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June 2020
BIG DATA IN BANKING: OPPORTUNITIES, ISSUES AND PRIVACY
LOUIS LOIZOUCHAIRMAN, HELLENIC BANKERS ASSOCIATION-UKPARTNER, LOIZOU&CO
Agenda
Section I. What is Big Data? 02-04
Section II. Where and how to leverage Big Data in Banking? 05-16
Section III. Privacy and Other Issues 17-23
2
It is a familiar term, but what exactly is ”Big Data”?
3
BIG DATA IN BANKING |WHAT IS BIG DATA?
Big Data
MobileCloud
ComputingInternet of Things (IoT)
Customer
Engagement
Concept
• The collection and analysisof large volumes of existingor historic data - structuredand unstructured
• “Some call it the “new oil”,given its growing reputationas a valuable, largelyuntapped resource”1
Market Size
• Global revenues is estimatedto reach $260 billion by2022. A CAGR of 11.9%between FY17-22
• The banking sector is set tobe a principal driver of thisgrowth 2
Source: 1- International Banker - Banking and Big Data: The Perfect Match? Oct 201
2 - International Data Corporation (IDC)
3 – IBM 2015
Development
• Growth of mobile apps and
connected devices (IoT) has
increased the amount of
information available
• Cloud computing has made it
cheaper to use powerful big
data analytics
1
2
Sheer Volume
• Every day we create 2.5
quintillion bytes of data, 90
% of which was created in
the last two years alone”3
• “Social media accounts for
27% of the data used in
banking & financial markets 3
The breakdown of Big Data
4
BIG DATA IN BANKING |THE 5 V’S
Source:- Bird&Bird: What's the big deal? Big data in the financial services sector
Volume
• From tweets to videos, from emails
to online purchases – the amount
of data being created is enormous
(terabytes, files)
Value
Trends
Products
VelocityNear time
Real time
Streams
VeracityReliability
Open Sources
VarietyStructured, semi-
structured to unstructured Volume
Terabytes
Records
Social MediaVariety
• Structured (spreadsheets), semi-
structured (xml, csv files) to
unstructured (social media, photos,
call centers) and the latter is the
typed of data that cannot be easily
transferred to spreadsheets
Veracity
• Reliability of data might be
unknown – third party or ”open
source data”
Velocity
• Data is frequently updated and
analyzed in real time – in batches
Value
• By predicting trends, institutions
(eg banks) can create value for
customers by offering tailored
products
Agenda
Section I. What is Big Data? 02-04
Section II. Where and how to leverage Big Data in Banking? 05-16
Section III. Privacy and Other Issues 17-23
5
Where and how to leverage Big Data in Banking?
6
BIG DATA IN BANKING |OPPORTUNITIES
Source: Loizou&Co analysis
Client Segmentation
NeobanksFrom data to
insights
Cross-selling
Dashboard
Customer
experienceSmell test
Profiling
Client Segmentation and Profiling
From understanding behavioural patters to which products have been rejected in the past, customer segmentation with Big Data provides the required granularity to truly understand customer needs and value - taking CX to a new level
BIG DATA IN BANKING |OPPORTUNITIES
1- Accenture Report: Put your Trust in Hyper-Relevance - 2017
2 – Galllup: Bank Customers: Are Channel Experiences All That Matter? 2016
Source: Loizou&Co analysis
Customer Experience
Data Analysis
Customer Segmentation
Product Offering
Automatic savings and
investment products
based on spending habits
Cheaper car insurance
based on driving patterns
Better premium on health and life
insurance based on eating habits
and gym visits
International Payments , Extra
miles, Trade Finance – its about
knowing your customer
• By knowing where and how often you client shops(app’s), to where they like to eat, to which productsthey have rejected in the past, if they have received apromotion or started a family, the opportunities totailor the ultimate client experience is within reachingand a necessity for the banking sector
33%customers who abandoned a businessrelationship in 2016 did so because of a lack of personalization1
73%
Of CEO’s acknowledge thatthey need products andservices that are moremeaningful to customers1
56%
of the households surveyedsaid that the offer receivedfrom their bank was for aproduct they already owned 2
60-70%Is the rate that businessesmore likely to sell toexisting customers thanthey are to prospects –upselling has been madeeasier by Big Data2
Cross & Upselling From Data to insights
From data to insights with the potential to achieve cross-selling and upselling
8
BIG DATA IN BANKING |OPPORTUNITIES
Source: Loizou&Co analysis
1 - NGData, Finextra and Clear2Pay
76%
71%
55%
Of banks said that the keybusiness driver forembracing big data is toenhance customerengagement, retention andloyalty 1
Of banks said that toincrease their revenue theyneed to better understandcustomers and Big Data willhelp 1
Of banks said that having areal-time view of dataprovides a significantcompetitive advantage andbelieve that batch modedata is ineffective 1
5- 10xThe cost of acquiring newretail, small business orcommercial customers Vsthe cost of retaining anexisting one2
50-100% +Is the average amount spentof a repeat customer whencomparing to a new one 1
2- Finextra - 9 Keys to Bank Cross-Selling Success
Behavioral Patters
Customer-focused programs(vs product-driven) thatevaluate each customer'stransactional, productownership and evenbehavioural characteristics ona monthly basis
The key is to understand patterns. Is the client travelling but is he/she not using
miles? Have they received a large amount of cash? Does the client owns a home? Does
he/she have enough capital to pay for a mortgage?
20%
Is the estimate madeby McKinsey as to howmuch banks can saveon marketing if BigData is used fordecision making
Digitalising a traditional bank does not “create” a neobank
9
Important Considerations
BIG DATA IN BANKING |OPPORTUNITIES
Yes or No?
If you digitalise a traditional bank
will that make it a neobank?
Will neobanks be in a position
to undercut traditional banks
indefinitely?
And why?
“culture eats strategy
for breakfast”
They would still fail
the “smell-test”
They would need to
turn profitable, but
their cost structure is
lower
No
Maybe
NeoBanks
Neobanks v traditional banks
10
BIG DATA IN BANKING | SMELL-TEST
Source: Loizou&Co analysis
Smell Test
Neobanks have taken full advantage of the application programming
interface (API) and the access to data (identity, credit history and income)
to bypass laborious customers applications. They have completely
automated the baking experience, from start to finish – maximizing
customers convenience and experience
Customer Centric Experience
Processes re-designed from a customer perspective
Automated Products
& Services
Fully automated
products and services,
providing ease of access
as well as convenience
Simplified
Operations
Digital core with no
front, back or middle
office, minimizing fees
Agile Thinking/ Organization
Flat organization with innovation
at it’s core & entrepreneurial
thinking
Flashy Marketing &
Efficient
communication
Interactive marketing
through social media,
cheap customer
acquisition through
referrals
UK is leading the development of the neobank business model. Globally.
11
▪ UK
– Atom Bank– Monzo Bank– Revolut– Tide– Monese– Starling Bank– Civilised Bank– Ffrees– Lintel– Loot
BIG DATA IN BANKING | UK LEADERSHIP – Monese– Monizo– Osper– Pockit– Secco Bank– Tandem– Tide– Shawbrook Bank
▪ Sweden
– Qapital
▪ Russia
– Rocket Bank– InstaBank
▪ Poland
– mBank– Nest Bank
▪ Greece
– Viva Wallet
▪ Denmark
– Lunar Way
▪ Italy
– BuddyBank– Soldo
▪ Netherlands
– Bung
▪ Germany
– N26– Fidor Bank
▪ USA
– Aspiration– Empower– Marcus
▪ Spain
– ImaginBank
▪ France
– Compte Nickel– Hello Bank– Morning– Qonto– Soon– fortuneo– ING– Boursorama
▪ Cyprus
– Ancoria
Are you a “future-ready” bank? Neobanks do not “suffer” from holding legacy assets and running legacy systems in contrast to traditional banks
12
BIG DATA IN BANKING | FUTURE-READY BANKING
Legacy problems and legacy systems create a burden for traditional banks
neobankstraditional
banks
Smart
notifications
Quick overview of Key Performance Indicators
Analysis of each social media
channel
Automatically generated
reports on a daily basis on:
• Engagement
• Performance of marketing
campaigns
• Lead pipeline
• Demographic trends
• Analytics
Date range for analysis
Current marketing
campaigns
performance
Engagement
sources and effect
on new accounts
Sources of digital traffic and referral sources
Dashboard
Unless you transform data into insights, there is no further competitive advantage
13
BIG DATA IN BANKING | DATA INSIGHTS
Source: Loizou&Co analysis
Examples of how banks are leveraging Big Data
14
BIG DATA IN BANKING |OPPORTUNITIES
Source: Loizou&Co analysis
Ability to develop
customer profiles to
keep track of
transactional
behaviors on an
individualized level
When plugged into
business intelligence
tools with automated
analysis features and
predictive
capabilities, can
trigger red flags on
customer profiles
that are higher risk
than others
Enhanced Fraud Detection
Superior Risk Assessment
Increased Customer Retention
Product Personalization
With in-depth
customer profiles at
your fingertips, it’s
easier to build
stronger, longer-
lasting customer
relationships that
drive customer
retention
Demonstrate your
commitment to
understanding each
individual customer
by developing
products, services,
and other offerings
tailored to their
specific needs based
on their existing
customer profiles
Streamlined Customer Feedback Stay up to speed on
customer questions,
comments, and
concerns by using
big data to sort
through feedback
and respond in a
timely manner
Workplace Improvement Create an environment that your employees
look forward to working in by using big data to
monitor performance metrics, assess
employee feedback and company culture, and
gauge overall employee satisfaction
15
Source: Loizou & Co based on publicly available sources.
BIG DATA IN BANKING | OPPORTUNITIES | NEOBANKS
Benchmarking (FY19) – Sorted by Total Revenues
Note: latest available information.
Bank LocationCountries of
PresenceLicense Customers (000’) Revenue (€m)
Funding (€m)
Deposits(€m)
Loans(€m)
Assets(€m)
Equity(€m)
Net Profit(€m)
Cost/ IncomeOperating Expense/
Customer (€)
Mbank PL 1 Yes 5,604 1,200.0 n.a. 27 23 33 4 0.2 0.4 0.1
Klarna SWE 9 Yes 85,000 807.1 873 1,167 2,817 3,787 503 (85.7) 0.9 7.1
NU Bank BRA 3 Yes 22,000 378.0 926 n.a. n.a. n.a. n.a. (56.3) n.a. n.a.
N26 GE 23 Yes 5,000 334.6 638 399 197 429 n.a. (35.3) 3 n.a. n.a.
Chime US 1 Yes 6,500 222.0 880 10,003 n.a. n.a. n.a. n.a. n.a. n.a.
Revolut UK 31 Yes 10,000 195.6 776 9,023 n.m. 12,323 217 (36.7) 3 n/a 9.0
OakNorth UK 2 Yes 144 115.4 997 2,206 2,290 3,030 522 55.5 0.3 374.6
Bank Mobile US 1 Yes 2,600 80.3 164 n.a. n.a. n.a. n.a. n.a. n.a. n.a.
Monzo UK 1 Yes 3,500 40.0 348 517 18 688 130 (52.9) 3 650%3 41.9
Atom UK 1 Yes 65 36.5 476 19,843 26,883 31,343 237 (89.8) 3 n.m. 747.5
Insta Bank NOR 3 Yes 6 22.3 n.a. 262 248 326 52 3.8 0.5 n.a
Soldo UK 7 Yes 60 6.7 78 n.a. n.a. 21 n.a. (8.1) n.a. 149.1
Starling UK 1 Yes 1,000 6.7 355 228 10 263 31 (30) 3 3,580%3 79.7
Hello Bank (BNP group) FR 5 Yes 2,900 n.a. 80 n.a. n.a. n.a. n.a. n.a. n.a. n.a.
Boursorama banque FR 4 Yes 1,700 n.a. n.a. n.a. n.a. 14,968 n.a. (28.2) 3 n.a. n.a.
Compte Nickel (BNP group) FR 1 Yes 1,200 n.a. 209 n.a. n.a. n.a. n.a. n.a. n.a. n.a.
Orange Bank FR 3 Yes 500 n.a. n.a. n.a. n.a. 5,296 n.a. (169.8) n.a. n.a.
Fidor Bank GE 1 Yes 310 n.a. n.a. n.a. n.a. 14,713 n.a. n.a. n.a. 0.0
Monabanq FR 1 Yes 310 n.a. n.a. n.a. n.a. 476 n.a. (9.1) 3 n.a. n.a.
Lunar Way DM 3 Yes 130 n.a. 47 n.a. n.a. 4 n.a. (3.6) 3 n.a. 11.2
Anytime BE 2 Yes 100 n.a. 8 n.a. n.a. n.a. n.a. n.a. n.a. n.a.
Qonto FR 4 Yes 65 n.a. 137 n.a. n.a. n.a. n.a. n.a. n.a. n.a.
Bunq NL 30 Yes 1,100 - 45 211 n.a. 231 n.a. (11.1) n.a. n.a.
Xinja AUS 1 Yes 25 - 385 n.a. n.a. n.a. n.a. n.a. n.a. n.a.
Leading neobanks across Europe and the UK have been attracting millions of customers, while the majority are still loss-making
Source: public disclosure. Notes: estimates by Loizou & Co.
Traditional banks/insurers invested c. EUR 1.3bn in standalone neobanks to attract digital savvy customers in Europe; and without cannibalising fee income in their traditional business model
BIG DATA IN BANKING | OPPORTUNITIES | NEOBANKS
▪ Total neobank funding since 2013 coming from VC and PE funds
amounted to €5.9bn
▪ Early VC investment (VC round + Series A, B & C) represented the
great majority of investments with 54.4% for a total of €3.2m
▪ Due to the nature of VC investments, PE firms only represented
3.9% of the total invested amount
✓ Eg: Blackrock participated in the €430m VC round of Klarna in 2018
0.6%
54.4%41.1%
3.9%
Seed/Angel Early VC Late VC PE
Total Funding (%)
Relevant investments – Traditional Banks
Bank NeoBank
Relevant investments – Specialized Funds
Fund NeoBank
16
Agenda
Section I. What is Big Data? 02-04
Section II. Where and how to leverage Big Data in Banking? 05-16
Section III. Privacy and Other Issues 17-23
17
The major Big Data challenges in banking
18
BIG DATA IN BANKING |RISKS
Source: Loizou&Co analysis
Legacy SystemsThe banking sector has always been relatively slow to innovate (92 of the top 100 world leading banks still rely on IBMmainframes1), one of the key reasons for the high fintech adoption as legacy system cant cope with the amount of new data
01
02
03
1- IBM
2 – ISACA International: 5 Alarming Cyber Threat Statistics: How Vulnerable Is Your Business
3.- Cyber Security Venture’s Cybercrime Report.
The bigger the data, the higher the risk “With great power comes great responsibility” – banks need to ensure that the data they collect is keptsafe. However, only 38% of global organizations are able to handle cyberthreats2. New regulations such asthe GDPR has place some restrictions on business gathering of data
Big data is getting too bigWith so many different types of data (structure to unstructured), its no surprise businessare struggling to cope. The task its even harder when trying to separate the useful data
04
Financial damage$6 trillion – that’s the estimated annual cost of crime damages from2021 according to Cyber Security Venture’s Cybercrime Report.Internal actors were responsible for 25% of those damages 3
A shift in paradigm – From trusting the banks with our money to trusting them with our data (or not)
19
BIG DATA IN BANKING | PRIVACY
Source: Loizou&Co analysis
Trust
of the 31,000+ respondents from around the globe
said that they trust financial services VS 77% who
said the same of technology companies2
50%
87%Is the accuracy rate of identifying someone by only
using their birthday, gender and postcode - which
could lead to harmful exposure of personal data1
The year when the European Banking Authority
warned that the integrity of the financial sector could
be at stake if insecure data use eroded trust3
2017
Of Europeans feel like they don’t control their data 81%
Of Europeans feel like that firms may use their data
to purposes other than those advertised 69%
Access
Are you happy to share your social media and personal data?
▪ Facebook status and facial expressions could predict creditworthiness and
impact access to credit 4
▪ The tone of your voice is also being studied for creditworthiness risk3
▪ The education level of your social media friends can reveal how likely you
are to repay your loan
▪ What if you are locked out of a health insurance because your Google
search history includes “ doughnut shops 3”?
▪ Would you allow Tesco to access your data for your loyalty card? And for
your health insurance? Many found it creepy for the latter, according to
EY5
▪ Amazon now sells loans, Alibaba has a payment system and Facebook has
patented a credit-rating system. Regulators should be just as worry about
non-traditional financers and fintech start-ups
4- Fair Isaac Corporation (FICO) – America’s main credit scorer
5 – EY: Big Data and Analytics
1 – Deustche Bank: Big Data, How can it become a differentiator
2- Fujitsu: Banking on privacy: Data security and trust in financial services
3- The Economist: Big data, financial services and privacy
What do consumers say when it comes to the financial services industry’s ability to use their data for their benefit?
20
Source: Market Research.
Respondents whether data by financial services firms is used for the benefit of the customer
BIG DATA IN BANKING | PRIVACY
companies know who they are and can quickly and easily access their
account information when they call, 34%
disagree with the suggestion that they might receive more
targeted and personalised offers and services, 38%
there is little consistency of user experience when
dealing with those companies across
different channels, 29%
their personal information was represented
incorrectly on official communications from these businesses, 23%
CRM issues?
Profiling and
segmentation?
Personalisation?
34% only agrees that
financial firms are using
data in customer’s benefit23%
34%
38%
29%
How UK banks are using AI and machine learning to improve compliance and reduce their risks
21
BIG DATA IN BANKING | PRIVACY
Source: Loizou&Co analysis
Anti- Fraud
▪ One of the core uses for machine learning in the banking worldhas been to combat fraud and improve compliance
▪ The technology is ideally suited to the problem as machinelearning algorithms can comb through huge transactional datasets to spot unusual behaviour
▪ Douglas Flint, chairman of HSBC said at the inaugural InternationalFintech Conference in April 2017: "Using AI and machine learningto police the financial system is creating opportunities to dothings better, to protect customers and ourselves”
▪ When you know about [fraud] now, something can be done aboutit," Andrew McCall, chief engineer for big data at Lloyds BankingGroup said. "If you know about something that happenedyesterday, it is not as effective as an anti-fraud mechanism
Anti-money laundering
▪ HSBC has been using Google Cloud machine learning capabilities for anti-money laundering since 2017. Five years after receiving a £1.2bn in 2012for failing to adhere to stricker controls
▪ The CIO at HSBC Darryl West said the bank is using machine learning to run"analytics over this huge dataset with great compute capability to identifypatterns in the data to bring out what looks like nefarious activity withinour customer base. The patterns that we identify are then escalated to theagencies and we work with them to track down the bad guys”
▪ Startup Quantexa has been working with HSBC to help the bank spotpotential money laundering activity and it is now integrating its technologyinto the bank’s system
▪ ComplyAdvantage - another UK-based start-up that has partnered withSantander, BBVA, Holvi and Robinhood to show how AI is ripe forapplication to study large amounts of data and tracking money laundering
Source: Computer World: HSBC turns to Google Cloud for analytics and machine learning capabilities 2017
Computer World : How UK banks are looking to use AI and machine learning
What benefits could Big Data bring to consumers?
22
BIG DATA IN BANKING | WHAT THE REGULATORS SAYS?
Source: Loizou&Co analysis
Financial Services that Help you• Tailored services - your
insurance company can warnyou that your current policydoesn’t cover the parachutejump that you haveannounced on social media
Better Fraud Protection• Big Data can allow your bank to know
where you are located in order toprevent a fraudulent electronicpayment happening in another country
Improved Access to Financial Services
• Big Data could help a young couplewithout sufficient credit history obtainloans. Likewise, young, inexperiencedrivers could install thematic devices intheir car and have the insurancecompany monitor their driving habits
What does the regulator say?
23
BIG DATA IN BANKING | WHAT THE REGULATORS SAYS?
Source: Loizou&Co analysis
TEXT
How to protect your rights Risk of Big Data in Financial Services
Contact
If it doesn’t
feel right
Giving consent
to share data
Control your
information
and privacy
▪ You control what type of information is to beshared – including on social media
▪ Check the privacy and data protection
▪ Only do so if you are comfortable with theprovider and how the information will be used
▪ If in doubt, request clarification
▪ Use your right to object to the processing ofyour data for marketing purposes. This couldstop unwelcome/ aggressive advertisements
▪ Submit your complaint to the respectiveservice provider
▪ Or to your national complaints handling bodyand/or your national data protectionauthority
Limited
Scope
Targeted
Offers
Risk
profiles
It can contain
errors
▪ The tracking of the movements can bemisleading and affect access to loans
▪ A health care professional on a night shiftcould be incorrectly interpreted as anindication of an unhealthy lifestyle
▪ Big Data will share your location and as aresult owners of homes in flood-prone areasmay have additional difficulties to get homeinsurance coverage
▪ Financial service providers can use theirincreased knowledge about you to sendtargeted offers, which could result in youbuying services that you do not really need
▪ Big Data can lead to highly tailored financialproducts and services which may potentiallymake it more difficult for you to compareproducts and decide which one suits youbetter
A number of rules have been established to reduce these risks and aim to protect the end customer
24
BIG DATA IN BANKING | WHAT THE REGULATORS SAYS?
Source: Loizou&Co analysis
02
03
04
01
The processing of your data requires a clear, specific consent from the user
Financial service providers are obliged to ensure that the information presented on their products is clear and not misleading
Financial service providers are obliged to act honestly and fairly when using Big Data to create services and products or when using it to offer you a product
Financial service providers have to take strict security measures to protect your data from hackers and other cyber threats
Contact
BIG DATA IN BANKING | PRESENTER
Louis Loizou
Email: [email protected]
Landline: +44 (0)20 3971 2314
25