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Artificial Intelligence

Big opportunities

$15.7 trillion that’s the global economic growth that AI will provide by 2030, according to PwC research. Who will get the biggest share of this prize? Those who take the lead now.

Be a first mover so it doesn’t leave you behind. With AI pilots and projects live all over the globe, and new use cases added daily, at PwC we’re already veterans at helping clients navigate the new world of AI safely and strategically.

Are you interested in the real value of AI for your business and how to capitalize it?
 

Read PwC's Global Artificial Intelligence Study

 

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What is AI?

Artificial Intelligence (AI) is a set of algorithms and methods designed to solve problems that traditionally require human intelligence.

Machine Learning (ML) represents a branch of AI, which enables computers to automate tasks without manually programming the rules (when rules are unknown or too complex).

  • Automated data processing
    produces more consistent, complete and precise results
  • Decision support
    provides users with advanced insights that aid decision-making
  • Anomaly detection
    reveals irregularities invisible in plain human sight

  • Process Automation
    offloads repetitive tasks to machines and speeds up the execution
  • Information extraction
    automatically extracts knowledge from a large amount of unstructured data
  • Anomaly detection
    allows focusing effort on the most suspicious cases

  • Comprehensive checks
    ensure compliance with regulations

Data: your new superpower

Imagine having advanced business analytics and using predictive analytics that give you the ability to predict and react to events before they happen.

Now imagine a data architecture that lets you process all that information instantly, to improve customer insights, build products faster, or spot fraud.

It would be like having a superpower, and as long as we recognize how heavily AI models rely on large quantities of data and the importance of data quality it is a superpower what we can help you capitalize on, since data is an asset you already own.

We’ll help you to analyse your current state, develop a strong data foundation, and then monetise that data and harness the power of the information you hold to optimise business performance and commercialise data opportunities.
 

PwC's Responsible AI Toolkit

Artificial intelligence is already part of many solutions in most of the industries, and looking forward the numbers are only going to grow. It can bring huge benefits, but those benefits come with multiple risks. These risks, however, are manageable. One of the ways of managing these risks is by using PwC's Responsible AI Toolkit.

Your stakeholders, including board members, Customers, and regulators, will have many questions about your organisation's use of AI and data, from how it’s developed to how it’s governed. You not only need to be ready to provide the answers, you must also demonstrate ongoing governance and regulatory compliance.

PwC's Responsible AI Toolkit is a suite of customizable frameworks, tools and processes designed to help you harness the power of AI in an ethical and responsible manner - from strategy through execution.
 

Risks of using AI

AI algorithms that ingest real-world data and preferences as inputs run a risk of learning and imitating our biases and prejudices.

Performance risks include:

  • Risk of errors
  • Risk of bias
  • Risk of opaqueness
  • Risk of instability of performance
  • Lack of feedback process

 

For as long as automated systems have existed, humans have tried to circumvent them. This is no different with AI.

Security risks include:

  • Cyber intrusion risks
  • Privacy risks
  • Open source software risks
  • Adversarial attacks

Similar to any other technology, AI should have organisation-wide oversight with clearly-identified risks and controls.

Control risks include:

  • Risk of AI going “rogue”
  • Inability to control malevolent AI
     

The widespread adoption of automation across all areas of the economy may impact jobs and shift demand to different skills.

Economic risks include:

  • Risk of job displacement
  • Risk of concentration of power within 1 or a few companies
  • Liability risk
     

The widespread adoption of complex and autonomous AI systems could result in “echo-chambers” developing between machines, and have broader impacts on human-human interaction.

Societal risks include:

  • Risk of autonomous weapons proliferation
  • Risk of an intelligence divide

AI solutions are designed with specific objectives in mind which may compete with overarching organisational and societal values within which they operate.

Ethical risks include:

  • Values misalignment risk
     

These risks can be addressed by focusing on five key dimensions during the design and development of AI applications.

Five key dimensions

The foundation for Responsible AI is end-to-end enterprise governance. At its highest level, AI governance should enable an organisation to answer critical questions about results and decision-making of AI applications, including:

  • Who is accountable?
  • How does AI align with the business strategy?
  • What processes could be modified to improve the outputs?
  • What controls need to be in place to track performance and pinpoint problems?
  • Are the results consistent and reproducible?

A proper AI governance foundation will start with strategy and planning across the organisation, but will also take into account existing capabilities and the vendor ecosystem, as well as the unique model development process and model monitoring and compliance.


Organisations should strive to develop, implement, and use AI solutions that are both morally responsible and also legal and ethically defensible.

For (ethical) principles to become actionable, they must be contextualised into specific guidelines for front-line staff. This way organisations may be able to identify the ethical implications of their AI solutions, and the relevant principles that should be taken into account when designing and operationalising AI models, allowing for robust mitigation of ethical risks.

Organisations also must monitor the regulatory environment in which they operate and understand how emerging regulations will shape future business practices.

There are times when it's needed to explain why a particular AI model made a particular decision. A lack of interpretability in AI decisions is not only frustrating for end-users or customers, but can also expose an organisation to operational, reputational, and financial risks.

To instill trust in AI systems, people must be enabled to look “under the hood” at their underlying models, explore the data used to train them, expose the reasoning behind each decision, and provide coherent explanations to all stakeholders in a timely manner.

To be effective and reliable, AI systems need to be:
resilient: next-generation AI systems are likely to be increasingly “self-aware,” with a built-in ability to detect and correct faults and inaccurate or unethical decisions.

secure: the potentially catastrophic outcomes of AI data or systems being compromised or “hijacked” make it imperative to build security into the AI development process from the start, being sure to cover all AI systems, data, and communications.

safe: above all, though, AI systems must be safe for the people whose lives they affect, whether they are users of AI or the subjects of AI-enabled decisions.
 

People perceive bias through the subjective lens of fairness — a social construct with strong local nuances and many different and even conflicting definitions. Therefore it’s impossible for every decision to be fair to all parties, whether AI is involved or not.

But it is possible to tune AI systems to mitigate bias and enable decisions that are as fair as possible and adhere to an organisation’s corporate code of ethics, as well as following anti-discrimination regulations.

In each case, establishing fairness requires businesses to choose their level of comfort in the choices they make, and balance these against the associated costs and wider impacts, which might be negative for some stakeholders.

Our approach

Throughout the years, we have learnt the importance of performing a thorough business analysis followed with a detailed data exploration. The findings from these two important analyses allow us to make a solid foundation for the products in development and help us to fully take advantage from your data with the help of our AI solutions.

Our AI solutions are aligned with PwC's Responsible AI Toolkit, as a result of which the risks, which usually come with using AI, are minimized.

By using the agile approach to deliver products:

  • working prototypes are available since the early stages of the project
  • frequent consultations with the client prevent miscommunication
  • design may be updated during the development based on reviews

 

How we can help?

With the combinations of different tools and methods we can help you to grow revenue, reduce cost, manage risks and even to gain competitive advantage.

Artificial intelligence

We’re helping our clients use AI to do what they do faster, cheaper and more accurately than they’ve ever been able to do. Machines can read terms and conditions. They can predict behaviour on transport systems. They can pick out faces in crowds.

Our AI teams are made up of specialists in cognitive computing, deep learning, machine learning and natural language processing and generation. They’ll help you turn the data you own into living, breathing insight and action in your organisation.
 

Anomaly detection

If something doesn’t look right, it probably isn’t right. But what if you can’t see it? That’s where anomaly detection comes in.

We’ll help you build the technology to spot the outliers that could spell trouble for your organisation. Unusual financial activity or spikes in transactions that could spell fraud. Unexpected fault reports in systems that could turn into defects. Or surprising results in medical reports that shouldn’t go unchecked.

The data is there. You just need to know how to find it.
 

Predictive modelling

If you’ve got the right data, and enough of it, you can predict the likely outcome of any given situation.

We’ve helped clients in the transport sector use big data and predictive analytics to see the impact of maintenance on transport routes - and model how people’s behaviour will change as a reaction. We’ve helped retailers combine insight from their store footprints, logistics and customer behaviour to accurately plan staffing levels, weeks in advance.
 

Simulation

Thanks to data and analytics, you can turn a theoretical situation into a dress rehearsal.

We use technology including virtual studios, gamification and other computer-generated simulations to help our clients answer the question: what if?

We can take advanced analysis and modelling, and apply it to a real-world world scenarios, so you can trial your response. See the effects of shutting down a transport network. Test how prepared you are to react to a systems outage. Simulate what would happen in the face of a major catastrophe.

So you’re ready and prepared when the real thing happens.
 

Problems we have solved

With each new day new problems arise, what motivates us to keep learning to be able to solve them. So far we have already developed solutions for multiple problems, some of them are:

  • Corporate loan clients who are likely to default pose a business risk
  • Our solution provides:
    • precise bankruptcy prediction
    • timely operations
    • reduced risks

  • Support operation suffered from a slow incident resolution, high cost, and poor customer experience
  • Our solution provides:
    • identified pain points
    • identified systemic issues
    • improved prioritization

  • Every year, significant number of EU agricultural subsidies are involved in various irregularities
  • Our solution makes it possible to:
    • verify declaration from satellite
    • detect agricultural operations
    • detect statistical anomalies

  • Client files are often unstructured and require manual processing to extract contained information
  • Our solution provides:
    • minimized manual work
    • extraction of important data
    • smart document routing

  • Differences between unsynchronized accounting records (breaks) have to be manually classified and fixed
  • Our solution makes it possible to:
    • identify and fix breaks
    • analyze each break class

Why to choose our team?

Our team consists of skilled specialists with strong scientific and technical backgrounds, diverse awards, publications and conference talks.

We deliver personalized solutions at the highest quality in short amounts of time.

We have already delivered over 40 projects for some of the largest banks, insurance companies and clients from other industries.

Our solutions are easy to customize, which lengthens their lifespan and broadens their areas of usage.

Contact us

Karsten Hegel

Karsten Hegel

Partner, PwC Slovakia

Tel: +421 911 402 630

Alexander Kabirov

Alexander Kabirov

Senior Manager, PwC Slovakia

Tel: +421 903 350 577

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