Digitalisation in European Building Societies and Mortgage Banks - Building Society Association Conference London, November 2020
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Digitalisation in European
Building Societies and
Mortgage Banks
Building Society Association Conference
London, November 2020
zeb is the leading consulting firm for financial services zeb.Competences in Digitalisation and AI:
in Europe – specialized in banking and insurance Strategy development with methodological and
technological Excellence (Learning Journey, Design Thinking
Strong competence in Advanced Analytics and
Prototyping, Implementation and roll-out
Artificial Intelligence
>100 successful use cases, >15 years project experience in
Approx. 1,000 employees – incl. >150 data scientists the field of data strategy
and data engineers Methods, incl. Learning Journey, Design Thinking
Versatile and long-standing experience
Highly relevant expertise and reference projects with
major financial institutions Expert Advisory Board
Digitalisation in Three use cases showing potential Lessons learnt from
Europe and the UK of up to 80% in cost savings European projects
Digitalisation is a catch-up game for most European banks that has got a significant push
during Covid-19 times but still a lot of potential
Development of digital maturity of European Banks 2016 – 2020 (selected results from zeb‘s bi-annual Digital Pulse Check) Excerpt
161 EUROPEAN PARTICIPANTS
with a focus on GSA and CEE
Digital Performance Indicator (1-5) slowly growing from 2016- 20201
5
4 3,5
3,0 2,7 3,2 3,0 2,8 2,7 3,1
3 2,7 2,5 2,6 2,8 2,4
2,3
70% TOP DECISION-MAKERS 2,5 2,0
2
on board or head of division level
1
Digital Business Management & Processes, DPI
Strategy Model Organisation Data & IT
Typical BS 2016 2018 2020
50% of participants from REGIONAL BANKS &
BUILDING SOCIETIES
• European banks are slowly catching up on their initial lack of digitization
• Typical building society bank with a DPI of 2,5 in 2020, with the maximum 5
corresponding to a fully digital bank
• Focus so far mainly on quick-wins in the areas of strategy, business model and
management & organisation
• Complex and time-consuming transformation process in the area of
4th edition covering 8 YEARS and processes, data and IT has been mostly neglected so far
• Corona pandemic, especially in the area of digital collaboration models, as a
diverse TRENDS
driver of change
1 Source: zeb.digital pulse check 2.0-4.0; DPI is determined based on the weighted four dimensions digital strategy, business model, processes, data & IT, management & organization
Our research and surveys show that advanced analytics and artificial intelligence (AA/AI) can
add significant value – perception about good use cases differs significantly b/w countries
Further research and astounding market differences Excerpt
Results from a 2019/2020 comparative survey about applicability of AA/AI in UK and
German Building Societies and Mortgage banks1
Processing Corporate
Marketing HR
(Credit/Savings) Management
approx. approx. approx. approx.
Germany
83% 81% 52% 33%
approx. approx. approx. approx.
UK
50% 70% 30% 30%
1 Source: https://www.bankinghub.eu/innovation-digital/ai-in-building-societies-future
One key belief: AI/AA is a means to an end, not an end in itself - systematic identification
and selection of relevant use cases necessary to avoid bad investments
Solving problems | Identification and prioritisation of Use Cases ILLUSTRATIVE
Sources of “good” use case identification Evaluation criteria1
Sales strategy and concepts CRITERIA PROBABILITY OF SUCCESS
Concepts and principles from client advisory and sales, i.e. segments,
criteria, affinities, approaches, and touch points SOURCE OF
THE IDEA
Customer journeys and customer needs REALISABLE
Customer journeys reveal critical/ tipping points and magic moments as MARGINAL
a basis for use case application UTILITY
Expert workshops COST-BENEFIT
ANALYSIS
Workshops with experts and stakeholders to discuss relevance of identi-
ORGANISATION
fied use cases – ongoing projects and initiatives to be considered as well ’S
MATURITY
Value driver approach LEVEL
Cause-effect-relations within the business-model – thereby ensuring a DATA FOR
comprehensive view on use cases and their respective relevance CALIBRATION
Process-oriented-approach IMPLEMENTATION
Review and derivation of potential fields of action for redesign of TEAM
processes, process steps or activities – esp. in operations & support
SELECTION OF
ANALYSIS
Trend radar and use case maps METHOD
Trend scouting radar shows current trends and use cases – both on
individual use case as well as in a portfolio view (using maps) DEGREE OF
INTEGRATION
5In recent years, we have implemented use cases across the whole value chain of (mortgage)
banking – three deep-dives selected for today
Selected use-cases Examples | for discussion
OPERATIONS SALES FINANCE & RISK
CREDIT CUSTOMER CREDIT RISK
Documentation improvements Customer motive analysis Extended credit risk scoring Property valuation Macroeconomic risk
Analysis of written contracts Client peer-benchmarking Credit economic risk capital Risk Simulation Unsupervised ML fraud detection
5min digital mortgage process Client transaction map Credit portfolio optimization Cash flow forecasts Shadow rating
Mail / Complaint Analysis Contagion/concentration risk Stress testing Macroeconomic risk
DEPOSITS, CARD & PAYMENTS
Processing of opening documents Product pricing
NON-FINANCIAL RISK LIQUIDITY RISK FURTHER FINANCE AND RISK
Customer affinities Customer Message Analysis TOPICS
Model risk Deposit modelling
ASSETS & INVESTMENTS Micro (customer) segmentation
Cybersecurity risk LCR limit optimization Exploiting price potentials
Understanding clients‘ preferences Wallet sizing
Climate risk (Group vs. segment) Forecasting early loan repayments Predictive country risk
AI Custody Account openings Customer churn
Reputational risk Advanced LCR Standardized data collection FS
Extended analysis of portfolio Transaction history analysis
Revenue risk Market (asset) liquidity risk Business risk
PHYSICAL OPERATIONS Automated call center
Systemic risk Trading risk Automated cost structure analysis
CS Branch availability & Presence Chat/ Voice Bot for requests
OTHER …
MORE USE CASES…
For all areas and scopes, more use cases can be provided
Selection for today’s conference
6AA/AI (in conjunction with other technologies) helped to significantly speed up the
1
mortgage application and approval process to below 5 minutes and
Use Case – 5min E2E digital mortgage process 60-80% potential
• Considered a lighthouse project for digital
transformation of the business
Demand • Fast Implementation of MVP for standard
Identification of financing of the building society
needs
Simplified credit
simulation Login or registration
(incl. data transfer) • No Connection to legacy systems of the
building society (currently) planned
Collection of Request
Third party account personal data (incl. • Cooperation with external providers for
access release account
view) decision engine, APIs, hosting, etc.
Confirmation/
amendment of the
Query KSV & CRIF
revenue and • MVP implementation enables omnichannel
expenditure account
usage
Specification Object
• Initially primarily to be used by
Property valuation
data Automatic intermediaries such as brokers
Offer
Automatic offer or
further allocation
Approval • Efficient implementation of the MVP through
an agile approach (concept and
implementation)
First intermediary, Automatic Building society employees
later also customer
7Unsupervised machine learning approach using clustering algorithms reveals hidden
2
patterns in form of motives, which are used for targeted sales approaches
Use Case – Micro segmentation 15-25% potential
Data Method Outcome
Age Residence Machine Learning approach Targeted approach based on client‘s
using unsupervised learning personal cluster profile
Gender Education
Internal
techniques to find hidden
Marital status Hobbies patterns of the customer
Profession
Spending 25 years, 35 years,
Product portfolio
Application of clustering student employee
algorithms such as k-means to
Customer related data globetrotter regional focus
identify groups with similar
features extensive credit building loan
card usage agreement
Social economic data
External
Milieus Analyzing cluster groups to
identify the shared features that
Residence specific data a typical for this group Approach 1: Approach 2:
travelling property
8When using the kNN algorithm, loan repayment predictions could be improved by a
3
factor of 51 – leading to optimized liquidity management and loan prizing
Use Case – AI in forecasting early/ unscheduled loan repayments 5-15% potential
Prediction with 2-factor model of the financial institution
• Systematic overestimation
Prepayment amount
of the prepayments by
applying a factor model
• 38.52% deviation from the
real value at overall bank
level for the test portfolio
Contract number
Reality
Moderate volatility over the 200 contracts shown in relation to the Prediction with kNN
actual prepayments p.a.
Financial institution’s methodology • Most differences range
Constant prediction of prepayments as x% applied to the nominal value between
of each contract -€5,000 and +€5,000
Use of ML algorithm • 8.75% deviation from the
Good recall rate compared to reality both for peaks and for the real value at overall bank
absence of prepayments level for the test portfolio
1) Compared to the real prepayment amount of the contracts and the 2-factor model used by the financial institution
9Successful implementation of new technologies / methods typically follows a four-step
approach
Overview phased approach Illustrative | for discussion
01 02 03 04
VISION, STRATEGY & CONCEPT & FEEDBACK & IMPLEMENTATION
GUIDING PRINCIPLES PROTOTYPING ADJUSTMENTS & ROLL-OUT
Ambition „Define the goals“ „Start the journey” „Ensure learning“ “Generate Impact”
Definition of ambition, scope Derivation of requirements Iterative approach to Extension and add-on of
and strategic roadmap for AI Data preparation and ensure consideration of use cases and prototypes
Identification/ prioritization clearing, building and feedback and consequent Validation of business
Content of use cases along areas of validating models adjustments and case/ impact vs. ambition
application improvements
Field testing MVPs/ rapid “Go Live”
Description of prioritized use prototyping Training of models with
Enabling of employees,
cases new/ additional data
Impact assessment/ ensuring “mind set”
business case
Maturity check Definition of requirements Gathering and Validation of business case
Trend scouting (Role: Business Translator) consideration of feedback Design/ concept roll-out
Engagement Vendor selection of und requirements
Data & Analytics Strategy change management
options technology provider (Role: Business Translator)
Use Case identification …
Know-How: Methods, …
Establishing Governance
prototyping, test design,
& Organization
…
…
10Thank You for Your Attention!
Any questions?
Dr. Florian Stahl Christopher Mallin
Your Specialist IT & Building Societies
Mobile +49.151.12054173
Regional Banking Expert UK
Mobile +44 7921 495045
contacts florian.stahl@zeb.de
Office Frankfurt
Christopher.Mallin@zeb.co.uk
Office London
Dr. Dirk Holländer
Senior Partner | MD zeb UK
Mobile +49.151.12054016
dirk.hollaender@zeb.co.uk
Office London & Frankfurt
11Employing new technologies for the mortgage sales and onboarding process delivered
1
significant value to an Austrian Building Society
Use Case – 5min E2E digital mortgage process (1/2) Client Example
Customer situation Value delivered by AI/AA
An Austrian building society with over 1.3m customers has a business model
whereby mortgage sales depend upon linked savings products and requires
standardisation as part of digitalisation
Increase business Guarantee secure
volume contracts
Project challenge
Ensure risk-adjusted data requirements are included early on in the project to
have verifiable digital data resources clearly prioritised
Approach
Ensure Maintain
zeb has created a prototype and ultimately a solution that is intuitive, flexible, compliance with sutstainable
creative and customer focused, implemented in agile way regulators portfolio quality
Application and processing of mortgages are digital, offers are decided upon
automated checks using AI/NLP (subject to formal document checks)
The mortgage sales process now takes place 24/7 – either by the customer
alone or resorting to the help of bank advisor Ensure stable Optimise process
First focus was on broker-related sales channel only; other sales channels to be IT applications and STP rate
added soon to allow the customer to change channels during the application
process
12A German Mortgage Bank is using advanced analytics for micro-segmentation and
2
efficient product offering
Use Case – Micro segmentation (1/2) Client Example
Customer situation Value delivered by AI/AA
Campaigns and their design typically based on intuition and experience
Clients addressed based on broad-brush segments or not at all (especially in
case of broker-related business)
Without consideration of clients’ specific motivation, situation and need
Project challenges
Combination of several models and analyses is critical–analytical problem to
large, complex and heterogenous for single model
Clients’ consent required for evaluation and use of personal data
Approach
Use of micro segmentation to find meaningful, action-oriented clusters of clients
Combination of several variable like behavior, life situation, lifestyle, values
Aggregation of internal data (age, address, transactions…) and external data Approach based on client motivations enables
(socio-economic statistics, geo-data incl. geo milieus, …) effective and objective micro-clustering of clients
Cluster algorithms and unsupervised learning approaches Efficient selection of very specific actions per client
13When using the kNN algorithm, loan repayment predictions could be improved by a
3
factor of 51 – leading to optimized liquidity management and loan prizing
Use Case – AI in forecasting early/ unscheduled loan repayments (1/2) Client Example
Customer situation Value delivered by AI/AA
Currently, the forecasting of unscheduled loan repayments is done with a
constant estimator/ parameters without differentiation
Severe deviation from the real value (38.52%) at overall bank level for the test
portfolio
Project challenge
Building the required data foundation for the use if AI, i.e. collecting and
preparing: before after
– client reference data, loan product data and conditions (e.g. interest rates,
repayment schedule)
– account balance data, transactional data, especially loan related transactions Significantly reduced deviation from the real value at
overall bank level for the test portfolio (8.75%) by
Setting up the classification algorithms and neural networks AI-based forecasting of early/ unscheduled loan
repayments
Approach Therefore improved refinancing and liquidity
Using real repayment data fed back for improving the model planning as well as reduced interest rate risk
Clustering loans by using classification algorithms (kNN2) and neural networks Optimized loan pricing
1) Compared to the real prepayment amount of the contracts and the 2-factor model used by the financial institution;
2) kNN algorithm – k-nearest neighbors algorithm (supervised machine learning).
14Bonus: Customer Approach – in Scandinavia, we are exploring the potential of a mobile app
with integrated market and customer analysis in the background
zeb APP – AI Car-Scanner
CAR DATA • Frictionless service
• Earlier entry into the customer journey
Upload SAVINGS PLAN
• Especially the young generation expects
Model
Credit: 5.500 € CONFIRMATION digital offers in the area of consumer credit
A-3
Monthly rate:
Brand • The consumer credit market is under strong
Audi 700,- € growth
Year Duration: • Consumer loans have a higher profitability
2018
8 months
rate than classic large loans
Total: 5.900 € Rate: 5,0 %
Fields are Fuel
filled Successful!
automatically Diesel • Process costs of consumer loans decrease
via AI image
analysis! Duration: A non-binding request with the
Gearbox
credit key data will be sent to • Cross selling effects
Automatic your bank team.
• Increasing Degree of automatization
We will get back to you as soon
NEXT
as possible! • Customer loyalty and marketing
Max rate:
• Competitive market environment among
others due to Fintechs
SEND
• Effort to win the customer's favor becomes
harder
• Strong competition consumer financeYou can also read
