How PwC can help
PwC's Data Analytics team is dedicated to providing world-class consulting and services in the field of AI, ML, analytics and data engineering. Our team combines technical expertise with industry knowledge to help organisations make data-driven decisions and unlock the power while utilising AI and ML. With a focus on delivering value to our clients, we offer a range of services, including advanced analytics, DataOps, MLOps, data-focused architecture, building data pipelines and development of custom AI solutions including all the modeling.
By working closely with our Applications Development and Cloud Transformation teams, we are able to provide end-to-end digital product solutions that drive business value.
We are a trusted partner to many of the world's leading organisations, and we take pride in the long-term relationships we have built with our clients. Whether you're looking to improve your data infrastructure or build intelligent systems, our team has the skills and experience to help you succeed.
The four pillars of our DA & AI capabilities
What makes us different is our ability to bridge the gap between AI driven PoC and production-ready, enterprise-grade solutions, benefiting from working closely with our Application Development and Cloud Transformation teams.
Data Scientists and AI Engineers
The people whose specialty is data processing, modelling and experimentation (where models are iteratively developed, trained, and tuned to find the best solution for any given problem) and for whom “GenAI” is not a revolution, but an evolution.
Data Engineers and Data Architects
Accomplishing the first and most important step, properly gathering and processing the data needed to develop AI driven solutions.
Data Visualisers or Data Storytellers
Where the world of data and AI meets our human-centric one, utilising visually stunning UX and design to better understand and use the data.
Data and Machine Learning Operations
Because a successful AI/ML project is so much more than just data processing, and model training and deployment, it is where most of the gap between individual PoCs and production solutions lie.
Our experts
Marek Novotný
Data Analytics Leading Partner
Marek Novotný has 20 years of experience in the tech. industry across the globe. He is recognised as a strong executive within the field of technology. Marek has been engaged in numerous technology oriented projects.
The main areas of his expertise include software development and system integrations, digital products and digital services, artificial intelligence and machine learning.
Tomáš Kuzin
Data Analytics Leader
Tomáš is an experienced Full Stack Data Scientist and AI Engineer with over 8 years of experience. At PwC, he has led, or worked on, a variety of data analytics projects covering ML/AI solutions, data modelling & ETLs, and reporting across multiple sectors and territories.
Tomáš's main areas of expertise are advanced machine learning and data mining with an emphasis on unstructured data processing (Computer Vision, NLP, LLMs), while also being proficient in Data Engineering and programming. His recent primary focus has been on generative AI, especially LLMs and the entire ecosystem around these.
Our services
- Data Science & AI
- Machine Learning Operations
- Data Engineering
- Data Visualisation
Data Science & AI
Data Science & AI
Turns Data into Profit
- The use of insights derived from data to train a predictive model using cutting-edge modeling techniques and tools
- The transformation of a process into mathematical space and the definition of an algorithm for its optimisation
- Unstructured Data Processing (Computer Vision, Text Analytics, NLP and LLMs)
- ML/AI Models validation and monitoring (accuracy, robustness, fairness, stability…)
- Explainability and interpretability of existing ML/AI models.
Machine Learning Operations
Machine Learning Operations
Brings AI/ML models into production
- Designing AI/ML solution architecture
- Turning data science code into robust and stable applications
- Building cloud-native applications with high availability and performance
- Integrating AI/ML solutions with current Enterprise architecture
- Equipping data scientists and data engineers with the latest technologies to unleash their full potential
- Optimizing large language models for efficient deployment and management in production environments
Data Engineering
Data Engineering
Makes data accessible
- The set-up of integration pipelines of individual data sources
- Design, creation and optimisation of data transformation and loading
- Set-up process and environment for code, data, model and metrics versioning
- Geo-data mart and geo-data transformations
- The designing of data structures and matching them with appropriate technologies
Data Visualisation
Data Visualisation
Provides an accessible way to understand, analyse and discover insights from data
- User experience seamlessly integrates data into user journeys
- Graphic design for intuitive and aesthetically pleasing design that respects brand requirements
- Context understanding and data storytelling
- Omni-channel accessibility to publish through web, mobile and other channels
- Embedding and security to make the insights part of a broader product with user-row level access control
References
Responsible AI Toolkit
PwC Product
A global initiative to help our clients build trust and confidence in AI within the organisation, and accelerate future innovation by empowering them to address the risks and challenges associated with AI in a proactive manner.
The Responsible AI Toolkit is an accelerator, currently built to be a modular solution that can be deployed or modified for existing workflows and, may be extended or enhanced as needed. The Czech AI Team played a key role in defining the Toolkit architecture and interface as well as by leading the AI development process.
We have designed the reporting layer and developed the Interpretability and Explainability module of the toolkit.Together with the Czech Application Development team, we brought life to the platform. Continue to the website.
Branch optimization using synthetic population
Banking, Ireland
- Estimating potential customer value in an area to optimise branch location, closure or expansion
- Insights based on socio-economic data from censuses, client segmentation, customer value (calculated using client internal transaction and interest data), footfall and dwell time
- Client internal data used to develop a customer value prediction model (2 steps: classification of customers with non-zero value and estimating the value itself for those customers)
- Use of local census data to create synthetic population
- Areas with high potential are identified by using the synthetic population features and customer value model (trained on the client internal data).
Automated credit scoring
Banking, Czechia
A large bank was undergoing a major digital transformation. One of the critical parts of the client acceptance process, the risk assessment, was still a paper-based weeklong exercise. Also, this scoring model was continuously losing on performance and stability.
Starting from scratch, the project delivered a fully functional scoring system, running on production on client premises. In total, we engineered 500+ features, 10 models and ran a series of workshops for fine-tuning the model and scoring system. We continue to manage the service and support the client post-deployment.
We reached out to industry data providers (telco), tapped into public (online) and government (registers) data sources and augmented these with PwC’s proprietary Geo-Data mart to maximize the level of insights and value possible.
Scoring apps
Banking and Telco, Czechia
Adding new dimension
- Client challenge. A growing Czech bank was looking for the means to improve performance
- PwC Role. Using the PwC GeoDataMart, create a database of hundreds of geospatial features
- How we delivered. The bank almost doubled its client portfolio between 2016 and 2018 observing a significant drop in the default rate due to improved scoring
Telco scoring pioneering
- Client Challenge. One of the largest Czech banks was lacking a model which would effectively assess the riskiness of new customers
- PwC Role. Designing and arranging the ecosystem where banks and the telco company can share customer data
- How we delivered. We defined the environment for sharing telco customer data to support the application process in a bank using score cards developed by us
Improving performance
- Client challenge. A Czech branch of a global top 1000 company was challenging the performance of its own application scoring models
- PwC role. We partnered with the telco operator and on the data of its customer developed model supplying the behavioural scoring.
- How we delivered. Our score card added more than 20% accuracy on top of the bank's scorecard.
Illness prediction model and automated dashboard
Automotive, Czechia
For a large Czech car manufacturer, we implemented an illness prediction automated process. The goal was to lower the impact and costs of illnesses through improved workforce planning. On a daily basis, anonymised data from ERP is transformed and loaded onto Cloudera.
Outputs of the predictive models are stored and historicised in the same database. Aligned with client's HR reporting standards, the results are presented as Power BI dashboards to end-users.
When illness levels begin to rise, this enhanced monitoring and early-warning system enables our client to intervene and take action at a lower cost compared to a reactive approach.
Analytics transformation
Global insurance group, EU
Our client, a large global insurer operating across 10+ countries, made a decision to internalise data science capabilities in order to preserve their competitive advantage. They were looking for a reliable partner to take them through the incubation of their own, internal, data science team.
The challenge: This was not the first time our client tried to build a DS capability. In fact, they had already spent several years training their staff only to eventually see them leave.
What we did:
- Trained the team: We hired and trained the data science team comprising of data scientists, business analysts and data/deployment engineers
- Set up the infrastructure: We kick-started a future proof technological environment for the team to easily create ML&AI algorithms
- Proved the value: Almost as a side product, we generated 4x the value of the project just within the first three months of cooperation
Ultimate benefit: After a period of 13 months, we delivered a sturdy and fully sustainable DS team capable of servicing all their territories with a backlog of 30+ shared use cases.
Reason of success: The advantage they had, was strong commitment from the C-level managers across territories to build the internal capability.
NLP solutions
Insurance, Germany
The objective has been to build various NLP solutions to support reinsurance related use cases. This allowed the client to further differentiate itself among competitors by fully utilising data and data analytics results for action-driven insights and strategies.
Oil & gas supply chain use case
- Improving the performance of existing text analytics solutions for the detection of risks connected with supply chain companies (through the application of advanced machine learning and AI). The deployment of deep learning models replacing rule-based and regular expressions solutions.
General use case
- Improving textual data preparation pipelines (embedding and tokenization)
- Entity extraction (e.g. insured name, address, SIC code) from various text sources
- Entity linking - improve fuzzy matching logic
Tech Stack: Tensorflow, spaCy, fastText, NLTK
Capabilities: Embedding, Tokenization, Named-Entity Recognition
NLP Solutions
PwC product, global
A global PwC project building a cloud platform which delivers an end-to-end ecosystem for building an AI document annotator by any user.
A modern Azure platform
- Build on the latest cloud stack, leverage Azure Kubernetes Cluster and microservices architecture
- (re) Deployment of a new workspace in just a few minutes
- Fully scalable and performance optimal
AI approach
- PwC artifactory of ready to deploy pretrained models
- Deep learning models using cutting-edge NLP algorithms
Continuous learning
- Unique AI framework where model performance can be continuously improved
NLP services
One of the largest online retailer and technology providers & Internal
One of the largest online retailer and technology providers: Identifying fake reviews
- Purchase decisions are highly affected by product reviews. It is sometimes too easy to downplay the quality of a product using fake reviews
- Using analytical and statistical tests as well as NLP, the low-quality reviews with a high risk of being fake were identified.
Internal: Detect the changing of job skills over time
- Skills are written in the advertised job description allowing a candidate for employment to excel in a particular job.
- The solution detects the changes of the required skills over time
- Uses text mining to group the skills in each specific job and compare it with the previous month/year.
Tech Stack: Tensorflow, spaCy, fastText, NLTK
Capabilities: Embedding, Tokenization, Latent Dirichlet Allocation
GenAI solutions
PwC Project, US
User-friendly experience
Goal was to create a simple and modern experience for users to retrieve any information from various
structured (Excel files, database) and unstructured (PDFs, Word files) datasets. Build an easy to
use tool which returns valuable predefined reports.
GenAI approach
We have developed several LLM-based solutions using an OpenAI API. Our solution supports
summarization, vector database searches, structured database queries and Python coding. We have come
up with strategies to prevent the model from hallucinating answers when data is unavailable.
Powerful insights
You can use the Copilot to extract insights from financial, auditing, policy documents, or internal
knowledge base. We always provide direct links to the cited resource when useful. There are multiple
ways users can interact with the tool: from a list of well engineered static prompts generating
several reports, to a well known chatbot experience. Results can have multiple forms as well: text
summary, or structured table in form of Web Application supporting Word/Excel file download.
