preparing for the ifrs 9 game-changer · overview of ifrs 9 implementation challenges the major...

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How to reconcile the demands of risk and finance on a single,

flexible platform

Author: Shlomo Cohen, Risk SME, AxiomSL

Date: 14 April 2016

Preparing for the IFRS 9 game-changer

Preparing for the IFRS 9 game-changer: How to reconcile the demands of risk and finance on a single, flexible platform

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Table of contents

Executive summary ................................................................................................ 3

Overview of IFRS 9 implementation challenges .............................................................. 4

1. Upgrading data handling for joint risk and accounting compliance .................................. 5

2. Changing the nature of internal databases .............................................................. 7

3. Addressing the goals of both CROs and CFOs ............................................................ 9

4. Reconciling conflicting regulatory and accounting demands ........................................ 11

Conclusion: IFRS 9 calls for a game-changer: AxiomSL .................................................... 13

About the author ................................................................................................. 15

About AxiomSL .................................................................................................... 15

Contact ............................................................................................................. 16

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Executive summary

International Financial Reporting Standard 9 (IFRS 9) is a new accounting standard for financial

instruments, which firms around the world must implement by 1 January 2018 at the latest. It has

been designed to value assets and liabilities in a more risk-sensitive manner than the incumbent

International Accounting Standard 39 (IAS 39). Despite being an accounting standard and not a

supervisory measure, it introduces a new approach to credit risk.

IFRS 9 presents many challenges for financial firms, including accommodating the differing demands

of risk and finance, and managing large volumes of risk and financial data, which must be refreshed

at more regular intervals than ever before. This white paper reveals how these challenges can be

overcome by using a single, integrated platform with a flexible data model.

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Overview of IFRS 9 implementation

challenges

The major challenges of implementing IFRS 9

revolve around the requirement to

accommodate the perspectives of both risk and

finance. Historically, these two functions have

operated in isolation from one another,

developing very different cultures. To grasp

the uncertainties of the future, the risk world

is fond of statistics, which it uses to analyze

historical patterns and spot recurring behavior.

In essence, it is a world based on principles

rather than formal rules. In contrast, the world

of finance and, more specifically, accounting is

characterized by many detailed rules. At

times, the excessive complexity of these rules

results in situations that defy common sense.

These very different cultures have deep roots

in the bank and they translate into different

types of organizations: typically, risk

management has some degree of liberty as

long as it is compliant with regulations;

accounting, on the other hand, has to strictly

follow accounting rules without any kind of

freedom. Accordingly, risk and accounting data

is organized differently, and the goal of

combining these different types of data is a

real challenge.

This white paper analyzes the technical

challenges of implementing IFRS 9 and suggests

effective ways for firms to maximize their

return on investment in data management.

The common feature of the solutions proposed

in this white paper is the ability to feed

processes and reports with data that is

dynamically collected from a number of

internal and external sources, with no

duplication of data and no need to change the

IT infrastructure. This enables a rapid

implementation at limited cost, all the while

maintaining strong data lineage for mining and

auditing. This is exactly what AxiomSL’s state-

of-the-art platform offers.

Proper data management will produce

significant benefits by integrating risk and

finance, but only if the following challenges

are addressed:

1. Data handling must be upgraded to

support joint risk and accounting

compliance

2. The nature of internal databases must be

changed

3. The diverging goals of chief risk officers

(CROs) and chief financial officers (CFOs)

must be addressed

4. Conflicting regulatory and accounting

views of risk must be reconciled

Each of these issues is described below and

appropriate solutions are proposed.

The common feature of the solutions

proposed here is the ability to feed

processes and reports with data that is

dynamically collected from a number of

internal and external sources, with no

duplication of data and no need to

change the IT infrastructure


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1. Upgrading data handling for joint risk

and accounting compliance

The Basel Committee on Banking Supervision’s

239 (BCBS 239) Principles for Effective Risk

Data Aggregation and Risk Reporting refer to

risk data, not accounting data, which is

managed based on accounting standards.

Nevertheless, implementing IFRS 9 requires a

large amount of data that will have to fulfill

both sets of requirements. So, what is the

underlying logic of each framework? How do

they conflict? And how can they be

accommodated?

Accounting aims to provide accurate and

auditable figures. Billion-dollar balance sheets

must be published with figures that are precise

down to the last dollar, and it must be possible

to trace these financial figures back to their

sources. Moreover, the figures must be

disclosed within tight timelines and must be

accurate and transparent.

All of this financial data is intended to inform

stakeholders about how well the company is

performing, so they can make informed

decisions about its future. The financial figures

are also used to calculate taxes - a serious

matter. This is why the disclosures bear the

signatures of both the chief executive officer

(CEO) and CFO. Inaccuracies, whether

inadvertent or otherwise, may result in a jail

sentence. Consequently, accounting is

performed in a very controlled, structured and

rigid production environment.

Vision, anticipation, openness and

reactivity are required when

measuring risk and supporting

risk/return decision-making


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Risk works in a different, less structured

ecosystem because it is intended to support

decision-making in an uncertain environment.

The risk function tries to reduce the

uncertainties of the future by clarifying the

alternatives. The resulting insights are used to

support decisions about which risks to take and

to what extent; and which risks to hedge or

avoid. Outputs are holistic by nature - risk

appetite, risk tolerance, risk hedging etc.

Despite the efforts of regulators to formalize

and industrialize risk assessment, practitioners

need vision, anticipation, openness and

reactivity when measuring risk and supporting

risk/return decision-making. This calls for

flexible and easy-to-change decision-support

systems, with an underlying IT environment

that should also share these characteristics.

Trying to combine the goals of accountants and

risk managers leads to the following question:

can data be managed in a controlled,

structured and rigid production environment

while simultaneously being available for open,

flexible and evolving decision support?

AxiomSL’s platform provides both the

controlled environment required by accounting

and the flexibility required by risk. By imposing

no constraints on where the data is located

and by avoiding duplication and double

storage, data can be used in a rigid production

structure as well as in a flexible, adaptable

decision-support environment. Complex logic

can be defined graphically and understood by

both IT and business users. The platform

enriches the data, but retains the links to all

sources, providing full data lineage. Thus, all

data changes, whether due to human

interaction or system logic, are tracked and

auditable. Full data lineage is also critical

during testing and production to understand

the results. The ability to drill down to the raw

data sources, which are already known to the

users, is essential for establishing trust in the

system and for reassuring management about

the reliability of the final results.

AxiomSL’s platform

provides both the

controlled environment

required by accounting

and the flexibility

required by risk


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2. Changing the nature of internal

databases

In order to implement IFRS 9, databases will

need to be larger, more dynamic and open to

external data.

Under IFRS 9, the correct assessment of

expected credit loss (ECL) will require

significant changes to internal databases. The

fact that almost all assets will have to be

impaired will dramatically increase data

granularity and data volumes; the point-in-

time (PIT) requirement will make high-

frequency updates compulsory; and the

forward-looking approach will require a

significant inflow of external financial and

economic data.

Why will data need to be more granular?

Assessing ECL under the IFRS 9 framework

means calculating impairments for all assets in

the ‘amortized cost’ or ‘fair value through

other comprehensive income’ categories.

These two accounting categories should

comprise the bulk of all assets. This means the

number of assets to be impaired will increase

dramatically from thousands to millions. This is

a whole new ball game: in terms of data

management, the processes of purifying and

enriching the data, and the operations of

extraction and transfer, will have to be

massively enhanced and automated.

Why will data need to be updated more

frequently? The spirit of IFRS 9 is to identify

credit risk increases as early as possible. This is

apparent in the PIT requirement and the

changes to the buckets in which assets are

grouped. PIT measures such as PIT probability

of default (PD), loss given default (LGD) and

joint default correlations will probably have to

be updated at least once a month. This is the

update frequency for many macro-economic

indicators affecting the markets. Higher

frequencies must also be considered: the

supervisory guidance specifies that “ECL must

capture all significant increases in credit risk”

(guidance #45), so ECL assessment must be

linked to market information and must be

ready to be updated at short notice.

Why will external data be needed? ECL is

assessed as a probability-weighted discounted

cash shortfall. The weighting refers to possible

future scenarios. These scenarios have to

include “information that is available (…) at

the reporting date about past events, current

conditions and future economic conditions”

(IFRS 9 5.5.17). As ECL changes will have to be

disclosed and explained, a significant mass of

external economic and market data, both

qualitative and quantitative, will have to be

collected and stored for future retrieval and

analysis.

In terms of data management,

the processes of purifying and

enriching the data, and the

operations of extraction and

transfer, will have to be massively

enhanced and automated


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How can financial firms design an open

solution that can handle this granularity,

frequent updates and large volumes of

external data?

AxiomSL’s platform can store, manage and

control data at different levels of granularity;

and the frequency at which this data is

updated can be tailored to meet the needs of

individual parts of the business. The AxiomSL

platform is unified and can be used to control

all data and processes. A single environment,

team and architecture can be leveraged to

manage large volumes of historical, retail,

statistical data and precise balance-sheet

information for corporate clients. This unique

approach allows financial firms to reuse

resources, including hardware, software and

teams, leading to significant reductions in the

total cost of ownership (TCO).

A single environment, team and

architecture should be leveraged

to manage large volumes of

historical, retail, statistical data

and precise balance-sheet

information for corporate clients

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3. Addressing the goals of both CROs and

CFOs

IFRS 9 compliance will require risk data. Both

the CRO and CFO will want to own this data

because it is critical raw material they need to

meet their own current and emerging

requirements.

The CFO signs off the financial accounts and is

liable to the shareholders and the auditors

with regards to the transparency and fairness

of the disclosures. Consequently, s/he is used

to controlling accounting data from A to Z. The

new issues faced by the CFO include a massive

increase in the number of assets to impair and

a higher volatility of provisions, which s/he

will have to dynamically forecast and manage.

Securing the quality and availability of data

will be a critical success factor.

The concern is the same for the CRO: s/he is

on the frontline managing regulatory pressure,

including compliance with BCBS 239, Basel III,

the Asset Quality Review (AQR) and European

Banking Authority (EBA) stress tests. The CRO

must deliver complex reports at high

frequency. To that end, s/he has an explicit

responsibility from the board to fulfill all

upcoming supervisory requirements. Moreover,

s/he will undoubtedly be summoned by finance

to provide the credit risk data required for

IFRS 9 compliance. As a result, the new issues

facing CROs include managing more granular

data down to the transaction level, moving

towards PIT and introducing forward-looking

components into their analysis.

So, should the CFO or CRO own the credit risk

data used for ECL assessment? Should the data

be duplicated or can it be shared? Will CEO

arbitrage be necessary or should there be a

chief data officer (CDO) position to decouple

data sourcing and usage (see the chart below)?

Options Main benefits Main challenges

CFO owns the data Firsthand IFRS 9 user

Controls everything

Provisions managed dynamically

Understanding and using risk data

CRO owns the data Maintains coherence

Regulatory compliance

Knows risk systems

Complying with accounting

requests

Duplicate sets of data

(one for CRO and one

for CFO)

Both CRO and CFO own

Can implement different

frequencies, perimeters and

content constraints

The possibility of discrepancies

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The current trend is to give ownership of the

data to the CFO because s/he owns the

impairment process and because the data used

to assess PIT measures and the forward-looking

stance are mostly related to the accounting

standard. Nevertheless, CRO supervision is

usually granted.

AxiomSL’s state-of-the-art dynamic data model

makes it possible to implement any of these

options. For instance, giving the CFO

ownership of credit risk data can be done

without changing the risk IT infrastructure.

CROs and CFOs are both on

the frontline managing

regulatory and accounting

changes, including compliance

with IFRS 9 and Basel III


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4. Reconciling conflicting regulatory and

accounting demands

IFRS 9 introduces a ‘naïve’ and refreshing view

of how to assess credit risk. This includes using

past and present data as well as available

forecasts, assessing risk based on the whole

life of transactions and properly including

diversification effects.

In contrast, even the most sophisticated

current regulatory approach, the Internal

Ratings-Based (IRB) Approach, does not offer a

proper way to assess credit risk. This

methodology, unchanged for the last 12 years,

assesses credit risk using through-the-cycle

(i.e. backward-looking) measures. It only looks

at a one-year time horizon regardless of the

real maturity of the transactions.

Diversification and concentration effects are

still based on an over-simplistic asset

correlation methodology. Recovery risk

assessment is collapsed into one single figure,

the downturn LGD.

Facing the tsunami of changes that are to

come, competent authorities are trying to stay

in control. The ‘Guidance on Credit Risk and

Accounting for ECLs’ was published by the

BCBS in December 2015. Regarding loan

portfolios, it adds to and reinforces the IFRS 9

requirements. The guidance was developed

based on the principle of non-objection by the

International Accounting Standards Board

(IASB) and, consequently, it remains quite soft.

In particular, even though it does mention the

discrepancies between the approaches

(“…regulatory capital models may not be

directly usable in the measurement of

accounting ECL due to differences between the

objectives of and inputs used for each of these

purposes” [Guidance #9]), it gives no

indication of how to overcome them.

Moreover, the same vocabulary is used across

the accounting and regulatory frameworks, and

is certain to be a source of confusion.

Commonly used parameters of credit risk

assessment, such as exposure at default (EAD),

PDs, LGDs, correlations and expected loss (EL),

are used indiscriminately, despite the fact that

they represent different concepts and realities

(see box below).

Regulatory view IFRS 9 view

EAD expected value one year ahead

(typically nominal + one year of interest)

EAD = net present value (NPV) for two of the

three asset classes

PDs through-the-cycle PDs point-in-time

One-year PD Multi-year PDs

Downturn LGD LGD distribution

Best ‘worst’ case Weighted average scenarios

One-year time horizon One-year and lifetime

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It is therefore essential to have explicit definitions, a

well-defined data model and attributes that remain

attached to the data wherever it is sent or used.

There are two features of AxiomSL’s state-of-the-

art, flexible data model that are particularly

important for fulfilling the above requirement: on

the one hand, every data source is clearly identified,

named and mapped in the data model; and on the

other, even when delivered within complex

reporting, the data remains in its source

environment and is not duplicated. This data lineage

avoids the risk of losing track of the data’s nature

and identity. It also facilitates data mining and

auditing, and guarantees the reliability of the

reporting, all the while allowing for quick checks and

controls at any stage of the reporting process.

It is essential to have

explicit definitions, a

well-defined data model

and attributes that

remain attached to the

data wherever it is used

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Conclusion: IFRS 9 calls for a game-

changer: AxiomSL

Fintech is said to be a game-changer - and it

really is. Data has become a critical asset for

all financial institutions, and Fintech

companies now give firms the ability to

manage it more efficiently than ever.

IFRS 9 is premised on widespread adoption of

Fintech - its requirements would have been

unthinkable just a few years ago, before

technology played such a significant role at

financial firms.

As demonstrated above, IFRS 9 will require:

an ever-increasing quantity of more

granular data

more frequent data updates to support the

PIT approach

the challenge of sharing the same data

between users with different objectives and

concerns

and the automation of complex processes,

such as ECL calculations for impairments,

without compromising data lineage and

auditability.

AxiomSL, the leading global provider of

regulatory calculation and reporting solutions,

has anticipated these challenges and offers a

robust technology platform that is fully

equipped to implement IFRS 9. The fully

integrated platform has been designed to

enable financial firms to make the best

decisions in terms of organization and

modeling, while reducing the time and effort

needed to access and manage the relevant

data.

External ECL models can be easily integrated

into the AxiomSL platform. AxiomSL has

formed a partnership with AlgoSave and has

integrated its model, which assesses ECL using

historical financial data, current market data

and economic forecasts. The model has been

field-tested in the asset management industry

and is now being made available to other types

of financial institutions. In this way, AxiomSL

offers a compelling solution to the challenges

presented by IFRS 9.


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About the author

Shlomo Cohen, Risk SME, AxiomSL

Shlomo Cohen has more than 20 years’ experience in risk, regulation and

finance in the banking industry. As AxiomSL’s Risk SME, he is focused on

IFRS 9 and related requirements, such as CECL.

Before joining AxiomSL, Shlomo worked at Dexia bank for 12 years, where

he was responsible for economic capital and Basel II-III Pillar 2. In this

role, he worked on a wide range of issues, including capital allocation,

ALM, risk appetite, budgeting, macro-prudential regulation, and sovereign

and systemic risk. Prior to Dexia, he was founder and CEO of FTM, a

management consulting firm dedicated to enhancing the shareholder value

of financial institutions.

A graduate of MIT, Shlomo is a regular speaker at industry conferences and

seminars. He has contributed articles to many publications and lectures on

risk and finance.

About AxiomSL

AxiomSL is the leading global provider of regulatory reporting and risk management solutions for

financial services firms, including banks, broker dealers, asset managers and insurance companies. Its

unique enterprise data management (EDM) platform delivers data lineage, risk aggregation, analytics,

workflow automation, validation and audit functionality.

The AxiomSL platform seamlessly integrates clients’ source data from disparate systems and

geographical locations without forcing data conversion. It enriches and validates the data, and runs it

through risk and regulatory calculations to produce both internal and external reports. The platform

supports disclosures in multiple formats, including XBRL. The unparalleled transparency offered by

the high-performance platform gives users the ability to drill down on their data to any level of

granularity.

AxiomSL’s platform supports compliance with a wide range of global and local regulations, including

Basel III capital and liquidity requirements, the Dodd-Frank Act, FATCA, AEI (CRS), EMIR,

COREP/FINREP, CCAR, FDSF, BCBS 239, Solvency II, AIFMD, IFRS, central bank disclosures, and both

market and credit risk management requirements. The enterprise-wide approach offered by AxiomSL

enables clients to leverage their existing data and risk management infrastructure, and reduces

implementation costs, time to market and complexity.

AxiomSL was voted Best Reporting System Provider in the 2015 Waters Rankings and was highlighted

as a ‘category leader’ by Chartis Research in its 2015 Sell-side Risk Management Technology report.

The company’s work has also been recognized through a number of other accolades, including success

in the Best Reporting Initiative category of the American Financial Technology Awards and the

Customer Satisfaction section of the Chartis RiskTech100 rankings.


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Contact

Americas

Email: [email protected]

New York world headquarters: 45 Broadway, New York, N.Y. 10006

Tel.: +1 (212) 248-4188

Website: www.axiomsl.com

APAC


Singapore head office: 14 Robinson Road, #10-01 Far East Finance Building, Singapore 048545

Tel.: +65 6513-0391


EMEA


London head office: 125 London Wall, London EC2Y 5AJ

Tel.: +44 (0)20 3823 4600


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Edited by: Nicholas Hamilton

Designed by: Laura Harland

Copyright ©2016 AxiomSL

http://www.axiomsl.com/


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