Doc Change doc type to Stated Primary Borrower is self-employed, Full Doc
requested
16
Alternative Deal Offerings
17
Best Fit – Minimize Monthly Payment
Strategies for structuring a
deal with goal to minimize
monthly payment
Represents different lines of
reasoning
Minimize
Monthly
Payment
Minimize
Rate
Increase Down
Payment
Lower Loan
Amount
Select Variable
Rate Product
Increase Points
Purchase:
Do not Finance
Closing CostsPay Closing
CostsRefinance:
Do not roll-in
Closing Costs
Select Fixed Rate
Productwith
Longer Term
18
Power to the People
19
Mortgage Suitability
Suitability is often defined as “a subjective evaluation by the lender whether a product is best for that borrower”
Suitability is a hot topic in a market where foreclosures are spiking due to customers being given loans that they were eligible for but that they cannot repay due to volatility of rates, insufficient income, etc.
Inversely, innovation in the market has allowed for the proliferation of exotic products that are suitable under certain circumstances:
Pay Option ARMs allow flexible payments and are suitable for customers who are paid seasonally or are professional investors – they are not for average salaried customers with low sophistication.
The solution is not to reduce flexibility of product offerings but to look at Suitability.
Lack of borrower sophistication and poor risk management in the secondary markets are key factors behind the suitability movement
20
Business Rules and Suitability
Introduce a new class of Suitability Rules to measure the degree of suitability of a deal offering
Concepts to include
Additional functionality to better communicate offerings of riskier products – explain the benefits and risks
Model borrower-centric what-if scenarios
Expanded modeling of borrower goals and objectives
Evaluate long and shorter-term needs
Risk assessment benefit messages and disclosures
21
Cross-Sell
Become a sole-source financial service provider working for the best-interests of customers
Build customer relationships
Know customer needs and objectives
Identify cross-sell services that will help customers achieve their goals
Point-of-Sale: Cross-sell decision support tools support point-of-sale customer relationship building by identifying products that will service the customer.
Offering sensible solutions to customer needs can clinch a deal
Servicing Portfolio: Reach out to existing customers and expand their relationship with you (Home Equity, Credit Cards, Insurance…)
22
Cross-Sell Decisioning
Enterprise Decisioning Platform
CRM
Data Base
Enterprise
Data
Business
Inquiry Business
Rule
Repository
Cross-Sell
Decisioning
Engine
Decision points often benefit from access to enterprise information
Processing
24
Processing Value and Risk
Borrower Credit Risk
Automated Underwriting – guidelines
Risk-Based Pricing – loan characteristic risk evaluation
Compensating factors – weighing positives and negatives
Pipeline Risk Management
Handling fallout – avoid losing loans before closing
Variation in interest rates - hedging
Fraud Detection
Reduce lender’s risk exposure to assist in loss mitigation
Income/Employment; identity theft; occupancy and property fraud
Underwriting
26
Automated Underwriting and Pricing
Business rules are most famously known for their use in
qualifying and pricing loans
These systems evoke the best that business rules have to
offer:
Reduce operational costs and streamline business process
Agility in face of rapidly changing market conditions
Real-time distribution across channels (guides, pricing, products)
Stability of rules throughout application time-line (application date)
and business process workflow (same rules for Pre Qual and UW)
27
Product Guidelines
Purchase and Rate / Term Refinance
Property Type
Maximum
LTV
(w/o Sec.
Fin.)
Maximum
LTV
(w/ Sec.
Fin.)
Maximum
CLTV
(w/ Sec.
Fin.)
Maximum
HTLTV
(w/ Sec.
Fin.)
Credit
Score
1-2 Unit Primary Residence (O/O)
$417,000 to $650,000 90% 90% 95% 95% 620
95% 95% 95% 95% 680
80% 80% 100%1 100%1 680
$650,001 to 1,000,000 70% 70% 70% 70% 620
80% 80% 95% 95% 680
$1,000,001 to $1,500,000 75% 75% 80% 80% 700
$1,500,001 to $2,000,000 70% 70% 70% 70% 700
28
Decision Processes
Strict Decisioning
IF Loan Credit Score < Guideline Credit Score
THEN Loan Application is Ineligible
Interpretive Decisioning
Difference Between Loan and Guideline Credit Score values
Greater than or equal to 0: Pass the Guideline
Less than 0 but greater than -10: Near-Miss – Not Sure what to do
Less than -10: Fail the Guideline
Loan Guideline Delta Result
700 680 20 Pass
675 680 -5 Near-Miss
640 680 -40 Fail
29
Compensating Factors
Guidelines are not true measurement of composite
borrower risk. They are heuristics designed to provide
an understandable structure for assessing loan
applications. Invoke other areas of borrower strength to
override the minor transgression of a guideline.
Exceptions work well in the limited context when single
factors are “Near-miss”. Consider the case when 2 or more
factors are near-miss:
LTV = 71 (Max 70) and DTI = 46 (Max 45)
Example: LTV within 5% of Guideline, will accept if
borrower meets 2 of the following
Credit Score at least 20 points above guideline
Borrowers show 6 months reserves when 3 or fewer are needed
DTI is at least 10 points lower than guideline
No 30-day lates last 24 months
30
Comprehensive Risk Assessment
Goal: Assess the strengths and weaknesses of the entire
case
How are CFs created?
Observe manual exception handling
Loan Performance data – not always seasoned, interest
environement changes
Policy – what investors will accept
31
Risk Scoring
A holistic perspective on borrower risk would look to balance all
risk factors in a loan application and determine the level of risk.
Higher Risk - The degree to which a factor exceeds a guideline
Lower Risk – The degree to which a factor is within a guideline
A composite risk score can be invoked in place of
compensating factors to provide a better measure of risk
32
Layered Decisioning
CF Analysis
Approved=Y?
Refer=N
Exceptions=N
AUS Decision =
Approved
Y
Ineligible=N?
AUS Decision =
Ineligible
Y
Refer=Y?
AUS Decision =
Refer
Y
N NScore Exceeds
ReferThreshold?
Y
N
AUS Decision =
Approved w/CF
N
Pricing
34
Pricing
35
Price-Rate Relationship
0.25 0.5 1.0 1.5
0.5
Rate
1.5
2.0
(0.5,0.25)
(1, 0.5)
(1.5,0.88)
(2,1.25)
1
Price
0.5
1.0
(-1,- 0.5)
Buy up
Buy down
Rate Adj Price Adj
- 0.5 - 1.0
0 0
0.25 0.5
0.5 1
0.88 1.5
1.25 2
Fraud Detection
37
Types of Fraud
It is believed that 10-15% of all Loan Applications contain misrepresentations
There are 5 sets of data that are targets for fraudulent activity
Income/Employment: Inflated Income, Falsified Employment
Credit: False identity
Property: Falsified appraisal data, wrong property type
Occupancy: Intent to occupy, investor listed as primary residence
Assets: Falsified bank statements, gifts, concessions
38
Objectives for Fraud
Falsification of data to purchase a property
Try to improve the 1003 data to ensure approval
Buy investment properties as Primary Residence to get better
terms
Misrepresentation of facts to profit from a mortgage
transaction
Flipping properties
Collusion among parties (Broker, appraiser, lender…)
Skimming Equity
Investor property with High LTV. Keep rent, do not make
payments
39
Actual Case of Repeated Fraud
09/06 – John Jones takes a purchase primary loan (SISA) from
Lender for residence in Woodland Hills, CA
10/06 – Carol Jones (wife) takes a purchase primary (NIVA) loan from
Lender for residence in Pasadena, CA
01/07 – John Jones takes a purchase primary (SISA) loan from
Lender for residence in Walton, KY
01/07 – Carol Jones takes a purchase investment (NIVA) loan from
Lender for property in Florence, KY and states she owns other
investment props in CA and IL jointly with spouse (no reference on
his applications)
07/07 – John Jones applies for a purchase primary (SIVA) loan from
Lender for beach-front condo in Naples, FL
40
Using Business Rules to Detect Fraud
Consistency of Data on Loan Application
Compare property state to employment or residency
Analysis of REO Schedule
Validating income levels
Comparison of new applications with CRM/Application
databases
Look for loans with same applicants with/without spouse
Look for trends in data
Use business rules to control workflow and integration with
third-party verification services
TALX – Income and Employment verification
Servicing Risk and
Portfolio Management
42
Loss Mitigation
Identify borrowers at risk of default and pursue appropriate
loss mitigation strategies designed to preserve
homeownership
Identify Loans within a servicer’s portfolio that may be at risk
for problems when their rates reset
Appropriate Strategies to Save Home Ownership
Loan Restructuring / Modification – ARM to fixed, 40 year term
Payment Deferral – tacking missed payments
Principal/Interest Rate Reduction
Other Strategies focus on helping borrower off load the
property prior to foreclosure
Technology
44
What Technologies Are Used?
Domain Ontology – Mortgage data is described more and more using MISMO
XML – Standard payload for system data exchange
Web Services – Standard protocols, service descriptions, …
Data Mining – Statistical regression, etc. to capture rules from unstructured data (e.g. actual manual exceptions, loan performance data)
Knowledge Acquisition/Business Analysis – Capture domain expertise (e.g. Sales, UW principles)
BPMS – Management of Business Process flow
Rules – Encapsulated business logic
Rule Engine – Encapsulated algorithm for handling Business Rules
BRMS – Business Rule Management
Usual Suspects: Systems Analysis, UML, DB...
45
What is MISMO?
Mortgage Industry Standards Maintenance Organization.
It’s mission is to develop, promote, and maintain voluntary electronic commerce standards for the mortgage industry.
Established in 1999 by the Mortgage Bankers Association, MISMO encourages participation from all sectors of the industry.
MISMO defines xml structures to coordinate transfer of information across the mortgage value chain. For example: Requesting an underwriting decision from an automated AUS
Ordering Title from a title company
Sharing loan closing data
Ordering or re-certifying credit from a credit reporting agency
Transferring loan information to a mortgage servicing company
Obtaining mortgage insurance coverage and monthly PMI
46
MISMO Influence
47
MISMO Credit Liability
<CREDIT_LIABILITY
CreditLiabilityID="CRLiab0002" BorrowerID="Coborrower"
CreditFileID="CRFilEFX02 CRFilTUC03 CRFilXPN02"
_AccountIdentifier="541712485999999"
_AccountOpenedDate="1999-02“
_AccountOwnershipType="AuthorizedUser“
_AccountReportedDate="2002-05“
_AccountStatusDate="2001-11“ _AccountStatusType="Open“
_AccountType="Revolving“
_CreditLimitAmount="16200" _DerogatoryDataIndicator="Y“
_HighCreditAmount="16200" _LastActivityDate="2001-11“
_MonthlyPaymentAmount="232“ _MonthsReviewedCount="39“
_PastDueAmount="1773" _TermsDescription="$232/M“
_TermsSourceType="Provided" _UnpaidBalanceAmount="11637“
CreditBusinessType="Banking“ CreditLoanType="CreditCard">
</CREDIT_LIABILITY>
48
Interoperability – Specification Initiatives
49
Rule Engines
Rules provide business logic encapsulation
Rule engines encapsulate rule processing algorithms
Most use RETE therefore engines are becoming a
commodity (MindBox ArtEnterprise, Blaze Advisor, ILOG
JRules,…)
RETE is an algorithm providing asymptotic performance – as
the number of rules increases the execution time stays the
same
50
Sample Business Logic – Gift Funds
Earnest Money and Gifts
If the LTV is 80% or less, 100% of the down payment can come
from a gift. This is only acceptable for those loans that are fully
or alternatively documented
If the LTV is 80% or greater, 5% of the down payment can come
from a gift. This is only acceptable for those loans that are fully
or alternatively documented
51
Employment History
Borrower
Employer
Previous
Employer
Most recent
CurrentEmployment
YearsOnJob
CurrentEmployment
MonthsOnJob
CurrentEmployment
TimeInLine
OfWorkYears
EmploymentBorrower
SelfEmployed
Indicator
EmploymentCurrent
Indicator = YES
Check for 2
Continuous Years
with Current
Employer
Check Multiple
Consecutive
Employment records for
2 Continuous Years In
Same Line of Work
CurrentEmployment
YearsOnJob
CurrentEmployment
MonthsOnJob
CurrentEmployment
TimeInLine
OfWorkYears
EmploymentBorrower
SelfEmployed
Indicator
EmploymentCurrent
Indicator = NO
2 Consecutive
Years of Self
Employment
52
Decision Tree vs. Decision Table
Employer
Relocation?
Distance >
35 Miles?
YES Current
WFHM
Customer?
No
Current WF
Customer?
No
YES
Code = CIG
YES
Code =
WFHM
No
Code =XX
Code = WF
YES
No
1 2 3 4 5 6 7
Employer Relocation
Moving Distance > 35 Miles YES
Current WFHM Customer YES YES
Current WF Customer YES NO NO YES NO
Agreement Code Assignment CIG WFHM WF XX WFHM WF XX
NONO
NO
Variable
RULE
NOYES
53
Comp Factors Model for DTI
DTIDelta
-1.5
-1
-0.5
0
0.5
1
1.5
-30 -20 -10 0 10 20 30
DTIActual
0
0.2
0.4
0.6
0.8
1
1.2
0 20 40 60 80
DTI Score is based on:
DTI Delta – the difference between guideline and actual DTI
-Dynamic range from -20 to 20
-Values below 0 are positive / above 0 are negative
DTI Actual – the Debt-to-Income percentage
-Dynamic range from 20 to 50
-Values closer to 20 are positive / closer to 50 are neutral
54
CF Scoring Models
Generates an overall score Indicates whether positive compensating factors outweigh any exception
violations
Maximizes the value of available historical data and incorporate industry best-practices May incorporate various scoring methods
Weighted-sum, neural network, fuzzy logic model, decision trees, etc.
-1.5
-1
-0.5
0
0.5
1
1.5
-30 -20 -10 0 10 20 30
-1.5
-1
-0.5
0
0.5
1
1.5
-40 -20 0 20 40 60
S
0
0.2
0.4
0.6
0.8
1
1.2
0 20 40 60 80
55
Automated Decisioning Transactions
Pricing Only Transactions
Multiple deals (vary by price point) with
pricing, no stipulations
Best Fit Transactions
Multiple deals (vary by product) with
pricing, no stipulations
Underwriting Transactions
Single deal with stipulations, no
pricing
Stated 1003
Stated Credit
Full 1003
Stated Credit
Full 1003
Full Credit
UBS Conduit Lender Live/X2 Quick Qual Quick Price Clayton Everbank
UWFF
UWFS
POSS
BFFS
BFSS
BFFF POFF
POFS
Correspondent
Wholesale
Retail
In Production
Future
UWSS
56
Mortgage Decisioning
Correspondent Customer Direct
Wholesale
Retail Affiliate
Other
Knowledge Repository
Pricing DB
Price/Rate Sheets
Price Determination/Database Price Feeds
Channels
Decisioning
Users
Rule Engine
POS/LOS/
Website/
Portal
Underwriter
Broker/Loan Officer
Borrower
Data Feeds
Due Diligence
Credit
Servicing
To support the various decisioning processes being called by
different channels, different interfaces, different users, using
multiple data feeds, at different points in the business
process the decisioning platform is created as a versatile
Service-Oriented Architecture
Final Words
58
ROI
Significant ROI for Business Rule Implementation
Reduced processing costs
Increased productivity
Automated workflow
Direct origination opportunities
More consistent decisioning across channels and business lines
59
Automated Decisioning in Mortgage-ROI
Up Markets – Increased Productivity/Volume
Down Markets – Lower Costs/Higher Margins
Decisioning – Consistent, sophisticated and fast processing of large and often incomplete data or data of suboptimal quality
Best Fit – Mimic behavior of best Sales staff anywhere in process
Underwriting – moving UW to POS, guarantee deal for brokers and exit strategy for Originators
Secondary Markets – No cost for re-UW to any set of Guides
Comp Factors – replace expensive manual processing
Pricing – add any adjustor rule easily
Knowledge Hub – centralize decision-making and maintenance of knowledge
60
Optimizing Value / Minimizing Risk
Across the value-chain: Understanding goals, values, pain
points, guiding principles, culture, standards, terminology
and finding common ground.
Loan Officers, Brokers, Underwriters, Processors, Due
Diligence officers, Secondary Markets, Traders, Securitizers
and Investors accessing the same Knowledge Base.
institutional centralization, industry standards, technology
standards, flexible and powerful architecture, centralized and
extensive/expressive knowledge repository.