BIG DATA IN BANKING: OPPORTUNITIES, ISSUES AND PRIVACY · • The banking sector is set to be a...

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




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


1- Accenture Report: Put your Trust in Hyper-Relevance - 2017

2 – Galllup: Bank Customers: Are Channel Experiences All That Matter? 2016


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



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


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


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


Examples of how banks are leveraging Big Data

14



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




17

The major Big Data challenges in banking

18

BIG DATA IN BANKING |RISKS


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


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


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



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?


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



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



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

BIG DATA IN BANKING: OPPORTUNITIES, ISSUES AND PRIVACY · • The banking sector is set to be a...

Documents

Transcript of BIG DATA IN BANKING: OPPORTUNITIES, ISSUES AND PRIVACY · • The banking sector is set to be a...