[PDF] Accenture Trade Finance Processing Automation





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Accenture Trade Finance Processing Automation

the benefits of RPA in trade finance. Trade transaction involves validation and data capture across multiple applications including core banking



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PROCESSING

FS

PERSPECTIVES

AUTOMATION IN TRADE FINANCE

CONTENTS

3 8 14 164
10 15 17 19

Summary

EXECUTIVE SUMMARY

US$24 trillion.

1 we explored how combining proven market technology with AI and machine learning can help banks master key

THE OPPORTUNITY:

TRADE FINANCE MARKET & TECHNOLOGY

grow at an annual rate of up to 6%

US$48 billion by 2021

Figure 1: Challenges in optimizing Trade Finance Processing

INFLEXIBLE BUSINESS MODELS

PAPER-BASED AND LABOR-

INTENSIVE PROCESSES

REGULATORY AND

COMPLIANCE CONSTRAINTS

POORLY INTEGRATED

LEGACY IT SYSTEMS

IMPACTS

Limitations in growing client base and

expanding range of products/services

Fintechs and tech platform providers

providing innovative solutions to clients leading to disintermediation of bank-client relationship

Longer transaction turnaround time

High handling & storage costs

High error rates from manual

Lack of standardization

Operational risk due to staff turnover

High costs due to manual compliance

screening with non-integrated platforms (eg. World Check, blacklists)

Non-standard reporting processes

and formats of adhoc transactions to regulators

Slow adoption of new technologies

Process breakage and complexity in

tracking due to manual handoffs

Reconciliation of data across systems

Transaction

requestMedium/long- term visionVision & strategy objectives

Clear go-

to-market strategy aligned to objectives

Strong

fundamentals as key enablers for growthClient &

Markets

Customer-centric

end-to-end approchProducts &

Pricing

Strong risk &

regulation framework

External collaboration

Organization cultureChannels &

Distribution

IT as a business

enabler

Streamlined & scalable

operating modelRevenue targets

Risk appetiteCapital

requirements & guidelines

Client

Markets

Sectors

Products

Pricing

Channels

Distribution

Core system

modernization

Digitalization

Digitalization

Deep insights

Automation

Sourcing options

is facing a major technology overhaul. Banks are in a race to go digital. While many are trying to digitize their front end, others are collaborating to build blockchain-based networks for automating trade. However, the results are nascent. Only either just beginning to develop their capabilities or have plans to invest in technology in the coming years (see Figure 2).

THE CHALLENGE:

Long transaction turnaround times

High handling and storage costs

Lack of process standardization

Operational risks due to highly manual processes

the potential for a

60-70% cost outtake and a reduced turnaround time from up to a week to 10-15

minutes per stage when automation is applied

GET TING LABOR-INTENSIVE PROCESSES RIGHT

TANSACTION PROCESSING VALUE CHAIN

CRITICAL INEFFICIENCIES

data

Digitization of document

Documentary checks

Compliance checks

Discrepancies between

documents

Non-compliance of UCP

trade rules

Compliance screening hits

Transaction

request release

Reduced costs

by 60-70%*

Reduced turnaround time

From 2-5 days to 10-15 minutes per stage*

1 2 3

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ML algorithms will take such decisions into

account going forward and add it to the historical data set, thus enabling them to “decide" automatically in future occurrences. Expert decision-making and judgment are required for these process steps and are vital to effective risk management. While the deterministic rule-based steps during (pre)processing are natural candidates for automation by traditional RPA, the judgmental processing steps, such as validating compliance to Uniform Custom and Practice for Documentary Credit (UCP) rules, legality and terms matching, remain very labor-intensive. reduction. This is where enhancements to the traditional RPA approach come into play. Table 1: Complementing traditional RPA with new technologies to bring additional gains in Trade Finance

What are the

prerequisites for a successful RPA implementation?

How can you address

the challenges and increase gains from

RPA investments in

Given the prerequisites, what makes

Table 2: Overlaying of RPA, ML & Intelligent OCR technologies in the Letter of Steps

RPAMLIntelligent

OCR

Therefore, achieving 100%

automation is hardly feasible.

15 months

can I measure them? Accenture has helped banks automate pre-processing and included completing pre-screening activities; retrieving automation; and generating reference numbers. Processing activities included checking terms and conditions, including sanction checks. We also helped capture payment details into underlying banking applications before transaction authorization takes place. We established that up to 60% of processes were suitable for automation. The remainder involved retrieving details from third-party websites, manual intervention and process exceptions.

DISCOVERY PHASE

IMPLEMENTATION PHASE

the journey? The power of bringing business and IT change teams together early

The power of ongoing involvement across teams

The power of aligning technology across all areas

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We have more than 1,200 automation experts and

over 9,000 automation and bot deployments.

Cognitive Robotic Process

Automation (CRPA)

Cognitive Monitoring

Agent (CMA)

Cognitive ReaderCAPABILITYDESCRIPTION

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