Global approach to successfully achieve the digital transformation of the bank. Simple road map to turn the classical banking activity into a real smart digital bank.

1. For the use of Mung Ki Woo only
BANKING AS A SERVICE - BAAS
Or how to turn the classical Bank into smart banking 4.0 Mehdi NOUAR
September
2018

2. For the use of Mung Ki Woo only
CURRENT BANKING
KEY FINDINGS CUSTOMER EXPECTATIONS
 Limited effectiveness/efficiency of conventional banks
concerning KYC AML FTC with reputation risk,
fines/penalties consequences;
 Lack of flexibility (slowness) of conventional banks in
opening account operations or credit decision;
 Traditional banks poorly organized to meet the
expectations and new generations uses;
 Frontal competition of GAFA thanks to customer behaviour
data-driven with oversimplification/banalization of banking
services;
 Limit of the classic banking model facing over 2 billion of
underbanked individuals worldwide (including Africa,
South America and Indian sub-continent).
 Usefullness and simplicity;
 Quickness and transparent execution;
 Operational efficiency;
 Safety and integrity;
 Smooth innovation;
 Justified cost.

3. For the use of Mung Ki Woo only
VISION OF BAAS OR SMART BANKING 4.0
 Paperless, no credit card, no check, no bank branch or banking agency only mobile application.
 Very fast and relevant decisions, innovative services, powerful user experiences and uses for new generations.

4. For the use of Mung Ki Woo only
HOW TO REACH BAAS OR SMART BANKING 4.0
 The initial postulate is that the Bank of tomorrow is a multifaceted services around an offer of credit and savings.
 Services, credit or savings require both a perfect knowledge of the customer or the prospect and an appropriate product offer.
Retail Professional SME
KYC AML FT Risk Assessment
Customer profiling Product adequacy
Automated
Decisioning
Same algorithm logical
applyed to all type of
customers
This postulate and these
assumptions can be
translated into ONE
SYSTEM abolishing silos

5. For the use of Mung Ki Woo only
PREDICT ONE SYSTEM FOUNDATION
DECISIONING AGENT
RISK ASSESSMENT
MODULE
PROFILING MODULE
IDENTITY: KYC-AML-FT
MODULE
Unique Banking ID
Authentification
blockchain encapsulted
BANK
customer
Datamart
Traditional data
Non-traditional
data
& all possible
acessible data Credit bureau
independent
rating/scoring &
PD LGD
DATAMART
Retail Professional SME One system: Advanced Deep Learning
Assessment Profiling Decisioning
Data driven approach
XX,000 data points
Rating/scoring
Customer profiling
Decisioning time<60 sec

6. For the use of Mung Ki Woo only
PREDICT ADL-APD KEY COMPONENTS 1/4
 Online information questionnaire, online document
integration, photo selfie, electronic signature, digital
identification procedure (see below);
 Automation and robotization processes (ARP) on Identity
control, identification of the beneficial owner, fraud identity
theft detection, crossing sanctions lists (EUR/US), FATCA tax
module;
 Recovery of professional reputation, current and past
management mandates, screening management prohibition
and possible sanctions;
 Executives/Management quality rating;
 Web footprint investigation, social networks ratings ;
 Access to external databases to cross check the
information in case of doubt or suspicious facts.
Determining a security identity rating;
 Assignment of a unique identification bank ID using the
Blockchain Digital register (in liaison with the R3
Consortium for example);
 RGPD Respect Module (Europe);
 Digital identification procedures: Biometrics, facial
recognition, voice recognition, fingerprint, retinal scanner.

7. For the use of Mung Ki Woo only
PREDICT ADL-APD KEY COMPONENTS 2/4
 Classical Evaluation from traditional data (Score card
sector -> Using existing models and then gradual
transformation);
 Unconventional Evaluation (cognitive INSIGHT) from
meta data recovered from structured and
unstructured web streams;
 Risk assessment by integrating a specific stress-
testing process into the classic risk assessment;
 Integration of specific parameters related to fraud,
spoofing, fraudulent assignment;
 Alternative Evaluation for unconventional customer,
underbanking customer or not;
 Determination of rating/scoring and association with
a probability of default and a maximum exposure
coefficient related to a dimensional matrix;
 Risk pricing component for final pricing
determination.

8. For the use of Mung Ki Woo only
PREDICT ADL-APD KEY COMPONENTS 3/4
 From a behavioral/trend following analysis of banking
Group millions of clients, there will be a definition of typical
profiles combined with specific behaviors. The behaviours
will be crossed with the types of banking products.
 The Module will allow for the establishment of matching rules
between products and customer profiles at different periods
of the business cycle.
 Profiling will define risk envelopes in pre-authorization.
 The approach chosen will be in cognitive INSIGHT mode in
order to better understand and also anticipate possible
changes in behavior (to pay).
 The module can also be oriented in a prescriber approach by
analyzing the client profile and comparing it with the
theoretical needs, the expressed needs and the needs put in
place.
 Profiling will also be in a precursive detection logic of
possible product drifts in a rollover cycle.

9. For the use of Mung Ki Woo only
PREDICT ADL-APD KEY COMPONENTS 4/4
 The heart of the decision engine with an integrated
learning module (cognitive INSIGHT). Decision taken
through a neural network triggered by the request to open
an account or to validate an operation fed by the different
components:
1. Identity module
2. Risk assessment module
3. Profiling module
 The neural network will adapt regularly according to the
demands, the change of environment, the performance of
the portfolios, risk policy evolution and the feedback of
the self-learning approach.
 At this final stage, the rating/scoring will be potentially
reassessed to take into account all the available
information. Consequently PD, EAR, LGD and pricing are
also reassessed.

10. For the use of Mung Ki Woo only
GOALS
 Setting-up superior customer uses experience application;
 Performing real time decisioning;
 Being a recognized data driven smart banking actor;
 Being a recognized automated credit bureau;
 Being an industry leader with one of the lowest default rate
combined with a sharp growth rate.
CREDIT
SAVINGS
SERVICES
• Digital safer
• Payment
• Cash Transfer classic or
crypto
• Digital crypto Wallet
• Digital funding pot
ADL-APD
BAAS OR SMART BANKING 4.0 GOALS

11. For the use of Mung Ki Woo only
DISCLAIMER
 This document may not be used for any purpose other than that for which it was intended and may not be reproduced, disseminated or
disclosed to third parties, whether in part or in whole, without prior written consent from MEHDI NOUAR. No information contained in this
document may be interpreted as being contractual in any way. This document has been produced purely for informational purposes. It consists
of a presentation created and prepared by MEHDI NOUAR based on sources it considers to be reliable. MEHDI NOUAR reserves the right to
modify the information presented in this document at any time without notice. MEHDI NOUAR will not be held liable for any decision taken or
not taken on the basis of the information in this document, nor for any use that a third party might make of the information.
 This document has been registered to attest to its anteriority(existence of prior art).

12. For the use of Mung Ki Woo only
Mehdi NOUAR
September 2018
THANK YOU