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Deep Learning WP
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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
1Deep Learning Wp
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.
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