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Deutsche BankDeutsche BankPrivate & Commercial Bank

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LendIt Europe 2017, LondonOctober 09

Fireside chat – Implementing Artificial Intelligence in Financial Services: Case StudyRoberto Mancone, Managing Director Deutsche BankGlobal Head of Disruptive Technologies and Solutions

Francesco Brenna, Executive PartnerIBM Global Business Services

1Deutsche BankPrivate & Commercial Bank

LendIt Europe 2017October 09, 2017

AI is ESSENTIALto transform

Advisory

Leading Banks are

embracing AI NOW

AI powered Advisory

solutions are REAL



Several players in the financial services industry are taking big leaps forward

Monitoring of traders’ phone calls to detect fraud

Automated review of loan contracts

Machine learning underwriting solution for credit decisions on thin-file borrowers

&

Predicting impact of events such as natural disasters on market prices

Language processing to analyze analysts’ reports

Over the last years most banks were

in the early stages of adapting

Artificial Intelligence.

Today, leading banks understand Artificial

Intelligence as a competitive advantage and invest significant

amounts

Examples from a Economist article

Source: The Economist (May 2017)



We look at AI as Augmented Intelligence as a mean to scale and elevate expertise

Augment your ExperienceDigitize your Knowledge +HUMANS

MACHINESOR>

Average Humans

+Machines

+ Strong

Processes

StrongHumans

+Machines

+ Weak

Processes

>

REASON

UNDERSTAND

LEARN



Advisor

Sca

labi

lity

Consistency and Personalization

Advisor + AIWork with relevant and actionable Insights

Respond to client needs with an holistic proposition based on evidences

Leverage the best available expertise and knowledge

Toward a scalable, consistent and personalized Advice

Overwhelmed by Data and Insights

Challenged by knowledgeable clients with different personal needs

Mainly focused on own experience and expertise



Artificial Intelligence builds upon data applications by further reducing reliance on human decision making and support

Industrial robotsE.g. Amazon

Human (Big data)(Decisions based on interpreting processed big data)

Machine (AI)(Integrated big data processing and

decision making)

Low

High

Complexity of data and interpretation requirements

Decision making

AI-enabled tradingE.g. Aidiya

High frequency tradingE.g. Virtu, Citadel

Credit evaluation and lending

E.g. Lending Club

Robo-advisoryE.g. Wealthfront

Medical applicationsE.g. IBM Watson

Fraud detection and compliance use cases

E.g. MasterCard

Internet-of-things applications

E.g. GE

Natural language processing

E.g. Kensho, Sentifi

Game applicationsE.g. Google (AlphaGo), IBM

(DeepBlue)

Trend forecasting E.g. Orbital

Insights

Self-driving carsE.g. Google

Sales, advisory andrelationship mgmt.

E.g. IBM Watson

Finance applications Non-finance applications

Example applications for big data and artificial intelligence



Handling of large sets of unstructured data not possible with legacy IT – IBM Watson and it’s breed can overcome this problem

ANALYTICS

Statistical and mathematical

analysis

OLAP

Multi-dimensional data

tables

BUSINESS INTELLIGENCE

Data driven decisions and reporting tools

BIG DATA & MACHINE LEARNING

Large, unstructured data; autonomously

analyzed

BIG DATA

Large, semi-structured data

1997 2000 2010 PRESENT NEAR FUTURE



Use cases in AI typically cluster around three interaction counterparties

Use case counterparty Role of AI systemPotential use cases (illustrative, not exhaustive)

Direct client experienceEnable direct interaction between client and AI system, e.g. for small advisory tasks

§ Self-directed user advice § Q&A guided KYC

Client advisor productivity

Free client advisor resources and enhance advisory quality, e.g. in client meetings preparation

§ Intelligent advisor tool to free client advisor from preparation work

§ Automate complaint processing to relieve client advisor

§ Guide internal advisor services hotline

Back-/ middle-office efficiency and en-hanced decisioning

Automate manual processes with multiple stakeholders, e.g. in client onboarding

§ Financial crime transaction monitoring with machine learning feedback loops

§ Trader surveillance to improve conduct-related controls with machine learning file analysis

§ Automated handling of mis-selling claims

Use cases critically dependent on acceptance within organization – unguided, direct, client interaction usually requires stronger internal acceptance



PCB activities to research AI sphere

Deutsche Bank Private & Commercial Bank has started to invest in Artificial Intelligence knowledge early

June 2015§ Cognitive value assessments for

commercial clients and private clients advisory

§ Design architecture phase§ Several workshops, brainstorming

sessions and conferences

2016 § Scouting and assessment of AI

technology providers in the market§ Joint evaluation across DB functions § Refinement of pilot use cases

July 2017 § Implementation of first three use

cases § MVPs in client advisory and in

Operations§ Parallel kick-off of a cross-divisional

Center of Competence

Discovery phase

Assessment phase

Implementation phase

6 months

12 months

6 months



Building on IBM Watson, we currently focus on three distinct value-adding use cases

Use Case 1:Privatkundenberatung der

Zukunft (PKBZ)

Use Case 2:VertriebsService Line (VSL)

Use Case 3:Complaint Management

Goal:Use AI technology to turn a process

driven customer journey into personalized, adaptive customer

experience

Goal:Use AI technology to manage

internal knowledge requests from relationship managers

Goal:Use AI technology to analyze

incoming written complaints (letters and fax) classifying and highlighting

relevant paragraphs and draft answers

Screenshots neu



PKBZ – Exemplary support by cognitive assistant

A personalized cognitive assistant guides clients through comprehensive online advisory process

Use Case 1 – PKBZ

Goals§ Create an end user driven instead of

process driven interaction§ Transform the advisory process into a

unique and seamless experience for end users

§ Leverage insights from external and internal data for personalized advice

What does it do§ Cognitive assistant with context-

sensitive chatbot functionality that guides users through online advisory process

Higher completion rates

Higher client satisfaction

Higher sales conversion

Benefits



VSL – Illustration of planned solution

AI technology acts as central RM interaction point for internal requests and answers most frequently asked questions

Use Case 2 – VSL

Goals

§ Create a cognitive assistant who provides answers to common questions

§ Reduce traffic of internal hotline§ Speed up the response time for

standard requests

What does it do

§ Chat-based cognitive assistant aims to provide specific answers to requests

§ If no satisfactory answer is available, cognitive assistant prepares search results for RM and transfers to human

ScalableNatural

Language Processing VSL employee

Cognitive assistant provides specific answer

Cognitive assistant shows cognitive search results

1st level of Interaction 2nd level of Interaction

If question remains unanswered

+ Summary of chat

+ Recommendation for solving issue

1

2?

Central entry point

RM (PeB/RBC)

Client3

Faster and better res-ponses to RM requests

VSL employee timefreed-up

RM time freed-up for servicing clients

Benefits



Complaint Management – Illustration of planned solution

Use AI technology to classify and draft answers for incoming complaints

Use Case 3 – Complaint Management

Agent

OCRLetter

Fax

Clientcomplains

§ Create (almost) automatic drafts for customer complaints

§ Reduce effort of handling customer complaints

§ Speed up complaint response time

§ Intent identification and classification of incoming complaints (letters and fax) by semantic text analysis

§ Automatic suggestion of text blocks for quick draft of response by agent

Complaint Classification

Highlight relevant phrases

Draft answer / suggest text blocks

Validates draft & finalizes response

Watson Watson Watson

Goals What does it doTime of back office agents freed-up

Faster and better respon-ses to client complaints

Benefits



Lessons learned in AI from leading financial services institutions

Lessons learned: Importance of setting up a functional organizational governance and of embracing strong planning

New technology needs internal buy-in and acceptance─ At top-management level: To ensure high priority on technology roadmap for implementation─ At business function / client advisor level: To ensure actual usage of new technology─ Business is more likely to accept use cases that relieve work from internal resources or that guide decisions

than to accept use cases that take over parts of client advisory and that put jobs at risk

Initial Proof of Concept (PoC) phase makes up leeway at later implementation stages─ Get time to understand benefits of new technology to generate acceptance─ Get time to figure out smooth internal governance and responsibilities of implementation─ AI is a scalable technology that allows for module-based add-ons on demand

AI systems are only as strong as they have been trained for─ Time-consuming (multiple months to years)─ Training needs real and diverse data that is suitable, i.e. understandable─ Even after training, AI will not necessarily give “black-or-white-answers”

Centralizing internal AI competence allows for oversight and orchestration of AI activity─ Avoid multiple divisions to work on the same ideas by giving clear guidance



… will transform retail banksThe disruptive potential of AI …

Artificial Intelligence is a multi-year journey with the potential to disrupt the way we think banking today

The rise of powerful AI will

be either the best or the

worst thing ever to happen

to humanity. We do not

know which.– Stephen Hawking

Transformation of user / customer journey without human interface

Empowered relationship managerswith usage of unstructured data

Increased efficiency in operations, via self-managing systems

Automated processes to save time and effort on regulatory demand

Deutsche BankDeutsche BankPrivate & Commercial Bank

Thank You!Do you have questions?

LendIt Europe 2017, LondonOctober 09

Roberto Mancone, Managing Director Deutsche BankGlobal Head of Disruptive Technologies and Solutions

Francesco Brenna, Executive PartnerIBM Global Business Services

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