Pre-Processing Big Data: Techniques to improve quality of big data analysis

For years, banks have been sitting on a goldmine of customer data. Only recently have they started exploiting that, although not surprisingly for their own benefit.
Personal data can give great insights to drive bank outcomes by decreasing credit losses and reducing fraud losses. In this webinar Ricky Knox will look at how we can use customer data to;
*Drive customer’s own advantage
*Avoid slip ups
*Dodge nasty charges
*Optimise the customer’s finances end to end.

Implementing AI applications based on machine learning is a significant topic for organizations embracing digital transformation. By 2020, 30% of CIOs will include AI in their top five investment priorities according to Gartner’s Top 10 Strategic Technology Trends for 2018: Intelligent Apps and Analytics. But to deliver on the AI promise, organizations need to generate good quality data to train the algorithms. Failure to do so will result in the following scenario: "When you automate a mess, you get an automated mess."

This webinar covers:

- An introduction to machine learning use cases and challenges provided by Kirk Borne, Principal Data Scientist at Booz Allen Hamilton and top data science and big data influencer.
- How to achieve good data quality based on harmonized semantic metadata presented by Andreas Blumauer, CEO and co-founder of Semantic Web Company and a pioneer in the application of semantic web standards for enterprise data integration.
- How to apply a combined approach when semantic knowledge models and machine learning build the basis of your cognitive computing. (See Attachment: The Knowledge Graph as the Default Data Model for Machine Learning)
- Why a combination of machine and human computation approaches is required, not only from an ethical but also from a technical perspective.

How are financial service firms around the world using machine learning systems today to identify and address risk in transactional datasets?

This webinar will look at a new approach to transaction analysis and illustrate how the combination of traditional rules-based approaches can be augmented with next-generation machine learning systems to uncover more in the data, faster and more efficiently.

We will span the various applications in banking, payments, trading, and compliance; looking at a variety of use cases from bank branch transaction analysis to trading data validation.

Anyone interested in financial technology, next-generation machine learning systems and the future of the financial services industry will find this webinar of specific interest.

Data is increasingly available in open API format, and this is particularly applicable to SMEs and businesses - from accounting APIs to Open Banking


We see a future of collaboration in business banking that will ultimately benefit SMEs/corporate clients. Businesses do not work in the same linear manner as consumers - companies have complex needs with multiple maturity periods. This requires algorithm driven technology utilising multiple sources for data innovation.

Our platform utilises private and public data including data via Burrau van Dijk for company and sector data, ownership and Peps/sanctions; Fitch Solutions Financial Implied Ratings (FIRs) for global coverage of more than 23,000 banks including 20,000 previously unrated; and Defaqto for full coverage of business banking products, as well as OECD and Customs and Excise trade data. Add to this private data sources such as banking transactions via Open Banking, accounting via open APIs and tailored products via our panel banks.

Business banking will undergo a transformation over the next decade, with change due to a combination of company / corporate demand and regulatory pressure on the banks. In the business banking space, banks and other financial institutions will revert to a focus on conventional banking, with increasing partnerships providing services to companies across the spectrum of financial products and more useful analytics tools linked to applicable products that change as the business life cycle and stage develop. Banks are considered digital identity guardians and have access to a wide range of data. When this is used with an appropriate Data Analytics platform, along with simple Product Prompts and product Execution, this will ensure that companies will benefit significantly from the changes underway in financial services.

Artificial Intelligence has a huge role to play in banking, no more so than in sustainable finance. However, data is very patchy and much source data is not available to inform Sustainable Finance. The challenge as we set off on this new journey is to make sure that the data and algorithms used are transparent and unbiased.

In this session, Richard Peers, Director of Financial Services industry at Microsoft will share how disruption and new entrants are bringing new business models and technology to play in banking as in other industries like the Auto Industry

One new area is sustainable Finance, a voluntary initiative as part of the COP agreement on climate change but the data to inform the markets is a challenge. Big Data, Machine Learning and AI can help resolve this.

But with such important issues at stake, this session will outline how AI much be designed to ethical principles

Tune in to this session for a high-level view of some key trends and technologies in banking. Get insight into sustainable finance; why AI can help and why Ethical AI is important; and the Microsoft principles for Ethical AI.

We are seeing a sea change in networking. SDN has enabled improvements in network telemetry and analytics.

In this presentation, I will talk about the current challenges that are out there and how the technology change is helping us to improve the overall network telemetry. Furthermore, I will share how deep learning techniques are being used in this field. Please join this webinar to understand how the field of network telemetry is changing.