fides treasury services ltd. - fides conference …...2018/08/24 · rule-based system machine...

Fides Conference 2018Digital Treasury 4.0

Fides Conference 2018

What does digital mean in the evolution of Corporate Treasury?

Zurich, 24 August 2018


Agenda

1 The evolution in Corporate Treasury

2 Digitization and what it can mean for a Corporate Treasury

3 Use case: Machine learning

4 Use case: Data analytics


Go to www.menti.com and use the code 90 04 4

What are the three first

topics you think of when

discussing «Digital

Treasury»?

http://www.menti.com/

The evolution in Corporate Treasury

Section


Where Treasuries stand today and where they are willing to evolve to

Treasuries are subject to

constant changes

triggered by internal

stakeholders and

external forces. This

interaction creates a

growing need to enhance

current treasury practices

and further link treasuries

with other non-common

or selective domains

such as working capital

management, tax, and

insurance, to name a few.Immature Developing Mature Leading Practice

Sim

ple

Sta

nd

ard

Co

mp

lex

Proportion of companies

Large entities

SME

Bandwidth

Evolution willingness

Maturity state

Degree of sophistication

Digital transformation is all about

interaction and iterative design


The evolution in Corporate TreasuryV

olu

me / C

om

ple

xit

y

Benefits

Full control of cash flows,

improved cash forecasting

Benefits

Enhanced visibility, more efficient

liquidity management

Benefits

Significant cost savings, improved

negotiating position with banks

Benefits

Greater operational efficiency,

reduce risks

Maturity

Manual efforts

Use of technology

Level of centralization

Treasury 1.0

Cash pooling and central visibility of

cash positions

Subsidiaries work with local banking

partners

Decentralized hedging approaches

of subsidiaries trading directly with

banks

Treasury 4.0

Expansion of the in-house bank

function to include collections on

behalf of subsidiaries (collection

factory)

Making use of alternative financing

and investment solutions (e.g. peer-

to-peer)

Sophisticated risk management

approaches e.g. by use of “at risk”

measures such as value at risk

(VaR) or Cash flow at Risk (CFaR)

Reduced manual processes by

using Robotic Process Automations

(RPA) and machine learning

applications

Treasury 3.0

In-house bank approach providing

various services for subsidiaries

Settlement of all intercompany and

external flows where possible via in-

house bank accounts

Central hedging (FX, commodity and

interest) via in-house bank

(Intercompany trades)

Global bank relationship

management with clear banking

strategy

Increased use of technology and

workflows to streamline processes

Treasury 2.0

More sophisticated cash pool and

upstreaming structures (e.g.

automated third party sweeping,

overlay structures, etc.)

Payment factory either as SSC,

processing centre or payments on

behalf of subsidiaries

Centralized funding and

intercompany financing

Global risk management and

hedging strategy and approach


A more centralized set-up increases efficiency and enables Treasuries to evolve in a more strategic role

Cash & liquidity management

Payments and collection

Funding & intercompany financing

Financial risk management

Intercompany netting

80%

10%

10%

Core operational activities of an in-house bank

Adding value with strategic initiatives and decision making

Today Tomorrow?

Strategic initiatives,

process excellence and

continuous business

development

Decision making and

execution

Core operational activities

(manual)

Improvement of

Target Operating

Model

► Straight through

processing

► Technology and

reduced manual

efforts

► Increased cost

efficiency

► Optimized

monitoring and

controlling

Working capital optimization

Tax and Treasury

Accounting change opportunities

Core operational activities Cash & liquidity management

Payments and collection

Funding & intercompany financing

Financial risk management

Intercompany netting

40%

30%

Treasury as strategic influencer to create shareholder value through improving bottom line P&L and optimizing balance sheet effectiveness

30%

Supply chain financial risk management

-50%

Digitization and what it can mean for

a Corporate Treasury

Section


Digitization in Treasury has started a long time ago but the speed of change is exponentially increasing

1995 >>>

► Internet

► E-mail

► Floppy disk

► Client server systems and

Local Area Network (LAN)

2000s

► Electronic payments

► Remote Banking and Home

Banking

► Treasury Management

Systems

► Increasing use of data and

information providers

► New commincation channels

► Hard token

► Electronic invoices

2010 >>>

► Swift for corporates

► Unification and

standardization of messaging

(eg. XML, ISO20022)

► Digital workflows

► Digital invoices and e-

payments

► Smartphones – Tablets

► SaaS and Cloud solutions for

Treasury Technology

► Straight Through Processing

(STP)

► QR codes

>>> 2025

► Consolidation of cloud solutions

► New service provider such as FinTechs

► New means of Connectivity to banks and

service providers

► XS2A Access-to-Account Service (PSD2

enabled)

► Peer-to-peer solution (e.g. funding and

investement)

► Robotic Process automations

► Data analytics and machine learning

► Cryptocurrencies and blockchain solutions

► eBAM

► Cybersecurity

Cloud

solutions

https://blog.blockchain.com/

https://blog.blockchain.com/


What future world class Treasury can mean for a Treasurer in a digital world

Distributed

Ledgers

Internet of

Things

Mobile

Applications

Big Data

Analytics

Payment

Technologies

Cyber securityPlatform

solutions

Machine

Learning

Robotic Process

Automation



Please choose the three

topics most relevant for

your company today

and in the near future



Innovations and trends have different potentials and different levels of feasibility

NOW WOW

HOW

Po

ten

tia

l a

nd

fea

sib

ilit

y

Degree of innovation

Distributed

Ledgers

Robotic

Process

Automation

Big Data

Analytics

Cyber

security

Platform

solutions(peer-to-peer)

Mobile

ApplicationsMachine

Learning

NOW

Solutions that might not be most

disruptive but are already feasible to

implement today at reasonable costs

and manageable risks.

HOW

Highly innovative and disruptive

solutions where feasibility is not yet

clear. Adoption is either difficult to

achieve and also costs and risks are still

to high.

WOW

True game changers for treasurers

being highly innovative with a high

feasibility for implementation.

Payment

Technologies

Section

Use Case: Machine Learning


What analytics is and how it can support the business

PrescriptiveDetermines which decision/action will produce most

effective result against a specific objectives/ constraints

What happened?Hindsight

DescriptiveMines past data to report, visualize, and understand

what has already happened

PredictiveLeverages past data to understand why something

happened or to predict what will happen in the future

Foundational Level of difficulty

Insig

ht

Valu

e

Advanced

ForesightWhat’s happening now?

InsightWhat might happen?

Analytics is the science of

understanding the past and

predicting the future in order to

obtain insight and foresight.

1

The process of inspecting,

cleansing, transforming, and

modeling data with the goal of

discovering useful information,

suggesting conclusions, and

supporting decision-making.

2

Analytics starts with data, but

using tools and techniques such

as modelling, visualization, and

technology turns the data into

insights.

3complex branch of ML built on neural networks to mimic the

functioning of the human brain to provide higher accuracy on

more complex tasks

Example: self-driving cars

ability to self-learn using statistical and optimization techniques

Example: Online streaming service proposals for music

automation based on domain knowledge

Rule-Based System

Machine Learning

Deep Learning

artificial intelligence spectrum



In which areas would

you expect machine

learning models to best

support your daily

operations?



Machine learning: Predictive analytics models based on an expansive set of use cases

Data Quality (DQ)

Remediation

Intraday Liquidity

Optimization

Cash Flow

Forecasting

Payment Anomaly

Detection

FX Hedging Model

1 2

3 4 5


Use case commodity price predictions with machine learning techniques

Increasing efficient trading and enabling competitive advantagesWhy

WhatPrediction of commodity prices with data from inside but also outside the

organization

HowApplying machine learning techniques on digital data and information to predict

commodity prices


Demonstration on commodity price predictions

Video can be shared upon request

Use case: Data analytics

Section


A changing environment influencing bank relationship management

Traditional banking

relationship

Banks

New banking

relationship

Core banks

Changingneeds

Wallet sizing

Banking strategy

Banking policy

…

Evolving regulations

Relationship

banks

Local banks

Other service

providers

FinTechs

peer-to-peer

Other

financing

platforms



Do you have sufficient

transparency over your

banking structure with

regards to services

provided by bank and

fees charged?



Bank Fee Analytics

Video can be shared upon request


Use case of big data analytics with regards to bank fees

Transparency of bank fees is hard to achieve but helps to share the wallet

efficiently amongst the banking partners creating a win-win situation and enabling

significant cost savings for corporates

Why

WhatDetailed analysis of bank fees charged globally with a detailed breakdown by

category, country, service line, bank etc.

HowUsing various data sources such as ERP, TMS as well as bank reports and

sophisticated analytics tools to collect, cluster, categorize and visualize the data


The better the question. The better the answer. The better the world works.

Will digital help us do less or be more?


Contact details

Roger Disch

Partner

EMEIA Financial Services Risk

Advisory

Ernst & Young AG

Maagplatz 1

8005 Zürich

Tel.: +41 58 286 41 59

Mobile: +41 58 289 41 59

Mail: [email protected]

Sven Goeggel

Senior Manager

EMEIA Financial Services Risk

Advisory

Ernst & Young AG

Maagplatz 1

8005 Zürich

Tel.: +41 58 286 34 98

Mobile: +41 58 289 34 98

Mail: [email protected]

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fides treasury services ltd. - fides conference …...2018/08/24 · rule-based system machine...

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