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1 20170616 Automation in Financial Services_ADETEM.pptx

Automation in Financial Services

Laurent Doucet

Club Banque, Finance Assurance

Mardi 20 juin 2017

Ère de l’intelligence artificielle… et si la machine nous remplaçait ?

En partenariat avec :


Contents Page

This document shall be treated as confidential. It has been compiled for the exclusive, internal use by our client and is not complete without the underlying detail analyses and the oral presentation.

It may not be passed on and/or may not be made available to third parties without prior written consent from .

© Roland Berger

A. Overview 3

B. Use cases 11

C. Detailed case studies 22

D. How to move forward? 33


A. Overview


Software-driven automation has the potential to raise efficiency to the next level across industries

Major efficiency levers over time

Source: Press research; Roland Berger

Today artificial intelligence is where the internet was in 1996"

We are entering a new phase in world history – One in which fewer and fewer workers will be needed to produce the goods and services for the global population"

Business process automation

2015 2005 1995

Automation of business processes using software robots

Quellen:

"Today AI is …"

http://www.merantix.com/

http://raceagainstthemachine.com/

By Erik Brynjolfsson

(http://twitter.com/erikbryn) and Andrew

McAfee (http://twitter.com/amcafee)

From off-shore to no-shore

(http://www.opuscapita.com/blog/2015/medium-sized-companies-don%E2%80%99t-outsource-they-do-and-the-trend-goes-upward)

From offshore to no-shore"

Business process outsourcing/offshoring

Outsourcing of operations and responsibilities to service providers in countries with lower labor costs

Business process re-engineering/management

Analysis and design of business processes within an organization to reduce non-value-adding work

Overview A


Software-driven automation techniques

Automation platforms offer higher flexibility than traditional scripting – Emerging machine learning opens up a whole new world

A Overview

Source: Roland Berger

Automation technique

Tailored software and scripting

0 Robotic process automation/ Automation platforms

1 Artificial intelligence (machine learning)

2

Data characteristics

Structured (rigid) Structured or patterned Unstructured and unpatterned large data sets

Examples > Complex reports in SAP

> Tailored workflow tools

> Automation of IT operations/tickets

> Aggregation of data from multiple systems

> Recognition of security threats from deviation of normal behavior

> Self-driving cars learning from observing humans

Description > Scripts or tailored (enterprise) software to support a specific process or workflow

> Rigid processes and high programming/testing effort are typically required

> Tools and platforms that help to automate and orchestrate repetitive processes across existing systems

> Software interfaces or non-invasive approaches mimicking human behavior

> Advanced algorithms that can handle ambiguity – Self-learning replaces need for prescriptive rules

> Systems that adapt their behavior based on observing humans

Traditional approach Emerging technology, vast potential

Tools available, usage increasing

Flexibility Low Medium High

Implementation effort

High Low Medium

1/2 Deep dives presented in this document


RPA and automation platforms promise to automate repetitive tasks easily without the costs and limited flexibility of tailored software

Robotic Process Automation & Automation platforms

1) What You See Is What You Get

Details Use cases

Robotic process automation

> Moving files and folders

> Scrapping data from the web

> Extracting and reformatting data into reports and dashboards

> Merging data from multiple places

> Software that mimics human behavior at a computer, e.g., non-invasive software to automate repetitive tasks

> RPA either aims to replace human labor or assist a human worker to improve efficiency

> Very easy to set up and adjust making deployment feasible even for one-off tasks, e.g., WYSIWYG1) interfaces

> Dedicated systems that aim to make automation easy

> Scripting/orchestration across applications using one common platform/language

> Connected to existing (legacy) systems via software interfaces and APIs

> Continuous improvement approach with constant creation and adaption of scripts

> Monitoring, escalation, and analysis to support operations

Automation platforms

> Update meta data in cloud environments

> Deployment of virtual machines

> Auditing and reporting the health of IT stacks in real time

> Smart City systems (e.g., smart parting systems)

> Allocation of cores and RAM for simulations on supercomputers

Source: WorkFusion; Roland Berger

1 A

RPA / Automation platforms – Description and examples


RPA is expected to be one of the next major disruptions to come with sharp impact on existing way of doing business

Automation is threatening to replace swats of white-collar workers, much as mechanical robots have displaced blue-collar workers on assembly lines.

Wall street Journal

Robotic process automation will be the next big

disruptor.(…) Every organization will find the

combination that is right for it. But getting ahead of

this curve is paramount because RPA is here to stay.

Tanvir Khan, Dell

Anything that could give rise to smarter-than-human

intelligence — in the form of Artificial Intelligence,

brain-computer interfaces, or neuroscience-based

human intelligence enhancement — wins hands down

beyond contest as doing the most to change the

world. Nothing else is even in the same league."

Eliezer Yudkowsky, Co-Founder and Research

Fellow, Machine Intelligence Research Institute

Computer coded software that:

Walking, talking, independent robots, replacing humans in all their capabilities

What

it is What

it is not

Physical machine processing physical things

A software with artificial intelligence or voice recognition with reply functions

Definition Software that simulates a 'virtual person' and interacts with existing application software through rule-based tasks in the same way humans would do

Replace humans in performing repetitive rule-based tasks, use logic to model decisions in the process

Interact with any application or system and can work on multiple systems

Process transactions, manipulate data, triggers responses and communicate with other digital systems

✓ ✗

RPA in a nutshell

Robotic Process Automation & Automation platforms 1 A


https://vimeo.com/156440621

https://vimeo.com/156440621


AI relies on machine learning techniques for problem solving – A breakthrough in deep learning enabled AI for practical applications

Search and optimization

Constrain satisfaction

Local reasoning

Control theory

Probabilistic reasoning

Machine learning

Reinforcement learning Random forest

Support vector machines

Bayesean networks

Genetic algorithms

Deep Learning

Association rule learning

Decision trees

Problem solving techniques

Artificial intelligence – Terminology classification

Source: IBM; Roland Berger

Artificial intelligence

> Artificial intelligence extends cognitive computing by not only suggesting solutions to problems, but also by making actual decisions based on the results of data analyses

> Example: Self-driving car that analyses the environment and decides to break, accelerate, or change lanes

Cognitive Computing

> Cognitive computing supports people in making decisions by analyzing large amounts of (unstructured) data and suggesting solutions to problems

> Cognitive computing systems only support the decision making, the actual decision is taken by humans

> Example: System that analyses patient data and suggests potential treatment options including advantages and disadvantages to doctors

Artificial intelligence (machine learning) 2 A

Most important problem solving technique for AI


Artificial intelligence – Simplified history

It took close to 60 years and many so-called "winters" of stagnation for AI to reach today's state

Source: Press research; Roland Berger

1956-1974

> Solving problems in a specific domain of knowledge by using rules derived from experts

> Potential use cases were:

– Identification of chemical compounds from spectrometer data

– Diagnosis of infectious blood diseases

> Understanding and processing natural language, e.g., translating from English to Russian

Natural language

Reasoning as search

> Finding a problem solution by searching for the answer

> Focus on artificially simple situation, e.g., block worlds consisting of colored blocks of various shapes and sizes

Microworlds

> No translation of words with context-dependent meaning possible

> No extrapolation outside microworlds

> Complexity exceeding avail. computing power

Issues leading to disappointment of ambitions and stop of funding

AI winter

Expert systems

> No synergies in creating expert systems for different domains – Proprietary algorithms for each system required

1980-1987 1980-2012

Intelligent agents

> Isolation of problems and finding verifiable and useful solutions

> Common language allowing interaction with economics and control theory

New advanced tools

> Utilization of new tools like

– Bayesean networks

– Stochastic modeling

– Neural networks

– Evolutionary algorithms

Break- through 2012

Deep learning

> Utilization of multi-level neural networks to solve complex problems like picture and speech recognition

Issues leading to disappointment of ambitions and stop of funding

AI winter



Artificial intelligence – Projects of global technology companies

As a response to AI's success in recent years, most global technology companies have made it one of their key priorities

Source: Press research; Company information; Roland Berger

Google IBM Facebook

> The Google Brain project investigates deep learning since 2011 and developes TensorFlow, an open source software library for artificial intelligence

> In 2014, Google acquired DeepMind Technologies, the company that later developed the AlphaGo program

> IBM started to develop its cognitive computing system Watson in 2005 and added deep learning algorithms after its commercialization in 2014

> Based on Watson, IBM offers solutions for R&D projects in the pharma, publishing, and biotechnology industry, self-service applications, as well as enterprise analytics

> FAIR was founded in 2013 and developed several deep learning algorithms used for photo tagging and text translation as well as extensions to Torch, an open-source library for AI development

> In 2015, Facebook acquired Wit.ai that currently develops its personal assistant "M"

"Our deep learning tool has now been deployed in many environments, particularly across Google in many of our production systems"

"Watson is the the biggest, most important thing I’ve seen in my career and is IBM’s fastest growing new business in terms of revenues"

"We’re trying to build more than 1.5 billion AI agents – One for every person who uses Facebook or any of its products"



B. Use cases


Application of RPA and augmented intelligence by activity type and impacts


Robotic Process Automation and Augmented Intelligence apply to different perimeters

Easily robotized

activity

Simple

and rule-

based

Complex

and

judgment

based

Unstructured Structured

Sim

plic

ity

Structure

> Software that simulates a 'virtual person' and interacts with existing application software through rule-based tasks in the same way humans would do

Robotic Process Automation

Focus on

maximizing

robotization and

interfacing with

human

interaction

Focus on

structuring

processes to

enable

robotization

Complex activity

needing human-

like decisions

-

Augmented

Intelligence

sweetspot

> Partially automates operations and enhances complex decision making through solutions combining Natural language processing, machine learning and hypothesis generation

Artificial Intelligence

Quality

Speed

Cost

1

2

3

> Reliability: 100% accuracy > Improved customer satisfaction > Better decision making thanks to

increased focus of staff on more added value tasks

> Solutions working 24/7 > Enhanced processing speed > Increased capacity to handle

volume in back office leading to less demand failure in front office

> Quick payback ( Typically 12-24 months)

> Reduced labor costs > Limited investment needed given

smart interfacing with existing IT infrastructure

1

Use cases B


In most industries, maximum AI and RPA potential is reached on back / middle-office activities and selected support functions

AI RPA Human


Overview of potential use of RPA and AI technologies by function

Front-office Back-office Middle-office Support functions

Au

tom

atio

n p

ote

nti

al

> Highest automation potential in Back

and Middle office activities driven

by:

– input data already digitized to a

large extent

– industrialized processes with

clear rules

– proliferation of IT systems and

tools (CRM, sales, claims, etc.)

> Several support functions involving

data processing can be automated

to a large extent (e.g. accounting,

controlling, payroll, etc.) – unlike

functions involving more creativity

and human interactions (marketing,

communication, recruitment,…)

Use cases B


Overview of vendors (selection)

A growing number of vendors is offering software solutions for business process automation

Source: Company information; Roland Berger

Typical use cases

Solution providers

> Automated data transfer between different systems, e.g., between CRM and ERP systems.

> Error detection for large data arrays like transaction matching and account reconciliation

> Automated cybersecurity incident response

> Self-learning of ability to distinguish between different types of documents, e.g., between invoices, claims and questions

> Chat and voice bots with ability to process natural language and to answer automatically including clarification questions if necessary

Use cases B

Robotic process automation/ Automation platforms


2 Automation technique


The number of growing use cases for RPA, automation platforms, and AI confirms the large potential of software-driven automation

Overview of selected use cases


Banking process automation (RPA)

> Automation of review processes for banking transactions

Use cases B

Software service automation (RPA)

> Automated handling of software requests

Robotic process automation/ Automation platforms


2

Automated cyber attack response (RPA)

> Automated reaction to cyber threats

IT incident handling (Automation platform)

> Automated solving of standard IT incidents, esp. L0 and L1

> 85% automation of the SSI1) process in global banking

Call center automation

Automated email processing

> Automated categori-zation of emails incl. recognition of key data

Automated claims processing

> Automated import of data from claims in database

Automation of SSI process in banking

Recruiting process automation

> Automated candidate search and prequalification

> Automated handling of incoming customer calls

Automated energy management

> Automated optimi-zation of data center power consumption

1) Standard settlement instruction


Automation of banking transactions / IT services / reaction to cyber threats

RPA can be used to automate standard banking tasks, IT services, and the handling of cyber threats

Source:

http://www.blueprism.com/case-studies

Source:

http://thoughtonomy.com/computacenter-deploy-virtual-workers-in-service-desk-2/

Source:

http://ayehu.com/cyber-security-incident-response-automation/eyeshare-for-automated-cyber-security-incident-response/

Robotic process automation & Automation platforms 1 B

Problem

> Each day more than 2500 high-risk bank accounts with insufficient funds have to be reviewed manually

✓

Approach

> RPA is used to automate the process based on predefined rules

> Software accesses the bank's core systems and does not require any system changes

Advantage

> 80% reduction of processing costs

> Process time reduction by more than 50%

> Increase of consistency

Problem

> High workload for service desk staff due to manual procedures shifts focus away from their individual customer service tasks

✓

Approach

> 95% of key user administration tasks are offered via an self-service portal by utilizing RPA

> After service request, RPA performs task by emulating a human user

Advantage

> Self-service portal enables 24/7 execution of key activities

> Desk staff can focus on customer service instead of manual intensive tasks

Problem

> IT personnel can only hardly handle increasing volume and high speed of cyber attacks

✓

Approach

> RPA is used for

– automated log-out and password reset in case of multiple simultaneous logins

– isolation of client from LAN in case of malware detection

Advantage

> Decreased response time to cyber incidents

> Reduced workload for IT personnel



Artificial intelligence (machine learning) 2 B

Call center automation

Amelia understands, learns, and adapts to natural language to handle service desk and expert advisory tasks

Cognitive agent Amelia

Understanding information

> Amelia understands written and spoken language including contextual information

> She is able to understand the user's mood

Learning

> Amelia learns from live interactions

> If she cannot solve a problem, she hands it over to an employee and learns by listening to him

Service desk / call center support

✓ Problem

> The IT service desk of a large media company needs to handle more than 65,000 calls per month which leads to high workload

Approach

> Amelia learned to take 64% of the incoming calls through observational learning

Advantage

> Reduction of staffing requirements from 76 to 32 FTEs

> Reduction of the mean time to resolve an issue from 18.2 to 4.5 minutes

> Reduction of the average speed of an answer1) from 55 to 21 sec.

✓ Problem

> Equipment troubleshooting requires large amounts of knowledge

Approach

> Amelia learned from machine manuals and company policies and provides guidance to engineers

Advantage

> Improved equipment trouble-shooting in complicated situations

Expert advisor for field engineers

1) Average time it takes for a call to be answered, includes time in waiting loop and duration of time in which the agents phone is ringing

Source:

http://leoforce.com/product.php

Source: Company information; Roland Berger


AI solutions have already started to be implemented in Financial Services to optimize Middle Office activities (1/2)

Underwriting Contracts Management – Middle Office

Contracts Management - Operations

Claims Management

Solution prov. Year

Use cases in Financial Services – Middle Office

2016

2015

2016

2016

2015

2016

Service

Assistant virtuel : Réponse aux questions des chargés de clientèle dans le domaine de

l'assurance

Moteur de recherche intelligent multi-source (textes, images, média sociaux,…) : analyse des données client et identification des moments de vie

Moteur de recherche intelligent : Recherche de toutes les données disponibles (structurées et non structurées) pour construire une vision client synthétique, 360° en temps réel

Agent conversationnel : Réponse en direct aux questions des clients (ou transfert vers un gestionnaire si la complexité est trop élevée)

Moteur de recherche multi source comprenant le langage naturel : anticipation de la volumétrie des motifs d'appels au support client, pour jour la FAQ / page d'accueil en anticipation

Analyseur d'email clients: Détection de l'intention et prise automatique des rendez-vous commerciaux / réponses à certaines demandes (ex : transmission d'attestation d'assurance)

Agent conversationnel intelligent répondant aux questions des clients

Country

2014

Client

RBS

Source: Analyses Roland Berger



AI solutions have already started to be implemented in Financial Services to optimize Middle Office activities (2/2)

Use cases in Financial Services – Middle Office

2015 Hitachi (TBC) Moteur de recherche support aux gestionnaires (call center auto) : Recherche de réponses aux questions clients – grâce à la compréhension de la voix. Gain de 15% sur les temps de communication

2013 Assistant virtuel: réponse aux questions des clients sur les produits et services

Non communiqué 2015 Moteur de recherche intelligent : Automatisation du processus de KYC (recherche, agrégation, et vérification des données clients )

2012 Assistant vocal KAI répondant oralement aux demandes des clients concernant leur compte bancaire

2016 Agent conversationnel : Réponse aux questions des clients (après avoir été entrainée en interne au sein du "helpdesk" du service informatique)

Assistant virtuel : Réponses aux questions des clients, intégré à l'application mobile de la banque

2016





Claims Management

Solution prov. Year Service Country Client


AI solutions have already started to be implemented in Financial Services to optimize Operations activities

Use cases in Financial Services – Operations

Analyseur d'emails : Identification du contenu des emails client (intention, urgence) et lancement d'actions pour certaines intentions (ex : pré remplissage des champs de virement)

2016

2014 Analyseur d'emails : Identification du contenu des emails client, routage vers le service compétent, proposition de réponses automatiques, aide à la réponse,

Agent virtuel conversationnel. Réponse aux questions des clients et réalisation d'opérations : virement, analyse de dépenses,… via une interface de "chat" intégré à l'application

2016




Claims Management


Contracts Management – Operations


AI solutions have already started to be implemented in Financial Services to optimize Claims Management activities

Use cases in Financial Services – Claims Management

Aide à la détection de fraude : identification de profils de fraudeurs basé sur l'analyse des données clients

Confidentiel

Aide à la détection de fraude : construction en temps réel d'un score qualifiant le caractère suspect ou non des déclarations de sinistres envoyées et de qualifier les types de fraudes potentiel

Confidentiel

Digitalisation du traitement des sinistres, grâce à la reconnaissance du langage écrit, l'auto remplissage de formulaires, et l'interface avec de nombreux systèmes de gestion

Global

Application mobile de déclaration des sinistres : Scan des pièces justificatives, détection automatique des fraudes

Traitement automatisé des sinistres via la reconnaissance du langage écrit, l'extraction des informations, la comparaison des informations vs. termes de l'assurance

COGITO



Claims Management

Global 2015+

2016

2016

2015+

2015+





C Detailed case studies


RPA was the preferred option in a cost reduction exercise for the finance function of a global insurance company

Context Approach

> Leading insurance company

with operations in +40

countries and ambitious

growth and profitability targets

> Finance team of ~200 FTEs

with Accounting team

representing 60% of finance

staff. Limited use of SSC or

offshoring so far

> Recent merger in the group

has set expectations for

synergies in the Finance

function

> Overall cost reduction of 20% set for

the finance function, combined with a

necessity to reduce headcount

> Target cost reduction of 30+% for

accounting as it was identified as the

area with most potential

> Integrate finance organizations of

recently merged companies

> Detailing of activities within

accounting (80+ activities for 125

FTE)

> Definition of baseline volume of

workload (#FTE) per activity

> Evaluation of potential for

process automation and

offshoring for each individual

activity

> Consolidation and challenge of

results from a general perspective

to increase the level of

offshoring/automation while

maintaining local oversight


Case study – Finance RPA in Insurance

RPA case study – Finance function automation C 1


Criteria of savings potential & digital feasibility drive the decision for automation together with local constraints

Source: Roland Berger project experience

Should the activity be robotized or off-shored?

Possible gains from robotization?

• Is the process (relatively) stable over time with frequency from (intra-) daily to weekly/monthly?

• Is the volume of workload of this activity sufficient to justify up-front investment & license cost?

• Is the process subject to frequent errors?

• Is the process centralized?

• Is the workload for this activity variable, leading to low team productivity ?

• Does the process involve high headcount?

Does it have potential it be robotized? High

Low

Low High

Po

ten

tial

fo

r ro

bo

tiza

tio

n

Potential for offshoring

Retained local • Regulatory obligations

• Client-facing roles

• General oversight over

finance & accounting

Offshored to SSC • Regulatory obligations

• Client-facing roles

• General oversight

Robotization (preferred over offshoring)

• General ledger accounting

• Accounting reconciliations

• Cash disbursement & bank reconciliations

• Fixed asset accounting

• Core business accounting (claims & premiums)

• Data input from other systems (e.g. HR,

operations)

60% 15%

25%

Decision tree for analysis of robotization or

& outsourcing potential

Does it have potential to be offshored to a SSC?

Business & legal constraints to offshoring?

• Is the activity subject to regulation requiring the activity to be performed at local level ?

• Is the activity required to maintain oversight over the accounts & be able to assume legal responsibility?

• Is the activity in strong interaction with the client?

• Is an error in this activity potentially impacting business result?

• Are specific language skills required to carry out the activity?

• Is in-depth personal interaction with other functions required to carry out the activity?

• Is the expertise required to carry out the activity available in the remote location?

"When possible, robotization

should be preferred over

offshoring, for reasons of cost,

quality & speed"

Illustration of segmentation results

Feasibility of robotization?

• Does the activity follow a process that can be largely standardized (vs. subject to many exceptions)?

• Does the activity follow a rule based logic that can be programmed (vs subject to judgment and interpretation)?

• Can the input for the activity be digitalized, in a structured and consistent format ?



Assessment of robotization potential was carried out on a granular list of activities with the local finance teams in the different BUs

Source: Roland Berger project experience

I. Accounting/ reporting General accounting General accounting

I. Accounting/ reporting General accounting Entries to the general ledger (including provisions) Automation / RobotisationFinance Shared ServiceFinance Shared Service

I. Accounting/ reporting General accounting General ledger change management Retained in local BU Retained in local BU Retained in local BU

I. Accounting/ reporting General accounting Cost and revenue allocation principles and systems implementation Automation / RobotisationRetained in local BU Retained in local BU

I. Accounting/ reporting General accounting Quality assurance, accounting policies, standards proce-dures setting Retained in local BU Retained in local BU Retained in local BU

I. Accounting/ reporting General accounting Controls over reconciliations Retained in local BU Retained in local BU Retained in local BU

I. Accounting/ reporting General accounting Booking of reserve setting Retained in local BU Retained in local BU Automation / Robotisation

I. Accounting/ reporting General accounting Booking of impairments of investments Regional Competence CenterFinance Shared ServiceFinance Shared Service

I. Accounting/ reporting General accounting Booking of tax reserve setting Retained in local BU Retained in local BU Retained in local BU

I. Accounting/ reporting General accounting Closing process for full monthly, quarterly, and yearly financial statements Automation / RobotisationRetained in local BU Retained in local BU

I. Accounting/ reporting General accounting Closing Accruals Finance Shared Service Finance Shared ServiceFinance Shared Service

I. Accounting/ reporting General accounting Closing Accruals Retained in local BU Retained in local BU Retained in local BU

I. Accounting/ reporting General accounting Closing reports for consolidation Finance Shared Service Finance Shared ServiceFinance Shared Service

I. Accounting/ reporting General accounting Legal statement for audit, local reports, insource local activitiesRetained in local BU Retained in local BU Retained in local BU

I. Accounting/ reporting General accounting Other Retained in local BU Retained in local BU Retained in local BU

I. Accounting/ reporting Investment accounting Investment accounting Finance Shared Service Finance Shared ServiceFinance Shared Service

I. Accounting/ reporting Reinsurance accounting Reinsurance accounting

I. Accounting/ reporting Reinsurance accounting Group reinsurance accounting Automation / RobotisationFinance Shared ServiceFinance Shared Service

I. Accounting/ reporting Reinsurance accounting Reinsurance accounting fronting Automation / RobotisationRetained in local BU Retained in local BU

I. Accounting/ reporting Premiums accounting Premiums accounting

I. Accounting/ reporting Premiums accounting Insurance accounts receivables Automation / RobotisationRetained in local BU Retained in local BU

I. Accounting/ reporting Premiums accounting Billing and charging Automation / RobotisationRetained in local BU Retained in local BU

I. Accounting/ reporting Claims accounting Claims accounting Automation / RobotisationRetained in local BU Retained in local BU

I. Accounting/ reporting Non-insurance accounting Accounts payables

I. Accounting/ reporting Non-insurance accounting Accounts payables Finance Shared Service Finance Shared ServiceFinance Shared Service





I. Accounting/ reporting Non-insurance accounting Accounts receivables

I. Accounting/ reporting Non-insurance accounting Accounts receivables Retained in local BU Retained in local BU Retained in local BU


I. Accounting/ reporting Non-insurance accounting Accounts receivables Finance Shared Service Finance Shared ServiceFinance Shared Service




I. Accounting/ reporting Non-insurance accounting Fixed assets

I. Accounting/ reporting Non-insurance accounting Fixed assets Finance Shared Service Finance Shared ServiceFinance Shared Service


I. Accounting/ reporting Travel & entertainment expensesTravel & entertainment expenses

I. Accounting/ reporting Travel & entertainment expensesT&E booking Finance Shared Service Finance Shared ServiceFinance Shared Service

I. Accounting/ reporting Travel & entertainment expensesT&E payment Finance Shared Service Finance Shared ServiceFinance Shared Service

I. Accounting/ reporting Payroll accounting Payroll accounting

I. Accounting/ reporting Payroll accounting Booking payroll Retained in local BU Retained in local BU Automation / Robotisation

I. Accounting/ reporting Payroll accounting Clearing payroll Retained in local BU Retained in local BU Retained in local BU



























































































Illustration of analyses carried out

Highly detailed mapping of activities and sub-activities (c. 80 activities for c. 125 FTEs)

Determination of FTE baseline per geography

Systematic assessment with local teams of possible levers per activity:

– Offshoring to shared service centers

– Robotization & automation

– Required to be retained in local BU

Interviews with local people to understand drivers & complexity of activities

Cornerstones of the approach

BU 1 BU 2 BU 3

BU 1 BU 2 BU 3

BU 1 BU 2 BU 3

Overall coherency check & further challenge of the allocation results



Through these experiences, we acquired a broad view on robotization potential within Finance functions

Robotization potential across insurance companies – illustration

Large scale robotization Partial robotization No robotization

Source: Project experience, Roland Berger

Finance process

Transac- tional Accounting

Insurance Accounting

General Accounting

System support

Other Finance

Accounts payable + TE

Accounts Receivable

Bank reconciliations/ cash applications

Fixed assets accounting

Re insurance accounting

Premiums accounting

Claims accounting

Investment accounting

Solvency II support

General accounting

Third party accounting

Credit control & reporting

Finance solutions (MDM)

Management reporting

Data analytics

Legal & Compliance Support

Actuarial support

Procurement

Global Corporate insurer

Global service company

Global re-insurer

Large global life insurer

Large national life/non-life insurer (1)





Robotization was often preferred to offshoring – 40% of the accounting team was directly impacted by the project

Achieved results Key learnings

> 40% of accounting organization directly impacted by the project results in a first phase, with additional potential to be further investigated at a later stage

– Reduction of ~25% of total staff through robotization projects, allowing the remaining organization to focus on more value adding tasks

– 15% of total staff relocated to a shared service center, to realize activities not suitable for automation at a lower cost

> Run-rate cost reduction of ~30% compared to overall labor cost of the function, taking into account all costs related to offshoring

> Process to be optimized as much as possible before robotization, to ensure adequate quality level and limit system investments

> High activity volume & frequency are preferred scope for automation to counterbalance the required investments & workload

> Complexity of processes only shows when detailed analysis is carried out


Return on experience of the project



Watson was implemented on two pilots highlighting savings opportunities of up to 50min/day on account managers by 2020

Productivity savings estimates [2016-2020] – Pilots


90%

70%

2016 2017 2018 2019 2020

55%49%43%36%

2016 2017 2018 2019 2020

30%

Email analyser

2020 2016

2020 2016

50 min. 25 min.

Detection rate

Total

Virtual assistant

Satisfying answers rate

> Automatized identification of e-mail intent and level of priority, sorting

and visualization based on those two criterias

> Automatic login into IT applications and pre-filling of some information in

the target application

> Customized client answer proposal

> Automatic answers on simple cases

> Machine learning leveraged to continuously improve successful detection rate

Watson Performance Description of levers

Productivity savings [min/day]

> Chat bot to answer simple and recurring questions on products

> Connection to the document database

> Display of a short list of information specifically extracted

– Probability estimate of successful answer

– Link to relevant documents

> Machine learning leveraged to continuously improve successful answer rate

AI case study – Watson assessment and prioritization C 2


Illustration – Bottom-up analysis and sizing of e-mail activities Split of activities per profile [hours/day]

Watson extension to new use cases was assessed through a bottom-up analysis of account managers activities

Admin. work

E-mails

Profile C

8,0

0,7

0,3

3,9

0,5

1,4

Profile A

8,0

1,8

1,8

0,5

2,3

0,2 0,5

Profile B

8,0

0,7

Meetings

3,2

0,7

1,9

Meetings prep.

Researches

0,2

Operational tasks 0,9

1,2 1,2

Assessment of Watson potential - Analysis of an account manager typical day

Activity where Watson could prove useful in most areas


1%1%1%1%1%

1%2%2%2%2%2%

0%

6%6%

7%

3%3%3%3%4%

4%4%4%4%4%

5%

Rendez-vous - Le client souhaite obtenir un rendez-vous avec son chargé de clientèle ou passer en Caisse/en Agence

Contact - Le client souhaite être contacté par le Chargé de clientèle

Editer - Le client souhaite que la banque lui transmette un document

Document - Le client souhaite transmettre un document à la banque

Proposition - Le client souhaite bénéficier d'une offre commerciale de la banque

Ecriture - Le client souhaite des informations sur une ligne d'écriture de son relevé de compte (frais, commissions, etc)

Communication - Le client souhaite informer la banque d'une opération à venir

Virement - Le client souhaite effectuer un virement

Modifier - Le client souhaite modifier un contrat

Souscription - Le client souhaite ouvrir un contrat (assurance, prêt, etc)

Clôturer - Le client souhaite clôturer ou résilier un contrat

Négocier - Le client souhaite négocier une tarification

Moyen de paiement - Le client souhaite savoir si son moyen de paiement est disponible

Personnel - Le client fait part d’un changement d'information le concernant

Lever blocage - Le client demande le déblocage de sa carte bancaire

Tarification - Le client souhaite se faire expliquer une tarification

Renégocier - Le client souhaite renégocier un crédit

Situation – Le client fourni des informations liées à une situation débitrice

Rembourser - Le client souhaite rembourser son crédit par anticipation

Débloquer – Le client souhaite débloquer un crédit

Créancier - Le client souhaite interdire un créancier

Rejeter - Le client demande de rejeter ponctuellement un prélèvement

Chèque de banque - Le client souhaite l’émission d’un chèque de banque

Résiliation – Le client demande à clôturer un virement permanent ou un versement programmé

Fraude – Le client informe sur une fraude le concernant

Opposition – Le client souhaite faire une opposition de sa carte bancaire

Split of different e-mail intentions Difficulty Estimated time [min]

48

46

444

68

106

44

2888

64

26

42

62

4

Activity where Watson could prove useful in some areas


Confidential


We identified additional AI use cases which could lead to significant productivity savings

Productivity savings estimates on potential extensions [2020] - not exhaustive

> Automation of answers regarding document requests (identification of detailed intent, proposition of answer with documents)

> ~x min / day

> Automation of answers to information requests on fees (identification of the intent, proposition of a standardized answer for frequent cases)

> ~x min / day

E-mail analysis

Description of the levers Productivity savings

> Partial automation of contract modifications or changes in client information (including field matching and manual validation)

> ~x min / day

> Automation of rejected payment requests > ~x min / day

> Automation of meeting preparation: Client history & status synthesis, Product recommendations > ~ x min / day

> Partial automation of meeting minutes: Filling of specific field based on minutes in free text > ~ x min / day

> Automation of overdraft management: Recommended decision (no action /e-mail relaunch/ blockage) and standardized e-mail answers according to client history and situation

> ~ x min / day

Processing assistant

> Extension of the virtual assistant to additional fields

– Financing

– Insurance…

> ~x min / day

Virtual Assistant

> ~ x min / day > Client value management support: Prioritized listing of clients to contact


Commercial assistant



Overall, the productivity improvements could reach until 16% in 2020

Source: Analyse Roland Berger

Gains de productivité liés à Watson : Estimation de l'impact ETP en fin d'année [2017 – 2020; ETP 1)]

Gains de productivité (potentiel total) sur le réseau du client [2017-20]

Dec. 2019

Périmètre et évolution d'impact client

Périmètre client, évaluation revue

3 : Extension du périmètre des cas d’usage actuels

4 : Nouveaux cas d’usage

Dec. 2020 Dec. 2018 Dec. 2017

1

2

3

4

Impact en % des chargés de clientèle 4% 10% 13% 16%



Watson roll-out on account managers network shows potential for 16% of productivity savings

Achieved results Key learnings

> 16% productivity savings on total account managers network

– Revised potential on the pilot scope based on analysis of activities and pilot results

– Extension of existing use cases to additional scopes

– New use cases identified as part of the study

> Progressive ramp-up of productivity improvements over 4 years

> In-depth analysis of activities brings additional insights on AI potential

> Almost all activities can be partially or totally automatized with AI (even interactions / conversations with customers)

> Machine learning gives an advantage to size and experience / AI boundaries can gradually be pushed very far

> 2 types of AI solutions providers : "universal" (eg. Watson) vs. "vertical" (eg. fintechs)

> Social acceptance and impacts of AI solutions to be carefully handled and anticipated


Lessons learnt



D. How to move forward?


Financial Services players can consider several routes to leverage digital optimization opportunities

Approaches to digital optimization Digital levers

RPA … AI

Pro

cess

es

On-boarding

Mortgage re-financing

…

End-to-end digital re-engineering

> No restriction redesign

> From customers perspective

> Bottom-up "Reality Check"

Sustainable holistic transformation

C

Digital lever maximization

> Systematic across all process steps

> Combination of levers

Focused short term results

A

Marketing

Back-office

….

Digital AZBB

> Comprehensive activity review

> Digital and non-digital levers

Short/Medium-term impact

B

Approach D


P&C personal claims

Personal policy admin/underwriting

Risk management

Marketing analytics

Procurement BI

Payments processing

Business banking origination/servicing

Acturial & claims analysis

FP&A Finance MDM

Marketing automation/ campaign mgmt.

Transactional procurement

P&C agency support

Collections

AML Mortage servicing

P&C commercial underwriting

Sourcing/category mgmt.

Supplier risk & perf. mgmt.

Marketing MDM

Procurement MDM

Mortage origination

Basel2

Dodd frank compliance P2P

O2C

Auto finance

KYC/AML

Stress testing

Loan underwriting/origination

Loan portfolio mgmt.

Multi-channel customer mgmt.

P&C commercial claims

Account set-up/servicing

Retail brokerage

R2R

Equipment finance

Basel implementation

Retirement services

Tech

no

log

y ap

plic

abili

ty

Impacting important business challenges

Marketing Procurement Risk Finance Banking & Insurance Operations

Source: Genpact; Roland Berger

Technology applicability and impact on business challenges

Example – Many processes have a potential of using automation techniques but need to address most important challenges first

Few Many

Low

High

Potential automation applications which large impact on important business challenges

Target functions/

processes to automate

Source:

http://www.genpact.com/downloadable-content/insight/the-impact-of-technology-on-business-process-operations.pdf

Approach D


Example – An integrated RPA/AI roadmap needs to be designed along with a dedicated Target Operating Model to reach full potential

Illustrative – RPA/AI roll-out

> Understand which areas/processes offer potential for automation, evaluating

– Data intensity

– Complexity

– Volume

– Criticality

– Stability

– Systems and people involved

– etc.

> Prioritize areas of application based on savings potential vs. need for technical and organizational transformation

> Technological transformation

– Understand options, preconditions, and limitations of automation, including dialogue with selected vendors

> Organizational transformation

– Define TOM1) and assess the required changes to enable automation

– Set-up CoE2) : positioning in the organisation, profiles and roles & responsibilities, monitoring and evaluation guidelines

– Evaluate HR implications (personnel transfers, redundancies, organizational re-design, Workers Council involvement, change mgmt., etc.)

> Business plan

– Develop a holistic business plan and transformation roadmap

> Source the technology needed (make, buy, partner)

> Implement successive pilots subsequently enlarging the scope

> Start implementation of organizational measures and change management

> Train employees in new processes

> Measure results

> Evaluate automation in further areas – Implement continuous improvement mindset

Assess full potential Plan transformation Execute plan 1 2 3 > Lower cost

> Higher quality, accuracy, reliability, and compliance

> Focus of workforce on high-value tasks

> Build-up of critical technology know-how for constantly rising digital penetration

Realize benefits

Approach D


1) Target operating Model 2) Center of Excellence


Beyond selected enablers, full and lasting impact of digital levers relies on several sustainability factors


Implementation enablers and sustainability factors of Digital levers

Implementation enablers @

Cloud computing:

Simple and quick deployment of technologies, limited costs to store/use

large amount of data and to host solutions,…)

Data Management:

Availability and completeness of data, consistency of data quality, clear

governance and ownership over data

Visibility on activities/processes

Critical to prioritize, provides initial material for each lever, enables faster

time-to-market for re-engineering

Enablers Sustainability factors

Robot management

> Adapt the organization to integrate digital management

> Center of Excellence to cover governance (IT & Ops) / technology / Expertise rollout

HR adaptation:

> Fully leverage the downsizing or reprioritization opportunities with impacted teams

> Development of new skills

Compliance evolution

> Necessary to adapt to newly automatized process

> Requirement of new certifications

System orchestration

> Critical to reach end-to-end automation where possible

> Scoping and prioritization

> KPIs used to monitor the orchestration

Approach D

automation in financial services - adetem · 20170616 automation in financial services_adetem.pptx...

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