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The CFO's Guide to Natural Language Generation INTRODUCTION

If there’s any area within an organization that's ripe for automating manual, time-consuming tasks, it’s Finance. As they try to keep up with increasing amounts of data and stringent reporting cycles, many Finance professionals are turning to Natural Language Generation (NLG), a subfield of Artificial Intelligence that automatically analyzes, interprets, and communicates relevant information from data at scale.

This paper outlines the optimal starting points for NLG within Finance, a framework to assess organizational readiness, and tangible examples based on the report type, audience, and problems financial analysts are looking to solve.

The CFO's Guide to Natural Language Generation 12 July 2019

TABLE OF CONTENTS:

• Introduction

• The Case for Language ina Numbers-Rich Function

• Identifying the Best Placeto Start with NLG

• Practical Examples ofNLG in Finance

• Conclusion

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THE CASE FOR LANGUAGE IN A NUMBERS-RICH FUNCTION

Despite being a numbers-driven group, effective Finance organizations understand they can deliver additional value when communicating the story behind the numbers to their business counterparts.

Yet, according to Deloitte’s in-depth global survey of over 600 senior finance professionals, most organizations aren’t there quite yet. In almost half of the businesses surveyed, Finance spends a majority of its time creating and updating reports, despite wanting to spend more time on strategic business-facing activities (see Figure 1).1 Those familiar with typical financial reports won’t be surprised by this. Finance is wrought with ever-increasing amounts of data which needs to be analyzed and reported on during regular cadences to various audiences, each requiring different levels of interpretation.

Finance spends the majority of its time creating and updating reports, despite wanting to spend more time on strategic business-facing activites.

1 Deloitte: Developing Insightful Management Reporting, Opportunities and Challenges for CFOs. https://www2.deloitte.com/content/dam/Deloitte/uk/Documents/finance/deloitte-uk-management-information-report-2016-final.pdf

Figure 1: Current and preferred time Finance spends on activities, percentage of respondents from Deloitte Survey of over 600 finance professionals.

NLG is meant to disrupt and ultimately reverse these findings. Similar to the tasks a financial analyst undertakes, NLG analyzes data, interprets the most interesting and important facts, and writes an audience-specific commentary. NLG is being used across Finance to automate executive summaries, P&L statements, and variance analyses. It can even be integrated into FP&A dashboards to explain insights from visualizations in natural, easy-to-consume language.

Time-savings is not the only benefit of automation. By utilizing NLG, organizations can also deliver a consistent, objective view of the performance drivers within their data in a scalable fashion. This is increasingly critical for enterprises moving internal reporting to shared service centers that may not sit within the business and therefore cannot easily pick up the “why” behind the numbers. NLG helps ensure that all audiences can quickly understand the meaning of the data, no matter how familiar they are with the data itself.

Drawing from the same data, automated narratives can also be personalized towards a specific audience. For example, the technology can produce a bottom-line focused takeaway for an executive or a more detailed synopsis for a regional manager, specific to his or her region. The concept of automating customized narrative reporting at scale is catching on in enterprises that want to enhance service levels without needing to increase costs.

IDENTIFYING THE BEST PLACE TO START WITH NLG

Due to increasing demands for analytics and reporting within Finance, the opportunities for NLG can seem endless. In order to identify the optimal starting point, it’s helpful to equate implementing the technology with training a new analyst. Instead of throwing messy data at the new hire and instructing him to find something interesting, it’s more likely that you would direct him to identify and communicate relevant findings within an accessible dataset (For example: Call out the drivers of financial performance for Q4).

The same approach should be taken with NLG. The most successful NLG use cases are driven by a clear communication goal, with readily accessible structured data, where the benefits of automation can drive value (Figure 2).

When assessing Level of Value, ask:

1. Commentary Richness: To what extent areexecutive decisions driven from commentary?

High-value reports are often mission-critical, in that they contain relevant information that contribute to strategic decision-making.

2. Frequency: How frequently is the reportpublished?

Machine-generated reports do not need to be reconfigured every time the underlying data changes. As such, they are well-suited for reporting cadences that are often delivered at a high-frequency.

3. Distribution: Who is the intended audience forthe report?

To increase adoption of reports, it’s necessary to ensure they can be widely distributed. NLG output can be easily integrated into existing data analytics tools or information portals to maximize readability (and subsequently, action).

4. Scalability: To what extent can the same logicbe leveraged for other reports?

In order to achieve custom reporting at scale, it is ideal if the same data and general logic can be used for more than one report type.

When assessing Level of Complexity, ask:

1. Data Consistency: To what extent is theunderlying data already modeled around keybusiness dimensions and hierarchies?

NLG works with structured data: data that is already in a machine-readable format or can be transformed into one (i.e., csv, spreadsheets, JSON).

2. Report Consistency: To what extent will thecommentary structure remain constant fromperiod to period?

NLG applications are driven by the goal of what you want to communicate. If this goal changes or widens in scope, therefore changing the commentary structure, additional configuration may be required.

3. Ownership / Scope: To what extent does theproduction of a report involve a single person, orfew groups?

NLG is often used to streamline and standardize reporting processes that can involve multiple groups, however multiple owners can elongate deployment timelines. It is advised to have a sole owner and specific scope outlined clearly.

4. Logic & Ruleset: How straightforward is thelogic to determine drivers of commentary?

Don’t expect NLG applications to perform the data analysis tasks that an analyst would not be able to perform. Particularly for the first automated report, it’s helpful if the logic is relatively straightforward.

Figure 2: When prioritizing which reports to automate, the value criteria should be high and the level of complexity should be relatively low.

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PRACTICAL EXAMPLES OF NLG IN FINANCE

Not all reports within Finance are created equal. There are different uses and context for different types of communications. Therefore, it is important to match NLG functionality with the commentary type and the intended audience.

Here’s a snapshot of various narrative reports for various audiences within Finance:

Report Type Audience Information Value Achieved

Executive C-Level Summarization of performance, relational explanations from established business drivers

• Consistency oflanguage

• Prioritization ofrelevantinformation

Management FP&A Leaders Standard, repeatable reports that are generated consistently

• Faster startingpoint for insight

• Easily shared• Consistency of

language

Operational Analysts Sorted and/or summarized transactional data used in analyzing performance and drivers of change (e.g., A/R data)

• Time-savings• Operational

efficiency

Detail Ad-Hoc Requestors Point in time presentation of metrics for a single member (Customer, Channel, Geography)

• Faster ad-hocanalysis andquerying

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A Fortune 50 company is deploying an NLG solution as a pillar of its FP&A transformation. The company identified an opportunity to streamline commentary generation across its Financial Planning & Analysis group, most of which focused on business driver analysis, budget variances, and segmentation. By employing NLG to draft the reports, increased efficiencies will be realized across the organization. In addition to the operational efficiency gains, the analysts also appreciate how automation relieves some of the stress associated with close of month reporting, as well as the newly enabled grammatical and language consistency across the reports.

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At another company, the global Finance organization has automated monthly financial reporting packages for its emerging markets regions. Prior to using NLG, the client needed to engage more than 40 analysts around the world, which created multiple hand-offs, and as a result, a lot of complexity each month. Using Narrative Science’s NLG platform, Quill, the organization could create the first draft in 10 minutes rather than several weeks.2

CONCLUSION

In order for Finance to be able to spend the time required to drive institutional change within the business, it may be necessary to partner with intelligent technologies that automate manual, time-intensive tasks. NLG is one such example, and it is being used to support enterprise Finance transformation initiatives by increasing operational efficiency, achieving consistency and standardization in reporting, and accelerating time to insight. NLG is optimal for communicating relevant information within data when there is a clear communication goal, accessible structured data, and the need for scale.

As used in this document, “Deloitte” means Deloitte Consulting LLP, a subsidiary of Deloitte LLP. Please see www.deloitte.com/us/about for a detailed description of our legal structure. Certain services may not be available to attest clients under the rules and regulations of public accounting.

This publication contains general information only and Deloitte is not, by means of this publication, rendering accounting, business, financial, investment, legal, tax, or other professional advice or services. This publication is not a substitute for such professional advice or services, nor should it be used as a basis for any decision or action that may affect your business. Before making any decision or taking any action that may affect your business, you should consult a qualified professional advisor. Deloitte shall not be responsible for any loss sustained by any person who relies on this publication.

Copyright © 2018 Deloitte Development LLC. All rights reserved.

Prior to using NLG, the client needed to engage more than 40 analysts around the world. With NLG, the organization could create the first draft in 10 minutes rather than several weeks

Adrian TayManaging Director, US Digital Finance Analytics & Insights LeaderDeloitte Consulting LLPTel: +1 213 688 3212Email: adtay@deloitte.com

Zach DworkisSenior Manager, US Digital Finance Deloitte Consulting LLPTel: +1 303 513 7145Email: zdworkis@deloitte.com

Dux GandhiManager, US Digital Finance Deloitte Consulting LLPTel: +1 512 698 1909Email: dugandhi@deloitte.com

Matt BramsonVP of Business Development Narrative ScienceTel: +1 312 219 8526 Email: mbramson@narrativescience.com

2 The Wall Street Journal: Digitizing Finance Communications with Natural Language Generation. http://deloitte.wsj.com/cio/2017/10/05/digitizing-finance-communications-with-natural-language-generation/?mod=WSJBlog