IFRS 9 automation and loan pricing

Copyright©2018BBAnalytics
Copyright © 2018 BB Analytics
BankingBook Analytics:
IFRS 9 and Loan Pricing Automation
Sept. 2018

Executive summary
• Banks continue to be challenged by complexity of IFRS 9. The technical
complexity challenge is compounded by associated high cost of managing and
running IFRS 9 models
• Developing IFRS 9 analytics and applying them in day-to-day business can
provide an important competitive advantage from pricing perspective
• To realize these benefits, and to address computational complexity under IFRS 9,
BankingBook Analytics has developed an automated solution
• The deck provides an overview of our solution and the key benefits that can be
realized
2
This deck proposes a way forward on industrializing IFRS 9 and developing
credit applications using Expected Credit Loss as the key pricing metric. The
deck is organized as follows:

Content
3
Automation application design1
2 Cost-benefit analysis
5
Benefits of using BankingBook Analytics3
Appendix 2: Our approach
Appendix 1: IFRS 9 requirements4

Content
4
5

Towards industrialization of IFRS 9
5
Standalone Excel
model
Automation and
production
Validation
• Production
environment in
Excel is not
designed for
frequent and heavy
data processing
• Prone to
operational risk and
errors
• Significant FTE
involvement –
maker/checker
• Lacks database
connectivity
• Online production
environment is
stable and
customized
• Cuts down FTE
times by 2/3rd and
creates savings
• Application in loan
pricing
Out of scope of this presentation
Current state

BankingBook Analytics’ (BBA) IFRS 9 application automates
modelling and reporting thus saving significant cost and
removing complexity from the standard
6
Data input Analytics engine Output
Standalone
Rated/
Implied PD
Unrated/Rated
Collective
observations
Lifeofloan
estimate
Lifeofloan
estimate
Survival forecast
Marginal probability of
default forecast
Cumulative Probability of
default forecast
Macroeconomic overlay
LGD forecast
Effective Interest
rare/Discount factor
Exposure at default
forecast
User-defined Stage
Assignment
Present value of credit
losses (ECL)
Stage 1 ECL
Stage 2 ECL
Stage 3 ECL
Next
portfolio/Account
Submission
Data
warehousing
IFRS 9 data output
Cloud hosted

Data input
Automated and manual data input options
7
Data input
Automated bulk data
input
(over 1,000 obligors
with 5, 4, 3, 2, 1 years
in residual lives)Flat file/CSV
API
Manual
Standalone
(e.g., project finance,
large corporates)
Calibrated PDs
Master scale
Manual Collective
(primarily for retail,
e.g., product view,
pricing view, similar
ratings)
Censor data
Defaults
Performing
Calibrated
PDs
Master
scale

Analytics engine
8
Engine
• PD’s derived as a
function of
defaulted accounts
• Historical default
bias removed by
using survival
function
Exposure at
Default
Loss Given
Default
• Current PDs used
• Implied PDs
developed for
unrated
• Historical default
bias removed by
using survival
function
Top-down
Bottom-up
Macroeconomic
Adjustment
Exposure at
Default
Loss Given
Default
Macroeconomic
Adjustment
Separate BBA model
Term/Residual
life
Term/Residual
life

Output
Application provides 3 ECL metrics for each loan – analyst will have
to choose which one to apply
9
ForeveryLoan/investmentthreemetricsare
provided
Credit Impaired
Significantincreaseincreditrisk
sinceinitialrecognition
N
Y
Stage 1
Stage 2
Stage 3
12 month expected
credit losses
Lifetime expected
credit Loss
Lifetime expected
credit Loss
Output

Content
10
5

COST BENEFIT ANALYSIS FOR A MID-SIZED CREDIT UNION
Automation - size of prize for smaller banks
• Size of prize driven by functional cost savings (from ‘fire-fighting’ to a streamlined process)
o Automated data uploads
o IFRS 9 Expected Credit Loss metrics (removes complexity)
o Automation of reporting
FTE time/month* FTE time/year
Total FTE time (in $)
@ $150/hour
Without automation 36 hours/month 432 64,800
With automation* 4 hours 48 7,200
Net savings/year $57,600
*Excluding escalation
• Prevention of operational losses not taken into account/monetized
11

12
0
100
200
300
400
500
600
8/5 18/5 28/5 7/6 17/6 27/6
Case study: Benefits tracking
BankingBook Analytics has significant experience in change
management and process refinement
Example metric: time spent administering IFRS 9 was reduced by half
Hours/quarter
Average time spent
during the pilot
Case study
‘Getting up the learning curve’
Automation benefit

We have been at the forefront of IFRS 9 implementation in Canada
and worked alongside Big 4 firms for IFRS 9 implementation and
validation
1-year Expected
Credit Loss
Lifetime Expected
Credit Loss
(Performing)
Lifetime Expected
Credit Loss (Non-
performing)
Benchmarking
• IFRS, “IFRS 9 Financial Instruments”, July 2014
• Global Public Policy Committee (GPPC), “The implementation of IFRS
9 impairment requirements by banks”, June 17, 2016.
Validation
• PD validation
• LGD validation
• EAD validation
• Challenger model
• Confidence
Interval approach
• LGD validation
Case study: IFRS 9 ECL validation
13

Credit application
Analysis
Adjudication
Post-automation benefits far outweigh the initial investment
required
DB connectivity
Processing engine
Reporting
Wholesale lending
workflow
ECL-powered
Loan pricing
IFRS 9 automation
Automation benefits
ECL-based pricing
Hurdle rate
Risk-adjusted
pricing
14

Distribution of non-captive Canadian banks by assets
Top-6 Canadian banks control 95.6% of the aggregate banking
assets, with remaining 4.4% managed by remaining 18 banks
15
• Total assets under management at top-6 Canadian banks are approx. $4.7Tn, representing 95.6%
of the aggregate banking assets
o Assets managed by remaining 18 banks are $207.7 Bn
0
1
2
3
4
5
6
7
$35M-100M
$100M-
400M
$400M-
800M
$800M-1.7B
$1.7B-6B
$6B-18B
$18B-51B
$51B-144B
$144B-490B
$490B-1.2T
• CIBC
• BMO
• BNS
• TD
• RBC
• National
Bank
• Equitable Bank
• Home Trust
• Vancity Community
Investment Bank
• Canadian Western Bank
• Laurentian Bank of Canada
• B2B Bank
• Concentra Bank
• Meridian Bank
• VersaBank
• HomEquity Bank
• CS Alterna Bank
• UNI Financial
Cooperation
• General Bank
• Industrial And Commercial
Bank of China
• KEB Hana Bank Canada
• First Nations
Bank of
Canada
• Street
Capital
• DirectCash
Bank

Return below hurdle rate implies capital depletion
16
Banks
Risk-adjusted return using
CAPM
National 25.66%
TD 18.72%
Scotia 18.24%
RBC 18.23%
BMO 14.49%
CIBC 13.13%
CWB 15.40%
Laurentian 9.03%
VersaBank 4.55%
Average
risk-
adjusted
return:
18.1%
Average risk-
adjusted
return: 9.6%
Domestic Systemically
Important banks
Non-DSIBs
0 1 2 3 4 5
VersaBank
RBC
TD
CIBC
National
Capital consumption
zone*
Capital creation zone
Laurentian
CWB
Risk-adjusted
capital (K)
Size of bubble = risk weighted asset (in C$M)

Content
17
5

Creating business-as-usual capabilities
18
Retail
Corporate
Investments
Regulatory
and Legal
docs
Businessmodel
SPPI
Measurement
Transitionand
disclosure
DIA
Documentation
On-goingsupport
Modelling
Capacity
enhancement
Systems and
reporting
Special loans
IFRS 9 Program
creation
IFRS 9 Business
as usual

We understand data
Our modelling roadmaps that address data challenges
19
Lack of data Lack of ratings
Lack of internal
expertise
Correctness Timeliness
System & Processes
Model design
Implementation
Regulatory
expectations
Management
Audit and assurance
Industry practice
Cost and effectiveness
Model design based
on data availability
Reporting and
disclosure that
addresses internal and
external needs
Developed in
collaboration with audit
and assurance firms
Best practice
approach
Consulting only on as
needed basis (Plug
and Play solution)
Stakeholders’ expectations
of compliance
BBA Value Proposition

Sign-up for 30-day free trial of our IFRS Impairment Analyzer (IA)
20
Request your BBA’s IFRS 9 IA free 30-day trial
Complete below or call 905-499-3618
First name*
Last name*
Title*
Bank*
Email*
Phone number*

Content
21
5

Stage 1: Performing
Key requirements
§ Non-credit impaired
§ Gross carrying amount using
Expected Loss
§ ECL to be calculated as the
difference between the book value
of an asset and the discounted
expected cash flows of the asset
using the Effective Interest Rate
for the product (EIR)
Data & Calculation Approach
§ Point in time determination of risk parameters
Where,
v = discount factor for horizon k
!"#$
%
= Forward looking 12-month PD
&'"#$ = 12-month LGD
()"#$ = 12-month Exposure at default in dollars
ECL12= v ∗ !"#$
%
∗ &'"#$ ∗ ()"#$
22

Stage 2: Under-performing
Key requirements
§ Non-credit impaired
§ Gross carrying amount using
Expected Loss
§ ECL to be calculated as the
difference between the book value
of an asset and the discounted
expected cash flows of the asset
using the Effective Interest Rate
for the product (EIR)
Where,
vk = discount factor for horizon k
= Forward looking PD within the 12 moth period between k
and k-12
= Forward looking LGD within the 12 moth period between k
and k-12
!"#$
%
&'(!
)
= Exposure at default in dollars for account that default in 12
month period between k and k-12
LT = Expected remaining lifetime of the account
LOL ECL= ∑!,#$
-.
/!
!"#$
%
0(!
)
∗ !"#$
%
23(!
)
∗ !"#$
%
&'(!
)
!"#$
%
0(!
)
!"#$
%
23(!
)
23

Stage 2 definition requirements
24
Qualitative triggers
Changes in credit ratings
• Drop in external/internal
ratings
Changes in internal price
indicators of credit risk
• Significant deterioration of
LTV
• Breaches in financial
covenants
• Increased pricing
Changes in external market
indicators
• Rise in variable rates for
borrowers’ obligations
• Drop in borrower’s bond
prices
• Increase in credit default
swap prices
Changes in business,
financial or economic
conditions
• Macroeconomic deterioration
• Sectoral meltdown
• Industry downturn
• Increase in unemployment
rate
Changes in operating results
• Decline in revenues/margins
• Financial challenges
Other qualitative inputs
• Litigations
• Negative media
• Profit warnings
Quantitative triggers

Stage 3: Non-performing
Key requirements
§ Credit impaired
§ Under Stage 3 (where a credit
event has occurred, defined
similarly to an incurred credit loss
under IAS 39), interest revenue is
calculated on the amortised cost
(i.e., the gross carrying amount
after deducting the impairment
allowance
LOL ECL = "#$#%&' ∗ )*+ ,
)*+ , = Lifetime LGD for defaulted accounts
25

Content
26
5

PD Analysis – our approach
27
Portfolio Example asset class
PD modelling
approach
Forward PDs
Delivery
approach
• Standalone/Single
account
• Residential real
estate
• Commercial real
estate
• Corporate loans
• Project finance
• SME/Mid-market
loans
• Cox regression for
Survival Function:
!"#
= %&'())+
∑
-./
0
1-23
• PD is given by:
45 6 = 1 − !5(6)
• Interpolation and
Extrapolation
9
= 9: + < − <: ∗ (9>
− 9:)/(<> − <:)
• Consulting
• Customized
model
development
• Cohort
• Homogenous pools
• Lines of credit
• Retail pools
• Loans without IRB
ratings
• Kaplan-Meier
estimation of
hazard function
(conditional on
survival) to remove
potential biases in
data (e.g.,
Censored data)
ℎ 6, B + 6, C
=
DE,C)
!5C(6 − 1)
• Software with
some consulting
• Securities/bonds • Externally rated • Transition matrices • Probit model
• Markov models
• Software
• Consulting

EaD Analysis – our approach
28
Portfolio
Example asset
class
Life-of-loan EaD
Delivery
approach
• Amortizing • Residential real
estate
• Commercial real
estate
• Corporate loans
• Project finance
• SME/Mid-market
loans
• Usually contractual • Prepayment ratet =
!"#$%&'#()*
+",-,(%),.( /%0%(1#
!"#$%&'#()"%)#* =
∝ + 5
678
9
:6;6 + 5
<78
=
><&<
:6 = ?. − #AA,1,#() .A '%1".#1.(.',1 B%",%/0#C
;6 = D%1".#1.(.',1 B%",%/0#C
>< = ?. − #AA,1,#() .A 0.%( 0#B#0 B%,"%/0#C
&< = E.%(0 #B#0 B%",%/0#
• Consulting
• Customized
model
development
• Revolving • Lines of credit
• Retail pools
• Loans without IRB
ratings
• Mean Residual Life
=
F&(1 + :I
(
)
∝)K
− )
(1 − L*)
• Exposure = Used Limit + K1
x (Unused
Limit),
Where, k =
M*6N6OP*6Q9RSTUVWXYM*66OP*6Q9 RSTYX
Z6=6*RSTYX[M*66OP*6Q9 RSTYX
• Software with
some
consulting
K1= Sometimes referred as DDF – Drawdown factor or CCF – Credit Conversion factor

LGD Analysis – our approach
29
LGD Delivery approach
where the indexi runs over all types of collateral and the Value of Collateral before
Defaulti
Here FBS is given by:
with the standardized normal distribution N, the lognormal distribution
• Consulting
• Customized model development1
1 ,0
n
i ii
Max
Recovery Rate Value of Collateral before Default
LGD Max
Exposure at Default
=
æ ö×
ç ÷= -
ç ÷
è ø
å
( ) unsecured, (1 ) (1 % . )BSLGD F VtL LGD CureRate Admin Costss= × × - × +
( )
( )( )
2 1
2
N( d ) N( d )
,
lognorm 1,ln , exp( ) 1
BS
VtL
F VtL
VtL
s
s
- - × -
=
-
( ) ( )
1 2
ln ln
;
2 2
VtL VtL
d d
s s
s s
= + = -

Macroeconomic modelling
Our approaches are mapped to the client’s desired sophistication
level
Transformation Function using
Probit
Systemic Factors distribution
Default
Rate Distribution
Forward
default rate
Economic
conditions(unfavourable
economic
conditions)
(favourable
economic
conditions)
30

Copyright © 2018 BB Analytics
Thank you for your time!
BB Analytics
Leading on Solutions | Leading on Impact
The Exchange Tower, 130 King Street West, Suite 1800 Toronto, Ontario M5X 1E3
Canada
+1-905-499-3618 contact@bankingbookanalytics.com Bankingbookanalytics.com

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