Artificial Intelligence: a driver of innovation in the Banking Sector

Consorzio ABI Lab – Centro di Ricerca e Innovazione per la Banca
www.abilab.it
ARTIFICIAL INTELLIGENCE IN BANKS
Marco Rotoloni, Senior Research Analyst ABI Lab
> European Big Data Value Forum 2019<
Helsinki, 15 October 2019

ABI Lab – Centro di Ricerca e Innovazione per la Banca
About ABI Lab
ABI Lab is the Banking Research and Innovation Center
founded by ABI in 2002 with the aim to foster the dialogue
between banks and companies.
We are a Consortium, which today includes 124 Banks and
70 Companies.
Our mission is to foster, develop and spread innovation
in the banking and financial sector through research
activities and knowledge sharing.
Some numbers:
• 5,782 users
• 63,738 visits
• 43.590 visitors
• 6,930 documents
• over 70,000 document downloads
• 20 workgroup, 85 meetings
• 11 Research activity reports

AI: why now?
AI has always had a strong appeal in the international scientific community and has been a field of
study on which a lively research activity has always been developed.
• Banks are facing a digital transformation
path
• The data are increasingly open and are
emerging data company models
• A wider level of openness will lead to an
higher level of competition and
internationalisation.
• It becomes more and more important “to
make culture”
• Share within the bank the main concepts to
be considered in AI
• Implement AI systems to support banking
and business operations
• Create governance tools of the technology
The theme is currently experiencing a hype, justified by:
• A huge amount of data: greater openness and greater access to
quality data;
• Increased computational capacity: it allows to make the most of
the potential of algorithms. IT architectures are constantly
evolving and new architectural paradigms are emerging.
Why now?
Why the Banking sector? What are the needs?
2

AI scenario in Italian Banking Sector
Source:ABILab,SurveyonICTprioritiesofItalianbanks,March2019,22
banks/bankinggroupsand4interbankoutsourcers
Application areas: activated initiatives State of development
3

The Report comes from the need to have a tool for sharing knowledge towards the different levels of
management within banks
1. Raise awareness about the opportunities of AI
2. Identify a common glossary
3. Analyse possible areas of application for the banking sector
4. List and describe the main open points
5. Compare results that have been measured and improvements
achievable, enable reflections on organizational and process
impacts
Objectives Structure of the Report
The Report is divided into 3 main sections:
1. Introductory chapter describing AI and
related technologies
2. Description of use cases in banking
3. Impact of AI on compliance, Data
Governance and Ethics
ABI Lab Report on AI in banking
A group of experts from the following banks and companies contributed to the Report
4

Our guide to understand AI
AI can be broken down into 3 logical levels of understanding. Enabling technologies are
associated at each level.
Techniques that enable AI
operation
Classes of
problems
Case studies
Examples
Machine learning
Evolutionary
algorithms
Expert Systems
NLU
Computer Vision
Pattern Recognition
Classification
Semantic engine
Chatbot
Robo Advisor
Technologies
General problems that can be addressed
by some specific AI techniques (or a
combination of available techniques)
AI implementation, to develop new
festures, imrove operational practices or
address a specific business initiative
Levels
Algorithms that allow machines to
perform functions typical of human
behaviour
5

Some Experiences in the Banking sector
Help Desk 2.0
Screening CV
Virtual Assistant for
Cards
AML Control
Audit assessment
Help Desk 2.0
Help Desk 2.0
Ticket
Remedy Classifier
Mailroom
IARI (IA for Internal
Review)
The main applications within the banking environment are divided into two main macro-areas:
2. Operational Support1. Assistance
6

Case studies in the Banking sector –
Lesson Learned
Among the different applications in banking it is however possible to identify common and recurrent patterns in
the macro areas of benefits, points of attention and KPIs used:
• Standardization and improvement of the
quality of questions and answers
• Average response time reduction
• Errors reduction
• FTE reduction
• Cultural aspect
• Regulatory aspect
• Interactions managed automatically /
total interactions
• Reduction of calls = trend of incoming
calls
• Efficiency of the process
• Increased effectiveness
• More control
• FTE Reduction
POINTSOF
ATTENTION
EXAMPLESOF
KPI
BENEFITS
• Cultural aspect
• Regulatory aspect
• Continuous training
• Tickets wrongly classified / total tickets
• Correctly addressed mail / total mail
2. Operational Support1. Assistance
7

• What will be the impact of AI on business
processes?
• Will it be necessary to develop a risk mapping
in order to be able to govern them?
• Which impact will technology have on data
governance?
• What skills will be needed to exploit and
manage Artificial Intelligence?
• Will new jobs and new roles be created within
the labour market?
• Which impact will technology have on the
employment level?
To properly manage the technology, companies
should focus on three main points
PROJECT MANAGEMENT1
Points of attention of Artificial Intelligence
• Which ethical aspects should be taken into
account?
• What has been done in the area of regulation?
• Are there problems with compliance or
transparency?
ORGANIZATION, ROLES AND CULTURE2
CHALLENGES3
8

RISK MANAGEMENT
• Risk mapping (i.e. operational risks) and think
on new controls points
• AI could mitigate risks (reducing human
mistakes or strengthening controls)
Project Management and Risk Management1
9
Pilot Projects
Building an AI team
by strengthening
internal skills
AI training and
culture
Definition and
development of
an AI strategy
and a roadmap
Internal and external
communication plan
1
2
3
4
5
*Source:AndrewYan-TakNg–AIProjectPhases
PROJECT MANAGEMENT* DATA GOVERNANCE
PROCESS STREAMLINING
• Data governance (DG) and AI
could be seen as
complementary topics
• Data preparation as a core part
of AI implementation (rethink
DG scope and processes)
• New DG competences arising
(data scientist, data steward,
data scouting and engineer)
• It’s hard to implement AI
without rethinking processes
• It’s important an inclusive
approach throughout the
organization

Cultural impact on Work
As in the past, a new technology such as AI will have a significant impact on people and their
occupations
Artificial Intelligence can be seen more as an Augmented Intelligence
that increases the tools available to people and the level of results,
resulting from a greater capacity for analysis and processing.
To correctly assess the impacts of AI it will be important to make some considerations on the
technology and the consequences deriving from its use:
1.
Positive impact on employment
AI will increase (and not replace) the
areas of employment, facilitating
the emergence of new economies,
new trades and new disciplines.
2.
New ways of working
It is important to emphasize the not
full substitutability between man
and machine: the technology will act
in support of the man, that will
consequently have to modify the
working modalities.
3.
New skills and competences
Need for a continuous updating of
skills (technical and management
skills) and a willingness to change,
which results in the intention to
create new organizational units
(centers of competence) between
business and IT.
2
10

To be evaluated
and continuously
addressed
throughout the
life cycle of the
AI system
1
2
3
45
6
7
The European Commission’s vision, in support of an ethical and reliable AI, is based on three fundamental pillars:
increased investment, openness to change and a clear regulatory framework.
Chapter II of the report* describes a list of requirements concerning:
Including fundamental rights, human agency and human oversight
Including resilience to attack and
security, fall back plan and general
safety, accuracy, reliability and
reproducibility
Including respect for privacy,
quality and integrity of data,
and access to data
Including traceability, explainability and
communication
Including the avoidance of unfair bias, accessibility
and universal design, and stakeholder participation
Including sustainability and
environmental friendliness,
social impact, society and
democracy
Including auditability,
minimisation and reporting
of negative impact, trade-
offs and redress.
Societal and environmental
wellbeing
Diversity, non-discrimination and
fairness
Accountability
Transparency
Privacy and Data Governance
Technical robustness and safety
Human agency and oversight
Ethical Challenges - The European
Commission’s Vision - Chapter II
3
*Source:EuropeanCommissionGuidelinesonAIEthics-ChapterII
11

(1) The protection of natural persons in relation to the processing of personal data
is a fundamental right.
Article 8(1) of the Charter of Fundamental Rights of the European Union (the
'Charter') and Article 16(1) of the Treaty on the Functioning of the European Union
(TFEU) provide that everyone has the right to the protection of personal data
concerning him or her.
Regulatory Challenges - GDPR - Recital 1,
Art. 22 and Art.25
Article 22
The data subject shall have the right not to be subject to a decision based solely on
automated processing, including profiling, which produces legal effects concerning
him or her or similarly significantly affects him or her.
The data controller shall implement suitable measures to safeguard the data
subject's rights and freedoms and legitimate interests, at least the right to obtain
human intervention on the part of the controller, to express his or her point of view
and to contest the decision.
Recital 1
Protection of
personal data
becomes a right
The controller shall, both at the time of the determination of the means for processing and at the time of the
processing itself, implement appropriate technical and organisational measures, such as pseudonymisation, which
are designed to implement data-protection principles, such as data minimisation, in an effective manner and to
integrate the necessary safeguards into the processing in order to meet the requirements of this Regulation and
protect the rights of data subjects. The controller shall implement appropriate technical and organisational
measures for ensuring that, by default, only personal data which are necessary for each specific purpose of the
processing are processed.
Article 25
12
3

The company will not be able to explain the results to its customers, auditors or compliance teams
when required
Very promising models are not still in production because companies cannot trust AI decisions that they
do not fully understand
A company also exposes itself to significant risk if it delegates responsibility to an AI system that is not
fully in line with its purposes and policies.
Two of the main challenges are trust and transparency. If AI is a black box that simply
takes data input and produces dark and unexplained results, then there is no way for
the company to judge whether these systems produce correct and accurate results.
This brings with it several critical issues:
As a solution to these critical issues, it is important:
• make the decisions explainable
• maintain a tracking of the inputs and outputs and reproduce the
processing at a distance of time
• control the life cycle of each AI asset, from initial design to training,
deployment to operational management.
The challenge of transparency
13
3

Among the key elements for the development of the AI HUB:
1
2
3
4
A synergy with the research world
An active role in dialogue with the European Institutions
Participation in European projects
Dialogue with international networks on AI
The restricted network of the AI HUB will benefit from a
structured interaction with the scientific and research world
(l’AIxAI)
Participation in EBF tables will also be strengthened with a
focus to dialogue with the European institutions
A strong presence on European projects will be maintained:
the ABI Lab Consortium has been invited to take part in the
European INFINITECH initiative
Opportunity to join as a member of the European Big Data
Value Association (BDVA)
14
Why an AI Hub?
06/11/2019

The structure of the ABI Lab AI Hub
1. Development of research activities on AI
2. Construction of positioning documents
and guidelines
3. Collection and analysis of case studies
4. Scouting of possible areas of
experimentation
5. Control of the regulatory scenario
Events & Workshop
A relational platform of reciprocal exchange
will be defined, in which the centers of
competence on the AI active with the banks,
banking experts, companies partner, research
centers and institutions will be able to
participate. Activities will develop along the
following lines:
Knowledge Base
Restricted
Research
Network
Enlarged
Network
15

Conclusions and future developments
An initial set of topics that should be developed in a structured approach on AI could be represented
by these five actions:
Regulatory
insights
Mapping of
technologies
and processes
Use Case
Development
Technological
landscape
Extension
Analysis of the
impacts in the
company
In addition to these five actions, there are three themes that need to be addressed in depth:
Transparency
Data and
compliance
Ethic
16

Artificial Intelligence: a driver of innovation in the Banking Sector

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