Deep Learning WP

contents

Artificial Intelligence: Technology of the Future 1

What is AI? 1

Machine Learning — Path to achieve greater heights with AI 2

supervised machine learning 2

Unsupervised machine learning 2

Deep Learning — Unlimited AI potential 3

Why Deep Learning? 3

Deep Learning in FinTech 4

Impact on the Tax Function 5

Automation of Repetitive tasks 5

Accurate Decision-Making 5

Automated customer support 6

Fraud detection 6

compliance and Risk Management 6

Predictive analytics 6

VATBOX – AI-Driven Global VAT Recovery 7

How VATBOX uses Deep learning 8

computer Vision 8

optical character Recognition (ocR) 8

Data extraction 8

natural Language Processing (nLP) 8

Deep learning delivers VATBOX Advantage 9

Unprecedented data integrity and validation 9

tight governance and compliance 9

360-degree VAt visibility insights 9

Summary 10

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ARtIFIcIAL InteLLIgence: technoLogy oF the FUtUReArtificial intelligence (AI) holds much promise for the future of corporations.

According to Pwc, business leaders believe AI will be fundamental in the future,

with 72% considering it a “business advantage.” With its almost unlimited

capabilities, the impact of AI technologies on business is projected to increase

labor productivity by up to 40%. With huge investments in AI—between $26 billion

and $39 billion—in 2016 alone, according to McKinsey, AI has emerged as the

backbone to all advanced forms of technology.

WhAt Is AI?AI is typically defined as a machine’s ability to perform the cognitive functions

typically associated with human minds, such as understanding, reasoning,

problem solving, learning, and even interacting with the surrounding environment.

AI has the potential to take over the mundane tasks employees currently handle,

freeing their time to be more creative and perform the strategic tasks that

machines cannot. The explosion of AI is a direct result of the availability of large

and diverse digital datasets, improved algorithmic capabilities, and the huge

increase in mathematical computing power that has characterized recent times.

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MAchIne LeArnIng — PATh To AchIeVe gReAteR heIghts WIth AIFurther advances in AI have been achieved by applying machine learning to vast

data sets. Machine learning is the practice of using algorithms to analyze data,

detect patterns, and then use the data to make predictions or recommendations.

Machine learning replaces the practice of hard-coding programming instructions

that enable software to accomplish a particular task. Instead, the machine is

“trained” using masses of data and advanced algorithms to “teach” it how to

perform the desired task and to help it improve over time. Machine learning can be

divided into two learning-based categories: supervised and unsupervised.

Supervised machine learning: Just as a teacher supervises a classroom and provides

the correct answers as part of the learning process, with supervised machine

learning, the output datasets are provided in order to train the machine’s algorithms

to deliver the desired outputs. The algorithm repeatedly makes predictions based on

the training data and is corrected by the human supervisor. The learning stage stops

when the algorithm achieves an acceptable level of performance. The majority of

today’s practical machine learning utilizes the supervised learning method.

Unsupervised machine learning: In contrast, with unsupervised learning no output

datasets are provided. This means there are no correct answers or teacher in the

classroom. Instead, the data is clustered into different classes, and algorithms

must self-discover the underlying structure or relationships in the data in order to

learn more about the data.

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Deep Learning — UnLimiteD ai potentiaLas the most advanced form of ai, deep learning enables independent learning of

massive data sets. Unlike classic methods in which a human expert must define

rules and attributes, deep learning can learn directly from data without human

intervention, either supervised or unsupervised. it can process a wider range of

data resources, and often produces far more accurate results than traditional

machine learning methods. most deep learning methods use neural network

architectures, with the term “deep” referring to the number of hidden layers

within the neural network. traditional neural networks only contain 2-3 hidden

layers, while deep networks can have as many as 150 layers. Deep learning

processes its data via these multiple layers, which learn increasingly complex

details of the data at each layer. a deep learning platform can then make a

determination about the data, learn if its determination is accurate, and utilize

the information it has already learned to make predictions about new data. For

example, once it learns what an object looks like, it can recognize the object in a

new setting.

With approximately 2.5 quintillion bytes of data created every day—and growing

– algorithms have more and more exposure to data examples that will help them

learn. this translates into a greater capacity for insights and higher accuracy

levels. Deep learning systems driven by these masses of data have reduced

computer error rates in some applications—for example, in image identification—

to about the same level as humans.

Deep learning has enabled a wide range of practical ai applications. Some

examples of applications powered by deep learning include preventive

healthcare, autonomous cars, virtual assistants and smart homes. in fact, deep

learning has already made a significant impact on every industry – including

retail, healthcare, automotive, defense, manufacturing, utilities, and financial

services.

these industries have harnessed deep learning’s potential to improve forecasting

and sourcing, automate and optimize operations, develop targeted marketing and

pricing practices, and enhance the overall user experience.

Why Deep Learning?• Achieve higher accuracy at a

practical speed• outperform traditional

methods of voice/facial recognition

• Predictive analytics• Business optimization• Targeted sales and marketing• enhanced customer

experience

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DeeP LeARnIng In FIntechThe use of deep learning neural networks by financial technology firms – FinTech

– has clear benefits across the financial spectrum. As an industry that relies

heavily on algorithms, repetitive manual tasks and advanced computing, finance-

based functions are turning to machine learning to help them work smarter rather

than harder. AI and deep learning have proven effective in finance-based solutions

by applying deep-learning aspects of human intelligence at a beyond-human scale.

the technology is seen as strategic to the future of the industry, such that Fintech

companies are among the leading adopters of AI. According to FinTech provider

FIS global, financial services firms that pave the way in adopting new technologies

outperform their peers in revenue growth.

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IMPAct on the tAx FUnctIonWhen it comes to the tax arena, applying the largely unchanged direct and indirect

tax legislation of the 20th century to the activities of today’s digital economy is

a commercial challenge faced by many businesses. The oecD’s base erosion

and profit shifting (BePS) Project has further altered the global tax landscape by

implementing a tax framework that has been adopted by over 84% of the total

world economy. This represents an opportunity for AI-based technology that

simplifies and eases global compliance. In fact, according to e&Y’s 2016 TaxTech

survey, 84% of participants voted that technology is the most important factor in

improving the effectiveness of the tax function. here are some possibilities:

Automation of Repetitive Tasks: Automation is transforming the tax function by

delivering improved transparency, controls and efficiencies, while mitigating risks,

reducing costs and delivering more accurate results. By eliminating monotonous

tasks that take valuable time from a tax professional’s day, organizations can

better use their employees’ time and skills for more strategic activities, and

minimize costly errors often associated with manual and repetitive tasks.

Accurate Decision-Making: Deep learning allows for data-driven decisions at a

lower cost. Machines process volumes of business and customer-related data,

analyze the data and deliver real-time recommendations. A well-known example is

IBM Watson’s partnership with h&r Block. Using Watson’s AI-based capabilities,

h&r Block ensures that those filing taxes receive as many tax deductions as they

can legally qualify for via Watson’s understanding of the US tax code, which has

no less than 74,000 pages and is updated every year. customers benefit from

Watson’s ability to provide deep insights built from over 600 million data points.

As h&r Block’s tax professionals use Watson, the platform learns from each

interaction, getting smarter and smarter every day, and enabling the partnership

to deliver highly personalized tax solutions.84% of participants voted that technology is the most important factor in improving the effectiveness of the tax function.

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Automated Customer Support: customers-facing systems such as IVR or

chatbots can provide human-like customer service or expert financial advice via

an automated interface, saving the company service costs. Deep learning further

improves automated customer support systems by accessing and processing data,

recognizing patterns, and interpreting behavior in a manner that mimics a human

agent. gartner predicts that by 2020, 85% of all customer interactions will no longer

be managed by humans. By making customer support channels more human-like,

financial institutions can provide enhanced, yet cost-effective, support.

Fraud detection: By leveraging deep learning, algorithms identify patterns in

masses of data to help detect and flag suspicious activities, potentially preventing

thousands of costly fraudulent transactions. By comparing each transaction

against account history, algorithms can quickly assess the transaction against

thousands of data points and make a determination whether or not the attempted

activity is unusual in any way. With their self-learning abilities, deep learning

systems can then adapt to changing habits and further enhance the detection

mechanism over time.

Compliance and Risk Management: While traditional software applications

determine risk based on static information from financial reports, machine learning

technology has the added ability to analyze risk based on current market trends and

even news-related items. With tax authorities cracking down on compliance, deep

learning can play a significant role in the mitigation of risk.

Predictive analytics: When applied to the tax function, predictive analytics can

directly impact overall business strategy, resource optimization and revenue

generation. Predictive analytics process a massive amount of data to find patterns

and predict insights, enabling companies to better understand the needs of each

individual customer.

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VATBoX – AI-DrIVen gLoBAL VAt RecoVeRyAs a premier collaborative FinTech company, VATBoX has successfully

streamlined the VAT recovery process, providing businesses and financial

institutions with unrivaled visibility, compliance and data integrity. Leveraging

the cloud and utilizing full automation, VATBoX exhibits complete control of a

company’s VAT spend, while making the recovery process more productive and

yielding higher returns.

With the system’s drill down analytics, companies easily gain visibility into VAT

spend for all entities involved. The entire VAT value chain can be controlled from a

single interconnected system. VATBoX provides companies with simplified access

into every single line and VAt expense, bringing structure to the data and ensuring

governance across all entities. This results in an organization gaining complete

control and transparency of VAT dealings.

VATBoX’ areas of expertise include Foreign VAT, Domestic VAT, Accounts Payable

(identifying, validating and claiming back all foreign AP VAt), Inter-company VAt,

conventions and event-related transactions (abroad and domestic), shipping

- Delivered Duty Paid and tooling (ensuring that all tools and equipment were

properly charged and correctly identified).

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nLP makes it possible for computers to read text, hear speech, interpret it, measure sentiment and determine which parts are important.

HoW VatBoX USeS Deep Learningas an ai-driven platform, VatBoX relies on advanced deep learning techniques

to streamline the Vat recovery process. these include:

Computer Vision: Computer vision is a field of AI that aims to give computers

a visual understanding of the world. the goal of computer vision is to imitate

human vision using digital images through image acquisition, processing,

analysis and understanding. Finding and recognizing objects within images

or videos includes several tasks, such as: classifying objects, localizing the

object within the image, distinguishing the object from other objects, and

identifying parts within the object. Deep-learning-based computer vision offers

incredible accuracy that makes it a core technology for VatBoX, especially for

pre-processing images of receipts to improve oCr results, and for detecting

suppliers based on their logos.

Optical Character Recognition (OCR): oCr transforms an image into editable

document text. However it must overcome a number of challenges, including

font and orientation sensitivity, language model bias, character miss-fit, optical

quality such as blurring or lack of contrast, and print quality. VatBoX applies

fuzzy logic pattern matching that learns from phrase similarities and relies on as

many words as possible for a task. VatBoX’s platform learns from many diverse

examples, such as font, quality, languagse, augmented and synthetic samples,

resulting in advanced oCr processing capabilities. For example, when multi-line

invoices are submitted, VatBoX uses oCr to break down the single invoice into

multiple smaller ones, for easier classification and processing.

Data extraction: VatBoX’s platform automatically extracts data from invoices

and populates the fuzzy text into the correct field within the system, such

as destination country, document type, etc. the system auto-recognizes the

percentage of valid evidence collected, and assigns a confidence level score to

the data. this results in better detection of issues before reclaim submissions,

and enhanced levels of compliance. For example, the system an extract data

from a credit card slip and detect a missing field for the product/services

provided, resulting in an alert for the invoice to be re-issued.

Natural Language Processing (NLP): nLp helps computers communicate

with humans in their own language and scales other language-related tasks.

For example, nLp makes it possible for computers to read text, hear speech,

interpret it, measure sentiment and determine which parts are important.

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DeeP LeARnIng DeLIVeRs VATBoX ADVAnTAgeUnprecedented data integrity and validation: truly automatic data processing

seamlessly manages the details of every single invoice or receipt – no matter how

small – and reconciles this data to the customer’s total spend. VATBoX integrates

smoothly with all eRP and expense management applications, ensuring a rapid

setup, flawless data consolidation and mapping.

Tight governance and compliance: VATBoX maintains a database of all current

and historical VAt rates, application rules and reclamation procedures across

all international and domestic jurisdictions ‒ updated in real-time ‒ ensuring

the highest levels of data security and compliance. Sophisticated tools greatly

enhance the transparency of a company’s tax policies, leading to stricter internal

and external compliance, and reduced exposure and risk.

360-degree VAT visibility insights: VATBoX provides its clients with full insight

with unprecedented analytics, segmentation, instant record retrieval, useful

reports, detailed audit trails, and visibility, and ultimately – higher returns. A

sophisticated dashboard and drill-down analytics provides detailed visibility into

the status of every invoice, resulting in a materially improved VAt recovery process

for the customer.

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SUmmaryArtificial intelligence and deep learning have transitioned from experimentation

to real-world application, helping businesses boost productivity and cut costs.

the technology continues to evolve and improve, using the myriads of data and

processing capabilities emerging from today’s digital world, delivering greater

insights and accuracy across a wide spectrum of use cases and applications. It

has been especially impactful on the tax function, where manual and repetitive

tasks can be transformed by cognitive computing. VATBoX, a Fintech company

focusing on indirect tax, has revolutionized the VAt landscape with its intelligent and automated VAT recovery solution. Learn more about how VATBoX’s AI-

driven solution can help your company thrive in today’s complex financial times.